text:
import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPooling,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    NextSentencePredictorOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_mobilebert import MobileBertConfig

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "google/mobilebert-uncased"
_CONFIG_FOR_DOC = "MobileBertConfig"
_TOKENIZER_FOR_DOC = "MobileBertTokenizer"

# TokenClassification docstring
_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "mrm8488/mobilebert-finetuned-ner"
_TOKEN_CLASS_EXPECTED_OUTPUT = "['I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC']"
_TOKEN_CLASS_EXPECTED_LOSS = 0.03

# QuestionAnswering docstring
_CHECKPOINT_FOR_QA = "csarron/mobilebert-uncased-squad-v2"
_QA_EXPECTED_OUTPUT = "'a nice puppet'"
_QA_EXPECTED_LOSS = 3.98
_QA_TARGET_START_INDEX = 12
_QA_TARGET_END_INDEX = 13

# SequenceClassification docstring
_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "lordtt13/emo-mobilebert"
_SEQ_CLASS_EXPECTED_OUTPUT = "'others'"
_SEQ_CLASS_EXPECTED_LOSS = "4.72"

MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = ["google/mobilebert-uncased"]

def load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path):
    """Load tf checkpoints in a pytorch model."""
    try:
        import re

        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        name = name.replace("ffn_layer", "ffn")
        name = name.replace("FakeLayerNorm", "LayerNorm")
        name = name.replace("extra_output_weights", "dense/kernel")
        name = name.replace("bert", "mobilebert")
        name = name.split("/")
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(
            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
            for n in name
        ):
            logger.info(f"Skipping {'/'.join(name)}")
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                scope_names = re.split(r"_(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "output_weights":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "squad":
                pointer = getattr(pointer, "classifier")
            else:
                try:
                    pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info(f"Skipping {'/'.join(name)}")
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if m_name[-11:] == "_embeddings":
            pointer = getattr(pointer, "weight")
        elif m_name == "kernel":
            array = np.transpose(array)
        try:
            assert (
                pointer.shape == array.shape
            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model

class NoNorm(nn.Module):
    def __init__(self, feat_size, eps=None):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros(feat_size))
        self.weight = nn.Parameter(torch.ones(feat_size))

    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
        return input_tensor * self.weight + self.bias

NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm}

class MobileBertEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super().__init__()
        self.trigram_input = config.trigram_input
        self.embedding_size = config.embedding_size
        self.hidden_size = config.hidden_size

        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

        embed_dim_multiplier = 3 if self.trigram_input else 1
        embedded_input_size = self.embedding_size * embed_dim_multiplier
        self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size)

        self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if self.trigram_input:
            # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
            # Devices (https://arxiv.org/abs/2004.02984)
            #
            # The embedding table in BERT models accounts for a substantial proportion of model size. To compress
            # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
            # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
            # dimensional output.
            inputs_embeds = torch.cat(
                [
                    nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0),
                    inputs_embeds,
                    nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0),
                ],
                dim=2,
            )
        if self.trigram_input or self.embedding_size != self.hidden_size:
            inputs_embeds = self.embedding_transformation(inputs_embeds)

        # Add positional embeddings and token type embeddings, then layer
        # normalize and perform dropout.
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings

class MobileBertSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.true_hidden_size, self.all_head_size)
        self.key = nn.Linear(config.true_hidden_size, self.all_head_size)
        self.value = nn.Linear(
            config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size
        )
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        query_tensor: torch.Tensor,
        key_tensor: torch.Tensor,
        value_tensor: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
    ) -> Tuple[torch.Tensor]:
        mixed_query_layer = self.query(query_tensor)
        mixed_key_layer = self.key(key_tensor)
        mixed_value_layer = self.value(value_tensor)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
            attention_scores = attention_scores + attention_mask
        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)
        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs

class MobileBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
        if not self.use_bottleneck:
            self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
        layer_outputs = self.dense(hidden_states)
        if not self.use_bottleneck:
            layer_outputs = self.dropout(layer_outputs)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs

class MobileBertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = MobileBertSelfAttention(config)
        self.output = MobileBertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        query_tensor: torch.Tensor,
        key_tensor: torch.Tensor,
        value_tensor: torch.Tensor,
        layer_input: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
    ) -> Tuple[torch.Tensor]:
        self_outputs = self.self(
            query_tensor,
            key_tensor,
            value_tensor,
            attention_mask,
            head_mask,
            output_attentions,
        )
        # Run a linear projection of `hidden_size` then add a residual
        # with `layer_input`.
        attention_output = self.output(self_outputs[0], layer_input)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs

class MobileBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states

class OutputBottleneck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.true_hidden_size, config.hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
        layer_outputs = self.dense(hidden_states)
        layer_outputs = self.dropout(layer_outputs)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs

class MobileBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
        if not self.use_bottleneck:
            self.dropout = nn.Dropout(config.hidden_dropout_prob)
        else:
            self.bottleneck = OutputBottleneck(config)

    def forward(
        self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor
    ) -> torch.Tensor:
        layer_output = self.dense(intermediate_states)
        if not self.use_bottleneck:
            layer_output = self.dropout(layer_output)
            layer_output = self.LayerNorm(layer_output + residual_tensor_1)
        else:
            layer_output = self.LayerNorm(layer_output + residual_tensor_1)
            layer_output = self.bottleneck(layer_output, residual_tensor_2)
        return layer_output

class BottleneckLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        layer_input = self.dense(hidden_states)
        layer_input = self.LayerNorm(layer_input)
        return layer_input

class Bottleneck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
        self.use_bottleneck_attention = config.use_bottleneck_attention
        self.input = BottleneckLayer(config)
        if self.key_query_shared_bottleneck:
            self.attention = BottleneckLayer(config)

    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
        # This method can return three different tuples of values. These different values make use of bottlenecks,
        # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
        # usage. These linear layer have weights that are learned during training.
        #
        # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
        # key, query, value, and "layer input" to be used by the attention layer.
        # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
        # in the attention self output, after the attention scores have been computed.
        #
        # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
        # four values, three of which have been passed through a bottleneck: the query and key, passed through the same
        # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
        #
        # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
        # and the residual layer will be this value passed through a bottleneck.

        bottlenecked_hidden_states = self.input(hidden_states)
        if self.use_bottleneck_attention:
            return (bottlenecked_hidden_states,) * 4
        elif self.key_query_shared_bottleneck:
            shared_attention_input = self.attention(hidden_states)
            return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
        else:
            return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)

class FFNOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
        layer_outputs = self.dense(hidden_states)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs

class FFNLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate = MobileBertIntermediate(config)
        self.output = FFNOutput(config)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        intermediate_output = self.intermediate(hidden_states)
        layer_outputs = self.output(intermediate_output, hidden_states)
        return layer_outputs

class MobileBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.num_feedforward_networks = config.num_feedforward_networks

        self.attention = MobileBertAttention(config)
        self.intermediate = MobileBertIntermediate(config)
        self.output = MobileBertOutput(config)
        if self.use_bottleneck:
            self.bottleneck = Bottleneck(config)
        if config.num_feedforward_networks > 1:
            self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)])

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
    ) -> Tuple[torch.Tensor]:
        if self.use_bottleneck:
            query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
        else:
            query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4

        self_attention_outputs = self.attention(
            query_tensor,
            key_tensor,
            value_tensor,
            layer_input,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        s = (attention_output,)
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        if self.num_feedforward_networks != 1:
            for i, ffn_module in enumerate(self.ffn):
                attention_output = ffn_module(attention_output)
                s += (attention_output,)

        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output, hidden_states)
        outputs = (
            (layer_output,)
            + outputs
            + (
                torch.tensor(1000),
                query_tensor,
                key_tensor,
                value_tensor,
                layer_input,
                attention_output,
                intermediate_output,
            )
            + s
        )
        return outputs

class MobileBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)])

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = False,
        output_hidden_states: Optional[bool] = False,
        return_dict: Optional[bool] = True,
    ) -> Union[Tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states,
                attention_mask,
                head_mask[i],
                output_attentions,
            )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )

class MobileBertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.do_activate = config.classifier_activation
        if self.do_activate:
            self.dense = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        if not self.do_activate:
            return first_token_tensor
        else:
            pooled_output = self.dense(first_token_tensor)
            pooled_output = torch.tanh(pooled_output)
            return pooled_output

class MobileBertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states

class MobileBertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = MobileBertPredictionHeadTransform(config)
        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False)
        self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.transform(hidden_states)
        hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0))
        hidden_states += self.decoder.bias
        return hidden_states

class MobileBertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MobileBertLMPredictionHead(config)

    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores

class MobileBertPreTrainingHeads(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MobileBertLMPredictionHead(config)
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> Tuple[torch.Tensor]:
        prediction_scores = self.predictions(sequence_output)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score

class MobileBertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = MobileBertConfig
    pretrained_model_archive_map = MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST
    load_tf_weights = load_tf_weights_in_mobilebert
    base_model_prefix = "mobilebert"
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, (nn.LayerNorm, NoNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

@dataclass
class MobileBertForPreTrainingOutput(ModelOutput):
    """
    Output type of [`MobileBertForPreTraining`].

    Args:
        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
            Total loss as the sum of the masked language modeling loss and the next sequence prediction
            (classification) loss.
        prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
            before SoftMax).
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
            shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: Optional[torch.FloatTensor] = None
    prediction_logits: torch.FloatTensor = None
    seq_relationship_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None

MOBILEBERT_START_DOCSTRING = r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MobileBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

MOBILEBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top.",
    MOBILEBERT_START_DOCSTRING,
)
class MobileBertModel(MobileBertPreTrainedModel):
    """
    https://arxiv.org/pdf/2004.02984.pdf
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = MobileBertEmbeddings(config)
        self.encoder = MobileBertEncoder(config)

        self.pooler = MobileBertPooler(config) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)
        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

@add_start_docstrings(
    """
    MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
    `next sentence prediction (classification)` head.
    """,
    MOBILEBERT_START_DOCSTRING,
)
class MobileBertForPreTraining(MobileBertPreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        "cls.predictions.decoder.weight",
        "cls.predictions.decoder.bias",
        "embeddings.position_ids",
    ]

    def __init__(self, config):
        super().__init__(config)
        self.mobilebert = MobileBertModel(config)
        self.cls = MobileBertPreTrainingHeads(config)

        # Initialize weights and apply final processing
        self.post_init()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddigs):
        self.cls.predictions.decoder = new_embeddigs

    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
        # resize dense output embedings at first
        self.cls.predictions.dense = self._get_resized_lm_head(
            self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
        )

        return super().resize_token_embeddings(new_num_tokens=new_num_tokens)

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=MobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        next_sentence_label: Optional[torch.LongTensor] = None,
        output_attentions: Optional[torch.FloatTensor] = None,
        output_hidden_states: Optional[torch.FloatTensor] = None,
        return_dict: Optional[torch.FloatTensor] = None,
    ) -> Union[Tuple, MobileBertForPreTrainingOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
            (see `input_ids` docstring) Indices should be in `[0, 1]`:

            - 0 indicates sequence B is a continuation of sequence A,
            - 1 indicates sequence B is a random sequence.

        Returns:

        Examples:

        ```python
        >>> from transformers import MobileBertTokenizer, MobileBertForPreTraining
        >>> import torch

        >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased")
        >>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")

        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
        >>> # Batch size 1
        >>> outputs = model(input_ids)

        >>> prediction_logits = outputs.prediction_logits
        >>> seq_relationship_logits = outputs.seq_relationship_logits
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output, pooled_output = outputs[:2]
        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)

        total_loss = None
        if labels is not None and next_sentence_label is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
            total_loss = masked_lm_loss + next_sentence_loss

        if not return_dict:
            output = (prediction_scores, seq_relationship_score) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return MobileBertForPreTrainingOutput(
            loss=total_loss,
            prediction_logits=prediction_scores,
            seq_relationship_logits=seq_relationship_score,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING)
class MobileBertForMaskedLM(MobileBertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [
        "cls.predictions.decoder.weight",
        "cls.predictions.decoder.bias",
        "embeddings.position_ids",
    ]

    def __init__(self, config):
        super().__init__(config)
        self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
        self.cls = MobileBertOnlyMLMHead(config)
        self.config = config

        # Initialize weights and apply final processing
        self.post_init()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddigs):
        self.cls.predictions.decoder = new_embeddigs

    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
        # resize dense output embedings at first
        self.cls.predictions.dense = self._get_resized_lm_head(
            self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
        )
        return super().resize_token_embeddings(new_num_tokens=new_num_tokens)

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output="'paris'",
        expected_loss=0.57,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, MaskedLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()  # -100 index = padding token
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

class MobileBertOnlyNSPHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
        seq_relationship_score = self.seq_relationship(pooled_output)
        return seq_relationship_score

@add_start_docstrings(
    """MobileBert Model with a `next sentence prediction (classification)` head on top.""",
    MOBILEBERT_START_DOCSTRING,
)
class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.mobilebert = MobileBertModel(config)
        self.cls = MobileBertOnlyNSPHead(config)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple, NextSentencePredictorOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
            (see `input_ids` docstring) Indices should be in `[0, 1]`.

            - 0 indicates sequence B is a continuation of sequence A,
            - 1 indicates sequence B is a random sequence.

        Returns:

        Examples:

        ```python
        >>> from transformers import MobileBertTokenizer, MobileBertForNextSentencePrediction
        >>> import torch

        >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased")
        >>> model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")

        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
        >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
        >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")

        >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
        >>> loss = outputs.loss
        >>> logits = outputs.logits
        ```"""

        if "next_sentence_label" in kwargs:
            warnings.warn(
                "The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
                " `labels` instead.",
                FutureWarning,
            )
            labels = kwargs.pop("next_sentence_label")

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]
        seq_relationship_score = self.cls(pooled_output)

        next_sentence_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1))

        if not return_dict:
            output = (seq_relationship_score,) + outputs[2:]
            return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output

        return NextSentencePredictorOutput(
            loss=next_sentence_loss,
            logits=seq_relationship_score,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """
    MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    MOBILEBERT_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing
class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.config = config

        self.mobilebert = MobileBertModel(config)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
        expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """
    MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
    linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    MOBILEBERT_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering with Bert->MobileBert all-casing
class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_QA,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
        qa_target_start_index=_QA_TARGET_START_INDEX,
        qa_target_end_index=_QA_TARGET_END_INDEX,
        expected_output=_QA_EXPECTED_OUTPUT,
        expected_loss=_QA_EXPECTED_LOSS,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """
    MobileBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
    a softmax) e.g. for RocStories/SWAG tasks.
    """,
    MOBILEBERT_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice with Bert->MobileBert all-casing
class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.mobilebert = MobileBertModel(config)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, 1)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(
        MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

@add_start_docstrings(
    """
    MobileBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
    for Named-Entity-Recognition (NER) tasks.
    """,
    MOBILEBERT_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification with Bert->MobileBert all-casing
class MobileBertForTokenClassification(MobileBertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
        expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
    )
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilebert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


text:
ACCEPTED

#### According to
Index Fungorum

#### Published in
null

#### Original name
Pleospora aphyllanthis Henn.

### Remarks
null

redpajama_set_name: RedPajamaGithub


text:
package com.github.dockerjava.api.command;

import java.util.Map;

import com.github.dockerjava.api.model.*;
import org.apache.commons.lang.builder.ToStringBuilder;

import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;

/**
 *
 * @author Konstantin Pelykh (kpelykh@gmail.com)
 *
 */
@JsonIgnoreProperties(ignoreUnknown = true)
public class InspectContainerResponse {

	@JsonProperty("Args")
	private String[] args;

	@JsonProperty("Config")
	private ContainerConfig config;

	@JsonProperty("Created")
	private String created;

	@JsonProperty("Driver")
	private String driver;

	@JsonProperty("ExecDriver")
	private String execDriver;

	@JsonProperty("HostConfig")
	private HostConfig hostConfig;

	@JsonProperty("HostnamePath")
	private String hostnamePath;

	@JsonProperty("HostsPath")
	private String hostsPath;

	@JsonProperty("Id")
    private String id;

	@JsonProperty("Image")
	private String imageId;

	@JsonProperty("MountLabel")
	private String mountLabel;

	@JsonProperty("Name")
	private String name;

	@JsonProperty("NetworkSettings")
	private NetworkSettings networkSettings;

	@JsonProperty("Path")
    private String path;

	@JsonProperty("ProcessLabel")
    private String processLabel;

	@JsonProperty("ResolvConfPath")
	private String resolvConfPath;

	@JsonProperty("State")
    private ContainerState state;

    @JsonProperty("Volumes")
    private VolumeBinds volumes;

    @JsonProperty("VolumesRW")
    private Volumes volumesRW;

    public String getId() {
        return id;
    }

    public String getCreated() {
        return created;
    }

    public String getPath() {
        return path;
    }

    public String getProcessLabel() {
		return processLabel;
	}

    public String[] getArgs() {
        return args;
    }

    public ContainerConfig getConfig() {
        return config;
    }

    public ContainerState getState() {
        return state;
    }

    public String getImageId() {
        return imageId;
    }

    public NetworkSettings getNetworkSettings() {
        return networkSettings;
    }

    public String getResolvConfPath() {
        return resolvConfPath;
    }

    @JsonIgnore
    public VolumeBind[] getVolumes() {
        return volumes.getBinds();
    }

    @JsonIgnore
    public Volume[] getVolumesRW() {
        return volumesRW.getVolumes();
    }

    public String getHostnamePath() {
        return hostnamePath;
    }

    public String getHostsPath() {
        return hostsPath;
    }

    public String getName() {
        return name;
    }

    public String getDriver() {
        return driver;
    }

    public HostConfig getHostConfig() {
        return hostConfig;
    }

    public String getExecDriver() {
		return execDriver;
	}

    public String getMountLabel() {
		return mountLabel;
	}

    @JsonIgnoreProperties(ignoreUnknown = true)
    public class NetworkSettings {

        @JsonProperty("IPAddress") private String ipAddress;
        @JsonProperty("IPPrefixLen") private int ipPrefixLen;
        @JsonProperty("Gateway") private String gateway;
        @JsonProperty("Bridge") private String bridge;
        @JsonProperty("PortMapping") private Map<String,Map<String, String>> portMapping;
        @JsonProperty("Ports") private Ports ports;

        public String getIpAddress() {
			return ipAddress;
		}

		public int getIpPrefixLen() {
			return ipPrefixLen;
		}

		public String getGateway() {
			return gateway;
		}

		public String getBridge() {
			return bridge;
		}

		public Map<String, Map<String, String>> getPortMapping() {
			return portMapping;
		}

		public Ports getPorts() {
			return ports;
		}

        @Override
        public String toString() {
            return ToStringBuilder.reflectionToString(this);
        }
    }

    @JsonIgnoreProperties(ignoreUnknown = true)
    public class ContainerState {

        @JsonProperty("Running") private boolean running;
        @JsonProperty("Paused") private boolean paused;
        @JsonProperty("Pid") private int pid;
        @JsonProperty("ExitCode") private int exitCode;
        @JsonProperty("StartedAt") private String startedAt;
        @JsonProperty("FinishedAt") private String finishedAt;

        public boolean isRunning() {
			return running;
		}

		public boolean isPaused() {
			return paused;
		}

		public int getPid() {
			return pid;
		}

		public int getExitCode() {
			return exitCode;
		}

		public String getStartedAt() {
			return startedAt;
		}

		public String getFinishedAt() {
			return finishedAt;
		}

        @Override
        public String toString() {
            return ToStringBuilder.reflectionToString(this);
        }
    }

    @JsonIgnoreProperties(ignoreUnknown = true)
    public class HostConfig {

        @JsonProperty("Binds")
        private String[] binds;

        @JsonProperty("LxcConf")
        private LxcConf[] lxcConf;

        @JsonProperty("PortBindings")
        private Ports portBindings;

        @JsonProperty("PublishAllPorts")
        private boolean publishAllPorts;

        @JsonProperty("Privileged")
        private boolean privileged;

        @JsonProperty("Dns")
        private String[] dns;

        @JsonProperty("DnsSearch")
        private String[] dnsSearch;

        @JsonProperty("VolumesFrom")
        private String[] volumesFrom;

        @JsonProperty("ContainerIDFile")
        private String containerIDFile;

        // TODO: use Links class here?
        @JsonProperty("Links")
        private String[] links;

        @JsonProperty("NetworkMode")
        private String networkMode;

        @JsonProperty("Devices")
        private Device[] devices;

        @JsonProperty("RestartPolicy")
        private RestartPolicy restartPolicy;

        @JsonProperty("CapAdd")
        private String[] capAdd;

        @JsonProperty("CapDrop")
        private String[] capDrop;

        public String[] getBinds() {
			return binds;
		}

        public LxcConf[] getLxcConf() {
			return lxcConf;
		}

		public Ports getPortBindings() {
			return portBindings;
		}

		public boolean isPublishAllPorts() {
			return publishAllPorts;
		}

		public boolean isPrivileged() {
			return privileged;
		}

		public String[] getDns() {
			return dns;
		}

		public String[] getVolumesFrom() {
			return volumesFrom;
		}

		public String getContainerIDFile() {
			return containerIDFile;
		}

		public String[] getDnsSearch() {
			return dnsSearch;
		}

		public String[] getLinks() {
			return links;
		}

		public String getNetworkMode() {
			return networkMode;
		}

		public Device[] getDevices() {
			return devices;
		}

		public RestartPolicy getRestartPolicy() {
			return restartPolicy;
		}

		public String[] getCapAdd() {
			return capAdd;
		}

		public String[] getCapDrop() {
			return capDrop;
		}

        @Override
        public String toString() {
            return ToStringBuilder.reflectionToString(this);
        }

    }

}


text:
copyright:
  years: 2014, 2017
lastupdated: "2017-02-06"

---

{:new_window: target="_blank"}
{:shortdesc: .shortdesc}
{:screen: .screen}
{:codeblock: .codeblock}
{:pre: .pre}

# Instrumentando o MQA com um app iOS (descontinuado)
{: #instrumentios}

Para usar o {{site.data.keyword.mqafull}} com seu app iOS, deve-se fazer download do SDK e instrumentá-lo fazendo algumas mudanças usando o ambiente de desenvolvimento do Xcode.
{: shortdesc}

Para poder instrumentar um app com o {{site.data.keyword.mqa}}, deve-se ter uma conta do {{site.data.keyword.Bluemix}} e a versão corretamente instalada do ambiente de desenvolvimento para as plataformas do app que você está instrumentando.

