from typing import Optional, Tuple, Union

import torch
from torch.nn import CrossEntropyLoss
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.models.gptj.modeling_gptj import GPTJForCausalLM, apply_rotary_pos_emb, logger


def gaudi_gptj_attention_forward(
    self,
    hidden_states: torch.FloatTensor,
    layer_past: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.FloatTensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False,
    token_idx: Optional[torch.Tensor] = None,
) -> Union[
    Tuple[torch.Tensor, Tuple[torch.Tensor]],
    Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
    """
    Copied from GPTJAttention.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
    The only differences are:
    - add new args token_idx
    - remove is_torch_fx_proxy
    - optimize KV cache
    """
    query = self.q_proj(hidden_states)
    key = self.k_proj(hidden_states)
    value = self.v_proj(hidden_states)

    query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
    key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
    value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)

    embed_positions = self._get_embed_positions(position_ids)

    repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
    sincos = torch.gather(embed_positions, 1, repeated_position_ids)
    sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)

    if self.rotary_dim is not None:
        k_rot = key[:, :, :, : self.rotary_dim]
        k_pass = key[:, :, :, self.rotary_dim :]

        q_rot = query[:, :, :, : self.rotary_dim]
        q_pass = query[:, :, :, self.rotary_dim :]

        k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
        q_rot = apply_rotary_pos_emb(q_rot, sin, cos)

        key = torch.cat([k_rot, k_pass], dim=-1)
        query = torch.cat([q_rot, q_pass], dim=-1)
    else:
        key = apply_rotary_pos_emb(key, sin, cos)
        query = apply_rotary_pos_emb(query, sin, cos)

    key = key.permute(0, 2, 1, 3)
    query = query.permute(0, 2, 1, 3)

    if layer_past is not None:
        past_key = layer_past[0]
        past_value = layer_past[1]

        if token_idx is not None:
            # HPU bug WA
            past_key.index_add_(2, token_idx - 1, key - torch.index_select(past_key, 2, token_idx - 1))
            past_value.index_add_(2, token_idx - 1, value - torch.index_select(past_value, 2, token_idx - 1))
            key = past_key
            value = past_value
        else:
            key = torch.cat([past_key, key], dim=-2)
            value = torch.cat([past_value, value], dim=-2)

    if use_cache is True:
        present = (key, value)
    else:
        present = None

    # compute self-attention: V x Softmax(QK^T)
    attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

    attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
    attn_output = self.out_proj(attn_output)
    attn_output = self.resid_dropout(attn_output)

    outputs = (attn_output, present)
    if output_attentions:
        outputs += (attn_weights,)

    return outputs  # a, present, (attentions)


def gaudi_gptj_block_forward(
    self,
    hidden_states: Optional[torch.FloatTensor],
    layer_past: Optional[Tuple[torch.Tensor]] = None,
    attention_mask: Optional[torch.FloatTensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    head_mask: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = False,
    output_attentions: Optional[bool] = False,
    token_idx: Optional[torch.Tensor] = None,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
    """
    Copied from GPTJBlock.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
    The only differences are:
    - add new args token_idx
    """
    residual = hidden_states
    hidden_states = self.ln_1(hidden_states)
    attn_outputs = self.attn(
        hidden_states=hidden_states,
        layer_past=layer_past,
        attention_mask=attention_mask,
        position_ids=position_ids,
        head_mask=head_mask,
        use_cache=use_cache,
        output_attentions=output_attentions,
        token_idx=token_idx,
    )
    attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
    outputs = attn_outputs[1:]

    feed_forward_hidden_states = self.mlp(hidden_states)
    hidden_states = attn_output + feed_forward_hidden_states + residual

    if use_cache:
        outputs = (hidden_states,) + outputs
    else:
        outputs = (hidden_states,) + outputs[1:]

    return outputs  # hidden_states, present, (attentions)


def gaudi_gptj_model_forward(
    self,
    input_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    token_idx: Optional[torch.Tensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
    """
    Copied from GPTJModel.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
    The only differences are:
    - add new args token_idx
    """
    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
    )
    use_cache = use_cache if use_cache is not None else self.config.use_cache
    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()
        input_ids = input_ids.view(-1, input_shape[-1])
        batch_size = input_ids.shape[0]
    elif inputs_embeds is not None:
        input_shape = inputs_embeds.size()[:-1]
        batch_size = inputs_embeds.shape[0]
    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 token_type_ids is not None:
        token_type_ids = token_type_ids.view(-1, input_shape[-1])

    if position_ids is not None:
        position_ids = position_ids.view(-1, input_shape[-1]).long()

    if past_key_values is None:
        past_length = 0
        past_key_values = tuple([None] * len(self.h))
    else:
        past_length = past_key_values[0][0].size(-2)

    if position_ids is None:
        position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
        position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

    # Attention mask.
    if attention_mask is not None:
        if batch_size <= 0:
            raise ValueError("batch_size has to be defined and > 0")
        attention_mask = attention_mask.view(batch_size, -1)
        # We create a 3D attention mask from a 2D tensor mask.
        # Sizes are [batch_size, 1, 1, to_seq_length]
        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
        # this attention mask is more simple than the triangular masking of causal attention
        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
        attention_mask = attention_mask[:, None, None, :]

