text:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import unittest
import shutil
import sys

from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow

from transformers import AlbertConfig, is_tf_available

if is_tf_available():
    import tensorflow as tf
    from transformers.modeling_tf_albert import (TFAlbertModel, TFAlbertForMaskedLM,
                                                 TFAlbertForSequenceClassification,
                                                 TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)

@require_tf
class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):

    all_model_classes = (
        TFAlbertModel,
        TFAlbertForMaskedLM,
        TFAlbertForSequenceClassification
    ) if is_tf_available() else ()

    class TFAlbertModelTester(object):

        def __init__(self,
                     parent,
                     batch_size=13,
                     seq_length=7,
                     is_training=True,
                     use_input_mask=True,
                     use_token_type_ids=True,
                     use_labels=True,
                     vocab_size=99,
                     embedding_size=16,
                     hidden_size=32,
                     num_hidden_layers=5,
                     num_attention_heads=4,
                     intermediate_size=37,
                     hidden_act="gelu",
                     hidden_dropout_prob=0.1,
                     attention_probs_dropout_prob=0.1,
                     max_position_embeddings=512,
                     type_vocab_size=16,
                     type_sequence_label_size=2,
                     initializer_range=0.02,
                     num_labels=3,
                     num_choices=4,
                     scope=None,
                     ):
            self.parent = parent
            self.batch_size = batch_size
            self.seq_length = seq_length
            self.is_training = is_training
            self.use_input_mask = use_input_mask
            self.use_token_type_ids = use_token_type_ids
            self.use_labels = use_labels
            self.vocab_size = vocab_size
            self.embedding_size = embedding_size
            self.hidden_size = hidden_size
            self.num_hidden_layers = num_hidden_layers
            self.num_attention_heads = num_attention_heads
            self.intermediate_size = intermediate_size
            self.hidden_act = hidden_act
            self.hidden_dropout_prob = hidden_dropout_prob
            self.attention_probs_dropout_prob = attention_probs_dropout_prob
            self.max_position_embeddings = max_position_embeddings
            self.type_vocab_size = type_vocab_size
            self.type_sequence_label_size = type_sequence_label_size
            self.initializer_range = initializer_range
            self.num_labels = num_labels
            self.num_choices = num_choices
            self.scope = scope

        def prepare_config_and_inputs(self):
            input_ids = ids_tensor(
                [self.batch_size, self.seq_length], self.vocab_size)

            input_mask = None
            if self.use_input_mask:
                input_mask = ids_tensor(
                    [self.batch_size, self.seq_length], vocab_size=2)

            token_type_ids = None
            if self.use_token_type_ids:
                token_type_ids = ids_tensor(
                    [self.batch_size, self.seq_length], self.type_vocab_size)

            sequence_labels = None
            token_labels = None
            choice_labels = None
            if self.use_labels:
                sequence_labels = ids_tensor(
                    [self.batch_size], self.type_sequence_label_size)
                token_labels = ids_tensor(
                    [self.batch_size, self.seq_length], self.num_labels)
                choice_labels = ids_tensor([self.batch_size], self.num_choices)

            config = AlbertConfig(
                vocab_size_or_config_json_file=self.vocab_size,
                hidden_size=self.hidden_size,
                num_hidden_layers=self.num_hidden_layers,
                num_attention_heads=self.num_attention_heads,
                intermediate_size=self.intermediate_size,
                hidden_act=self.hidden_act,
                hidden_dropout_prob=self.hidden_dropout_prob,
                attention_probs_dropout_prob=self.attention_probs_dropout_prob,
                max_position_embeddings=self.max_position_embeddings,
                type_vocab_size=self.type_vocab_size,
                initializer_range=self.initializer_range)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

        def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
            model = TFAlbertModel(config=config)
            # inputs = {'input_ids': input_ids,
            #           'attention_mask': input_mask,
            #           'token_type_ids': token_type_ids}
            # sequence_output, pooled_output = model(**inputs)
            inputs = {'input_ids': input_ids,
                      'attention_mask': input_mask,
                      'token_type_ids': token_type_ids}
            sequence_output, pooled_output = model(inputs)

            inputs = [input_ids, input_mask]
            sequence_output, pooled_output = model(inputs)

            sequence_output, pooled_output = model(input_ids)

            result = {
                "sequence_output": sequence_output.numpy(),
                "pooled_output": pooled_output.numpy(),
            }
            self.parent.assertListEqual(
                list(result["sequence_output"].shape),
                [self.batch_size, self.seq_length, self.hidden_size])
            self.parent.assertListEqual(list(result["pooled_output"].shape), [
                                        self.batch_size, self.hidden_size])

        def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
            model = TFAlbertForMaskedLM(config=config)
            inputs = {'input_ids': input_ids,
                      'attention_mask': input_mask,
                      'token_type_ids': token_type_ids}
            prediction_scores, = model(inputs)
            result = {
                "prediction_scores": prediction_scores.numpy(),
            }
            self.parent.assertListEqual(
                list(result["prediction_scores"].shape),
                [self.batch_size, self.seq_length, self.vocab_size])

        def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
            config.num_labels = self.num_labels
            model = TFAlbertForSequenceClassification(config=config)
            inputs = {'input_ids': input_ids,
                      'attention_mask': input_mask,
                      'token_type_ids': token_type_ids}
            logits, = model(inputs)
            result = {
                "logits": logits.numpy(),
            }
            self.parent.assertListEqual(
                list(result["logits"].shape),
                [self.batch_size, self.num_labels])

        def prepare_config_and_inputs_for_common(self):
            config_and_inputs = self.prepare_config_and_inputs()
            (config, input_ids, token_type_ids, input_mask,
             sequence_labels, token_labels, choice_labels) = config_and_inputs
            inputs_dict = {'input_ids': input_ids,
                           'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict

    def setUp(self):
        self.model_tester = TFAlbertModelTest.TFAlbertModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=AlbertConfig, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_albert_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_model(*config_and_inputs)

    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_masked_lm(
            *config_and_inputs)

    def test_for_sequence_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_sequence_classification(
            *config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        cache_dir = "/tmp/transformers_test/"
        # for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
        for model_name in ['albert-base-uncased']:
            model = TFAlbertModel.from_pretrained(
                model_name, cache_dir=cache_dir)
            shutil.rmtree(cache_dir)
            self.assertIsNotNone(model)

if __name__ == "__main__":
    unittest.main()


text:
#pragma once
//====================================================================================
//Math_pch.h : precompiled header
//
//This code is part of Anubis Engine.
//
//Anubis Engine is a free game engine created as a fan project to be
//awesome platform for developing games!
//
//All sources can be found here:
//	https://github.com/Dgek/Anubis-Engine
//
//Demos based on Anubis Engine can be found here:
//	https://github.com/Dgek/Demos
//
//Copyright (c) 2013, Muralev Evgeny
//All rights reserved.
//
//Redistribution and use in source and binary forms, with
//or without modification, are permitted provided that the
//following conditions are met:
//
//Redistributions of source code must retain the above copyright notice,
//this list of conditions and the following disclaimer.
//
//Redistributions in binary form must reproduce the above copyright notice,
//this list of conditions and the following disclaimer in the documentation
//and/or other materials provided with the distribution.
//
//Neither the name of the Minotower Games nor the names of its contributors
//may be used to endorse or promote products derived from this software
//without specific prior written permission.
//
//THIS SOFTWARE IS PROVIDED BY MURALEV EVGENY "AS IS"
//AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
//THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
//ARE DISCLAIMED. IN NO EVENT SHALL MURALEV EVGENY BE LIABLE
//FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
//(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
//LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
//ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
//(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
//SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
//====================================================================================

#include <vector>

#include <Anubis.h>

#include "Vectors.h"
#include "Math.h"

#include <assert.h>

using namespace std;

namespace Anubis
{
	/*
	extern Color g_White;
	extern Color g_Black;
	extern Color g_Cyan;
	extern Color g_Red;
	extern Color g_Green;
	extern Color g_Blue;
	extern Color g_Yellow;
	extern Color g_Gray40;
	extern Color g_Gray25;
	extern Color g_Gray65;
	extern Color g_Transparent; */

	extern float3 g_up;
	extern float3 g_right;
	extern float3 g_left;
	extern float3 g_forward;
	extern float3 g_back;
	extern float3 g_camUp;	  //up direction for camera

	extern Vec g_up4;
	extern Vec g_right4;
	extern Vec g_left4;
	extern Vec g_forward4;
	extern Vec g_back4;
	extern Vec g_camUp4;    //up direction for camera

}; //Anubis

redpajama_set_name: RedPajamaGithub


text:
/*
 * expr.h - For parsing expressions.
 */
#ifndef EXPR_H_
#define EXPR_H_

#define EXPR_DIVISION_BY_ZERO -1
#define EXPR_MODULO_BY_ZERO -2

/* values below are used by SiriDB and are not necessary for this library */
#define EXPR_TOO_LONG -3
#define EXPR_TIME_OUT_OF_RANGE -4
#define EXPR_INVALID_DATE_STRING -5
#define EXPR_MEM_ALLOC_ERR -6

/*
 * Max size for expressions
 * (not the total query, just an expression in the query
 */
#define EXPR_MAX_SIZE 512

#include <inttypes.h>

/* Returns 0 when result is successful set */
int expr_parse(int64_t * result, const char * expr);

#endif  /* EXPR_H_ */