Para instrumentar o {{site.data.keyword.mqa}} para funcionar com seu app Objective-C ou Swift, conclua as tarefas a seguir:

1. Inclua as permissões requeridas e o SDK transferido por download no seu projeto do iOS.

	1. Crie ou abra seu projeto no Xcode.

	2. Inclua as Bibliotecas de dependências a seguir selecionando `+` na seção *Estruturas e bibliotecas vinculadas* da guia *Geral* do seu projeto Xcode:

		* AssetsLibrary.framework
		* AudioToolbox.framework
		* AVFoundation.framework
		* CoreData.framework
		* CoreLocation.framework
		* CoreMedia.framework
		* CoreMotion.framework
		* CoreTelephony.framework
		* CoreText.framework
		* CoreVideo.framework
		* MediaPlayer.framework
		* QuartzCore.framework
		* Security.framework
		* SystemConfiguration.framework

	3. Arraste o SDK do {{site.data.keyword.mqa}} para a pasta `Q4M.framework` do iOS no grupo **Estruturas** da árvore do navegador. Na janela Escolher opções, selecione *Copiar itens se necessário*, **Criar grupos** e marque a caixa para o nome do seu projeto como destino.

	4. Na seção Link da guia *Configurações de construção*, configure *Outras sinalizações do vinculador* como **-ObjC** para Depurar e Liberar. Importante: Certifique-se de que a guia *Todos* seja selecionada para que Outras sinalizações do vinculador sejam incluídas na lista.

	5. *Somente para Objective-C:* Inclua a instrução de importação a seguir nos arquivos `AppDelegate.m` e `ViewController.m`:

		```
		#import <Q4M/MQALogger.h>
		```
		{: codeblock}

	6. *Somente para Swift:* Inclua um cabeçalho de ponte Objective-C para o {{site.data.keyword.mqa}} SDK for iOS concluindo as etapas a seguir:

		1. Clique com o botão direito do mouse no nó raiz do projeto e selecione **Novo arquivo**.

		2. Na janela Escolher modelo no iOS, clique no modelo Arquivo de cabeçalho na seção Origem.

		3. Nomeie o arquivo `MyProject-Bridging-Header.h`, em que *MyProject* é o nome do seu projeto Swift, e selecione o nome do seu projeto como destino. Por exemplo, nomeie o arquivo `MyProject-Bridging-Header.h`.

		4. Clique em **Criar** para salvar o arquivo na raiz de seu projeto.

		5. No novo arquivo de cabeçalho de ponte, inclua a instrução de importação a seguir para o arquivo de cabeçalho de ponte:

			```
			#import <Q4M/MQALogger.h>
			```
			{: codeblock}

		6. Com seu projeto selecionado no Xcode Project Navigator, clique em
**Configurações de construção** e procure `Objective-C Bridging Header`.

		7. Em **Compilador Swift - Geral**, configure o valor de **Cabeçalho de ponte Objective-C** como o caminho para o cabeçalho de ponte. O caminho é relativo à raiz de seu projeto. Por exemplo, se o projeto MQASampleApp estiver em `/user/example/MQASampleApp`, o caminho será `/user/example/MQASampleApp/MQASampleApp-Bridging-Header.h`. Se você salvou seu arquivo na raiz do projeto, será possível usar a variável de ambiente para configurar o caminho inserindo `${SRCROOT}/${PROJECT}-Bridging-Header.h`.

		8. Salve e construa o app para verificar se o caminho está correto.

	7. No arquivo `info.plist`, verifique as configurações a seguir nas informações do seu projeto:

		* Versão do pacote configurável - o {{site.data.keyword.mqa}} usa o valor desse campo para determinar quando enviar uma nova notificação de construção aos testadores. Quando você fizer upload de uma nova construção para o {{site.data.keyword.mqa}}, assegure-se de incrementar esse número. A sequência no campo Versão do pacote configurável é restrita a números e ao caractere de ponto (.). Como essa restrição é um requisito do Xcode, a maioria dos aplicativos segue essa convenção e não requer mudanças.
		* Sequência de versões de pacote configurável - esse campo contém uma representação de sequência do número da versão. A sequência não tem as mesmas restrições que o campo Versão do pacote configurável.

2. Configure uma nova sessão para ser iniciada com cada login.

    1. Localize o método `didFinishLaunchingWithOptions` no arquivo `AppDelegate`:

        * Objective-C: arquivo `AppDelegate.m`
        * Swift: arquivo `AppDelegate.swift`

	2. Inicie a sessão do {{site.data.keyword.mqa}}.

		* Objective-C

			1. Inicie a nova sessão em um método dentro do método `applicationDelegate`, como `didFinishLaunchingWithOptions`. É possível localizar seu delegado abrindo a pasta com o mesmo nome de seu projeto no Project Navigator. Verifique o arquivo `projectDelegate.m`.

			2. Inclua o código a seguir dentro do método `didFinishLaunchingWithOptions` antes da linha com `return YES;`:

				```
				// Starts a quality assurance session using a
				    placeholder key and QA mode
				[MQALogger startNewSessionWithApplicationKey:@"Your_Application_Key"];
				```
				{: codeblock}

			3. Substitua a variável *Your_Application_Key* pela chave do aplicativo do {{site.data.keyword.mqa}}.
               Dica: é possível localizar a chave do app na área de janela da web do {{site.data.keyword.mqa}} em Configurações de app.

		* Swift

			1. Localize o método `didFinishLaunchingWithOptions` no arquivo `AppDelegate.swift`.

			2. Inclua o código a seguir dentro do método antes da linha que lê `return true`, em que *Your_Application_Key* é uma chave válida para seu app:

				```
				// Set the application key
				MQALogger.startNewSession(withApplicationKey: "Your_Application_Key")
				```
				{: codeblock}

	3. Defina se deseja usar o modo de pré-produção ou de produção. Para obter informações sobre as diferenças entre os modos de pré-produção e produção, consulte [Diferenças entre os modos de pré-produção e produção ![Ícone de link externo](../../icons/launch-glyph.svg "Ícone de link externo")](http://www.ibm.com/support/knowledgecenter/SSJML5_6.0.0/com.ibm.mqa.uau.saas.doc/topics/cPreprodvsProd.html){: new_window} no IBM Knowledge Center.

		* Objective-C

			1. Localize o método `didFinishLaunchingWithOptions`.

			2. Inclua uma das linhas de código a seguir dentro do método didFinishLaunchingWithOptions antes da linha com `return YES;`:

			Para usar o modo de pré-produção, inclua esta linha de código:

				```
				[[MQALogger settings] setMode:MQAModeQA];
				```
				{: codeblock}

			Para usar o modo de produção, inclua esta linha de código:

				```
				[[MQALogger settings] setMode:MQAModeMarket];
				```
				{: codeblock}

				**Exemplo completo de Objective-C**

					- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions
						{
					   // Substitua o ponto de customização após a ativação do aplicativo.
					   if ([[UIDevice currentDevice] userInterfaceIdiom] == UIUserInterfaceIdiomPad) {
					      UISplitViewController *splitViewController = (UISplitViewController*)self.window.rootViewController;
					      UINavigationController *navigationController = [splitViewController.viewControllers lastObject];
					      splitViewController.delegate = (id)navigationController.topViewController;
						}
					     #ifdef Debug
					   [[MQALogger settings] setMode:MQAModeQA];
					   #else
					   [[MQALogger settings] setMode:MQAModeMarket];
					   #endif
					      // Starts a quality assurance session using a placeholder key
					      [MQALogger startNewSessionWithApplicationKey:@"Your-Application-Key"];
					      // Enables the quality assurance application crash reporting
					      NSSetUncaughtExceptionHandler(&MQAUncaughtExceptionHandler);
					      return YES; }

				{: codeblock}

        * Swift

			1. No arquivo AppDelegate.swift, procure o método `didFinishLaunchingWithOptions`.

			2. Inclua as linhas a seguir no método `didFinishLaunchingWithOptions` antes da linha que lê `return true`:

				```
				//Set the SDK mode Market vs QA for Production
				   and Pre-Production
				#if Debug
				  MQALogger.settings().mode = MQAMode.QA
				#elseif Release
				  MQALogger.settings().mode = MQAMode.Market
				#endif
				```
				{: codeblock}

			Este é um exemplo do código:

				```
				func application(application: UIApplication, didFinishLaunchingWithOptions launchOptions: [NSObject: AnyObject]?) -> Bool {

				//Set the SDK mode Market vs QA for Production
				   and Pre-Production
				#if Debug
					MQALogger.settings().mode = MQAMode.QA
				#elseif Release
					MQALogger.settings().mode = MQAMode.Market
				#endif

				// Set the application key
				MQALogger.startNewSession(withApplicationKey: "Your_Application_Key")

				// Substitua o ponto de customização após a ativação do aplicativo.
				return true
				```
				{: codeblock}

				**Exemplo completo de Swift**

				func application(application: UIApplication, didFinishLaunchingWithOptions launchOptions:[NSObject: AnyObject]?) -> Bool {
					// Substitua o ponto de customização após a ativação do aplicativo.
					//Set the Shake Device option to true or false. If this is not explicitly set, the Shake Device is enabled by default.
					MQALogger.settings().reportOnShakeEnabled = true
					//Set the SDK mode Market vs QA for Production and Pre-Production
					#if Debug
					MQALogger.settings().mode = MQAMode.QA
					#elseif Release
					MQALogger.settings().mode = MQAMode.Market
					#endif

				//Start the MQA session with the specified app key
					   MQALogger.startNewSession(withApplicationKey: "Your_Application_Key")
				// Enables MQA crash reporting to catch uncaught exceptions
					   NSSetUncaughtExceptionHandler(exceptionHandlerPointer);

					   return true
					}

				{: codeblock}

3. Execute seu app para assegurar que {{site.data.keyword.mqa}} seja iniciado quando o app for iniciado.

4. Continue com as etapas na página [Introdução ao {{site.data.keyword.mqa}}](index.html).

# Links relacionados
{: #rellinks notoc}

## Links relacionados
{: #general notoc}
* [Introdução ao {{site.data.keyword.mqa}}](index.html){:new_window}


text:
So You Want to Deploy Hummingbird
=================================

## Concepts

### The Ring

The ring is a datastructure that keeps track of the which devices are in the cluster, and is used to determine which of those devices an object should be stored on.

#### Replicas

The replica count in the ring defines the number of copies of an object and is typlicaly set to 3.

#### Partitions

Partitions are abstract buckets that objects are assigned to.  The ring evenly distributes these partitions to each storage device in the cluster.  The number of partitions is typically represented by a power of 2 and is typically set to 22 which would mean that there are 2^22 (4,194,304) * (# of replicas) partitions in the cluster.

#### Minimum Partition Hours

When changes to the ring are made, it will only move 1 of the replicas at a time.  This helps ensure that data remains available in the cluster.  Once a partition has been moved, it can not move again until Minimum Partition Hours has elapsed.  This value should be set at a minimum of the maximum time a successful replication pass takes in the cluster.  It is typically set to something higher like 168 hours (1 week) to be safe.

### Zones

Hummingbird uses zones as an abstract concept that allows the deployer to define failure domains.  When objects are stored in Hummingbird it will store each replica in a different zone.  In a small deployment, a zone could be each server, and in a larger deployment, it could be each cabinet.

## Hardware Considerations

The recommendations made below are made based on average use cases, and should be considered as a good starting point, but should be tailored for each cluster's use case.

### Storage Nodes

All hummingbird services run on each storage node.  SATA drives are recommended to store object data and SSD drives are recommended for account/container data.  Typically 5% of the total storage should be SSD drives.  It is not recommended to use parity based RAID (like RAID 5) with the storage drives.  A RAID or HBA card with battery backed caching will improve performance though.  Modern Intel Xeon CPUs are recommended and typical servers will have 2 multi core processors.  Each server should have at least two 10Gb Ethernet ports, and 64GB is recommended as a minimum.

### Load Balancing

Each storage node is capable of serving API requests, and it is recommended to have a load balancer in front of the cluster.  HA Proxy is a capable open source load ballancer that works well with hummingbird.

### Networking

When designing your network, here are some general considerations:

  *  Each storage node needs full connectivity to each other storage node.
  *  Each incoming object sent will fan out to (# of replicas) storage servers, so intra cluster bandwith will be (# of replicas)x the overall throughput for incoming requests.
  *  All other requests only require a single storage node.
  *  For larger/busier clusters, consideration will need to be made for bandwidth between zones.

## Operating System Considerations

All testing to date has been on Ubuntu Server 16.04. Newer versions of Ubuntu should work as well, but no specific testing has been done. Other Linux distributions should also work, but tweaks to init scripts / systemd service files may be needed.


text:
<?xml version="1.0" encoding="utf-8"?>
<selector xmlns:android="http://schemas.android.com/apk/res/android">

    <item android:drawable="@drawable/ic_tab_relevance_selected" android:state_pressed="false" android:state_selected="true"/>
    <item android:drawable="@drawable/ic_tab_relevance_unselected"/>

</selector>


text:
#ifndef INCLUDE_git_filter_h__
#define INCLUDE_git_filter_h__

#include BOSS_LIBGIT2_U_common_h //original-code:"common.h"
#include "types.h"
#include "oid.h"
#include BOSS_LIBGIT2_U_buffer_h //original-code:"buffer.h"

/**
 * @file git2/filter.h
 * @brief Git filter APIs
 *
 * @ingroup Git
 * @{
 */
GIT_BEGIN_DECL

/**
 * Filters are applied in one of two directions: smudging - which is
 * exporting a file from the Git object database to the working directory,
 * and cleaning - which is importing a file from the working directory to
 * the Git object database.  These values control which direction of
 * change is being applied.
 */
typedef enum {
	GIT_FILTER_TO_WORKTREE = 0,
	GIT_FILTER_SMUDGE = GIT_FILTER_TO_WORKTREE,
	GIT_FILTER_TO_ODB = 1,
	GIT_FILTER_CLEAN = GIT_FILTER_TO_ODB,
} git_filter_mode_t;

/**
 * Filter option flags.
 */
typedef enum {
	GIT_FILTER_OPT_DEFAULT = 0u,
	GIT_FILTER_OPT_ALLOW_UNSAFE = (1u << 0),
} git_filter_opt_t;

/**
 * A filter that can transform file data
 *
 * This represents a filter that can be used to transform or even replace
 * file data.  Libgit2 includes one built in filter and it is possible to
 * write your own (see git2/sys/filter.h for information on that).
 *
 * The two builtin filters are:
 *
 * * "crlf" which uses the complex rules with the "text", "eol", and
 *   "crlf" file attributes to decide how to convert between LF and CRLF
 *   line endings
 * * "ident" which replaces "$Id$" in a blob with "$Id: <blob OID>$" upon
 *   checkout and replaced "$Id: <anything>$" with "$Id$" on checkin.
 */
typedef struct git_filter git_filter;

/**
 * List of filters to be applied
 *
 * This represents a list of filters to be applied to a file / blob.  You
 * can build the list with one call, apply it with another, and dispose it
 * with a third.  In typical usage, there are not many occasions where a
 * git_filter_list is needed directly since the library will generally
 * handle conversions for you, but it can be convenient to be able to
 * build and apply the list sometimes.
 */
typedef struct git_filter_list git_filter_list;

/**
 * Load the filter list for a given path.
 *
 * This will return 0 (success) but set the output git_filter_list to NULL
 * if no filters are requested for the given file.
 *
 * @param filters Output newly created git_filter_list (or NULL)
 * @param repo Repository object that contains `path`
 * @param blob The blob to which the filter will be applied (if known)
 * @param path Relative path of the file to be filtered
 * @param mode Filtering direction (WT->ODB or ODB->WT)
 * @param options Combination of `git_filter_opt_t` flags
 * @return 0 on success (which could still return NULL if no filters are
 *         needed for the requested file), <0 on error
 */
GIT_EXTERN(int) git_filter_list_load(
	git_filter_list **filters,
	git_repository *repo,
	git_blob *blob, /* can be NULL */
	const char *path,
	git_filter_mode_t mode,
	uint32_t options);

/**
 * Apply filter list to a data buffer.
 *
 * See `git2/buffer.h` for background on `git_buf` objects.
 *
 * If the `in` buffer holds data allocated by libgit2 (i.e. `in->asize` is
 * not zero), then it will be overwritten when applying the filters.  If
 * not, then it will be left untouched.
 *
 * If there are no filters to apply (or `filters` is NULL), then the `out`
 * buffer will reference the `in` buffer data (with `asize` set to zero)
 * instead of allocating data.  This keeps allocations to a minimum, but
 * it means you have to be careful about freeing the `in` data since `out`
 * may be pointing to it!
 *
 * @param out Buffer to store the result of the filtering
 * @param filters A loaded git_filter_list (or NULL)
 * @param in Buffer containing the data to filter
 * @return 0 on success, an error code otherwise
 */
GIT_EXTERN(int) git_filter_list_apply_to_data(
	git_buf *out,
	git_filter_list *filters,
	git_buf *in);

/**
 * Apply filter list to the contents of a file on disk
 */
GIT_EXTERN(int) git_filter_list_apply_to_file(
	git_buf *out,
	git_filter_list *filters,
	git_repository *repo,
	const char *path);

/**
 * Apply filter list to the contents of a blob
 */
GIT_EXTERN(int) git_filter_list_apply_to_blob(
	git_buf *out,
	git_filter_list *filters,
	git_blob *blob);

/**
 * Free a git_filter_list
 *
 * @param filters A git_filter_list created by `git_filter_list_load`
 */
GIT_EXTERN(void) git_filter_list_free(git_filter_list *filters);

GIT_END_DECL

/** @} */

#endif


text:
var tesla,
    express = require('express'), // GET EXPRESS
    app = module.exports = express(), // DEFINE THE APP
    request = require('superagent'),
    fs = require('fs'),
    browser = require('open'),
    growl = require('growl'),
    cheerio = require('cheerio'),
    server = require('http').createServer(app); // CREATE THE SERVER
    require('colors'); // PRETTY CONSOLE LOGGING
    require('fs'); // FILE SYSTEM
    process.env.NODE_ENV = process.env.NODE_ENV || 'development'; // SET DEFAULT ENVIRONMENT

// LOAD CONFIG & TESLA CLASS
require('./config/_settings')(app); // MAIN APP SETTINGS
tesla = require('./lib/tesla')(app);
tesla.inform(app, 'start'); // WELCOME MESSAGE

// REQUIRED SETTINGS & CONFIG FILES
require('./config/environment/' + process.env.NODE_ENV)(app); // ENVIRONMENT SPECIFIC SETTINGS

growl('Checking Nexus 6 stock...');

function checkGoogle( name, url ) {
  request.get(url, function(error, res){

      // console.log(res.text);
      var html = res.text,
      $ = cheerio.load(html);

      if ($('.play-button.devices').text().indexOf('cart') > -1) {
          growl(name.rainbow + ' is available @ Google!');
          console.log(name.rainbow + ' is available @ Google!'.rainbow);
          fs.appendFile('availability.log', name + ' was available from Google Play on ' + new Date().toString() + '\n');
          browser(url);
      } else {

        console.log(name.red + ' is sold out @ Google : '.red + new Date().toString().red);
        checkGoogle(name, url);

      }

  });
}

function checkMotorola( url ) {
  request.get(url, function(error, res){

      // console.log(res.text);
      var html = res.text,
      $ = cheerio.load(html);

      var items = $('.play-button.devices');

      if ($('.button.blue-1.medium.disabled').text().indexOf('Out of Stock') > -1) {
        console.log('Nexus 6 is sold out @ Motorola : '.yellow + new Date().toString().yellow);
        checkMotorola(url);
      } else {
        growl('Nexus 6 is available @ Motorola!');
        console.log('Nexus 6 is available @ Motorola!'.rainbow);
        fs.appendFile('availability.log', 'Nexus 6 was available from Motorola.com on ' + new Date().toString() + '\n');
        browser(url);
      }

  });
}

// CHECK ALL THE THINGZ
checkGoogle('32GB White Nexus 6', 'https://play.google.com/store/devices/details/Nexus_6_32GB_Cloud_White?id=nexus_6_white_32gb');
checkGoogle('64GB White Nexus 6', 'https://play.google.com/store/devices/details/Nexus_6_64GB_Cloud_White?id=nexus_6_white_64gb');
checkGoogle('32GB Blue Nexus 6', 'https://play.google.com/store/devices/details/Nexus_6_32GB_Midnight_Blue?id=nexus_6_blue_32gb');
checkGoogle('64GB Blue Nexus 6', 'https://play.google.com/store/devices/details/Nexus_6_64GB_Midnight_Blue?id=nexus_6_blue_64gb');
checkMotorola('http://www.motorola.com/us/consumers/nexus-6-header/Nexus-6/nexus-6-motorola-us.html');

// START THE APP BY LISTEN ON <PORT>
server.listen( process.env.PORT || app.config.port, function( err ) {

  if ( !err ) { // IF THERE'S NO ERRORS
    tesla.inform(app, 'done');
  } else { // OH NOES! SOMETHING WENT WRONG!
    tesla.inform(app, 'error', err);
  }

});

// HANDLE UNCAUGHT ERRORS
process.on('uncaughtException', function(err) {

  if(err.errno === 'EADDRINUSE') {
    tesla.inform(app, 'eaddr');
  } else {
    tesla.inform(app, 'error', err);
  }

  process.exit(1);

});

// EXPOSE APP
exports = module.exports = app;


text:
gulp-module-complexity
======================

Complexity Gulp Module Integration