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and the dtype's smallest value for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
        attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

    # Prepare head mask if needed
    # 1.0 in head_mask indicate we keep the head
    # attention_probs has shape bsz x num_attention_heads x N x N
    # head_mask has shape n_layer x batch x num_attention_heads x N x N
    head_mask = self.get_head_mask(head_mask, self.config.n_layer)

    if inputs_embeds is None:
        inputs_embeds = self.wte(input_ids)

    hidden_states = inputs_embeds

    if token_type_ids is not None:
        token_type_embeds = self.wte(token_type_ids)
        hidden_states = hidden_states + token_type_embeds

    hidden_states = self.drop(hidden_states)

    output_shape = input_shape + (hidden_states.size(-1),)

    if self.gradient_checkpointing and self.training:
        if use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
            )
            use_cache = False

    presents = () if use_cache else None
    all_self_attentions = () if output_attentions else None
    all_hidden_states = () if output_hidden_states else None
    for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
        # Model parallel
        if self.model_parallel:
            torch.cuda.set_device(hidden_states.device)
            # Ensure layer_past is on same device as hidden_states (might not be correct)
            if layer_past is not None:
                layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
            # Ensure that attention_mask is always on the same device as hidden_states
            if attention_mask is not None:
                attention_mask = attention_mask.to(hidden_states.device)
            if isinstance(head_mask, torch.Tensor):
                head_mask = head_mask.to(hidden_states.device)
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if self.gradient_checkpointing and self.training:

            def create_custom_forward(module):
                def custom_forward(*inputs):
                    # None for past_key_value
                    return module(*inputs, use_cache, output_attentions)

                return custom_forward

            outputs = torch.utils.checkpoint.checkpoint(
                create_custom_forward(block),
                hidden_states,
                None,
                attention_mask,
                position_ids,
                head_mask[i],
            )
        else:
            outputs = block(
                hidden_states=hidden_states,
                layer_past=layer_past,
                attention_mask=attention_mask,
                position_ids=position_ids,
                head_mask=head_mask[i],
                use_cache=use_cache,
                output_attentions=output_attentions,
                token_idx=token_idx,
            )

        hidden_states = outputs[0]
        if use_cache is True:
            presents = presents + (outputs[1],)

        if output_attentions:
            all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

        # Model Parallel: If it's the last layer for that device, put things on the next device
        if self.model_parallel:
            for k, v in self.device_map.items():
                if i == v[-1] and "cuda:" + str(k) != self.last_device:
                    hidden_states = hidden_states.to("cuda:" + str(k + 1))

    hidden_states = self.ln_f(hidden_states)

    hidden_states = hidden_states.view(output_shape)
    # Add last hidden state
    if output_hidden_states:
        all_hidden_states = all_hidden_states + (hidden_states,)

    if not return_dict:
        return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

    return BaseModelOutputWithPast(
        last_hidden_state=hidden_states,
        past_key_values=presents,
        hidden_states=all_hidden_states,
        attentions=all_self_attentions,
    )


class GaudiGPTJForCausalLM(GPTJForCausalLM):
    """
    Inherits from GPTJForCausalLM: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
    The only differences are:
    - add new args token_idx
    - add token_idx into model_inputs
    - from step2 when enable KV cache, slice next_input_ids from input_ids base on the token_idx
    - from step2 when enable KV cache, slice next_position_ids from position_ids base on the token_idx
    - from step2 when enable KV cache, slice next_token_type_ids from token_type_ids base on the token_idx
    """

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, inputs_embeds=None, token_idx=None, **kwargs
    ):
        token_type_ids = kwargs.get("token_type_ids", None)
        # only last token for inputs_ids if past is defined in kwargs
        if past_key_values:
            if token_idx is not None:
                input_ids = torch.index_select(input_ids, 1, token_idx - 1)
            else:
                input_ids = input_ids[:, -1].unsqueeze(-1)

            if token_type_ids is not None:
                if token_idx is not None:
                    token_type_ids = torch.index_select(token_type_ids, 1, token_idx - 1)
                else:
                    token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

        attention_mask = kwargs.get("attention_mask", None)
        position_ids = kwargs.get("position_ids", None)

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                if token_idx is not None:
                    position_ids = torch.index_select(position_ids, 1, token_idx - 1)
                else:
                    position_ids = position_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "position_ids": position_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids,
                "token_idx": token_idx,
            }
        )

        return model_inputs

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        token_idx: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            token_idx=token_idx,
        )
        hidden_states = transformer_outputs[0]

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.transformer.first_device)
            hidden_states = hidden_states.to(self.lm_head.weight.device)

        # make sure sampling in fp16 works correctly and
        # compute loss in fp32 to match with mesh-tf version
        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
        lm_logits = self.lm_head(hidden_states).to(torch.float32)

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

            loss = loss.to(hidden_states.dtype)

        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=lm_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )
