
"""simple docstring"""







import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCamelCase__   					=		'\\n    Text data.\n    Second line of data.'

lowerCamelCase__   					=		'file'
@pytest.fixture(scope='session'		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						List[str]									=      tmp_path_factory.mktemp('data'		) / (FILE_PATH + '''.zstd''')
	__lowerCAmelCase			:						Union[str, Any]									=      bytes(lowercase__       ,  'utf-8'		)
	with zstd.open(lowercase__       ,  'wb'		) as f:
		f.write(lowercase__		)
	return path
@pytest.fixture
def     __lowerCAmelCase				(_UpperCamelCase		):
	with open(os.path.join(tmpfs.local_root_dir       ,  lowercase__		)       ,  'w'		) as f:
		f.write(lowercase__		)
	return FILE_PATH
@pytest.mark.parametrize('compression_format'       ,  ['gzip', 'xz', 'zstd']		)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						Optional[int]									=      {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
	__lowerCAmelCase			:						str									=      input_paths[compression_format]
	__lowerCAmelCase			:						Tuple									=      tmp_path / '''cache'''
	__lowerCAmelCase			:						Any									=      DownloadConfig(cache_dir=lowercase__       ,  extract_compressed_file=lowercase__		)
	__lowerCAmelCase			:						Union[str, Any]									=      cached_path(lowercase__       ,  download_config=lowercase__		)
	with open(lowercase__		) as f:
		__lowerCAmelCase			:						Any									=      f.read()
	with open(lowercase__		) as f:
		__lowerCAmelCase			:						int									=      f.read()
	assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted'       ,  [True, False]		)
@pytest.mark.parametrize('default_cache_dir'       ,  [True, False]		)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						List[str]									=      '''custom_cache'''
	__lowerCAmelCase			:						Optional[Any]									=      '''custom_extracted_dir'''
	__lowerCAmelCase			:						int									=      tmp_path / '''custom_extracted_path'''
	if default_extracted:
		__lowerCAmelCase			:						List[Any]									=      ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
	else:
		monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'       ,  lowercase__		)
		monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'       ,  str(lowercase__		)		)
		__lowerCAmelCase			:						List[Any]									=      custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

	__lowerCAmelCase			:						int									=      xz_file
	__lowerCAmelCase			:						int									=      (
	    DownloadConfig(extract_compressed_file=lowercase__		)
	    if default_cache_dir
	    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir       ,  extract_compressed_file=lowercase__		)
	)
	__lowerCAmelCase			:						Optional[int]									=      cached_path(lowercase__       ,  download_config=lowercase__		)
	assert Path(lowercase__		).parent.parts[-2:] == expected
def     __lowerCAmelCase				(_UpperCamelCase		):
	# absolute path
	__lowerCAmelCase			:						Dict									=      str(Path(lowercase__		).resolve()		)
	assert cached_path(lowercase__		) == text_file
	# relative path
	__lowerCAmelCase			:						str									=      str(Path(lowercase__		).resolve().relative_to(Path(os.getcwd()		)		)		)
	assert cached_path(lowercase__		) == text_file
def     __lowerCAmelCase				(_UpperCamelCase		):
	# absolute path
	__lowerCAmelCase			:						int									=      str(tmp_path.resolve() / '__missing_file__.txt'		)
	with pytest.raises(lowercase__		):
		cached_path(lowercase__		)
	# relative path
	__lowerCAmelCase			:						Dict									=      '''./__missing_file__.txt'''
	with pytest.raises(lowercase__		):
		cached_path(lowercase__		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Tuple									=      get_from_cache(F"tmp://{tmpfs_file}"		)
	with open(lowercase__		) as f:
		__lowerCAmelCase			:						Tuple									=      f.read()
	assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE'       ,  lowercase__		)
def     __lowerCAmelCase				():
	with pytest.raises(lowercase__		):
		cached_path('https://huggingface.co'		)
@patch('datasets.config.HF_DATASETS_OFFLINE'       ,  lowercase__		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Union[str, Any]									=      tmp_path_factory.mktemp('data'		) / '''file.html'''
	with pytest.raises(lowercase__		):
		http_get('https://huggingface.co'       ,  temp_file=lowercase__		)
	with pytest.raises(lowercase__		):
		http_head('https://huggingface.co'		)
@patch('datasets.config.HF_DATASETS_OFFLINE'       ,  lowercase__		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[Any]									=      tmp_path_factory.mktemp('data'		) / '''file.html'''
	with pytest.raises(lowercase__		):
		ftp_get('ftp://huggingface.co'       ,  temp_file=lowercase__		)
	with pytest.raises(lowercase__		):
		ftp_head('ftp://huggingface.co'		)
@patch('datasets.config.HF_DATASETS_OFFLINE'       ,  lowercase__		)
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						str									=      tmp_path_factory.mktemp('data'		) / '''file.html'''
	with pytest.raises(lowercase__		):
		fsspec_get('s3://huggingface.co'       ,  temp_file=lowercase__		)
	with pytest.raises(lowercase__		):
		fsspec_head('s3://huggingface.co'		)



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OpenAIGPTConfig,
						    OpenAIGPTDoubleHeadsModel,
						    OpenAIGPTForSequenceClassification,
						    OpenAIGPTLMHeadModel,
						    OpenAIGPTModel,
						)
class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:Optional[int]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:int=13			,			lowerCAmelCase__			:List[str]=7			,			lowerCAmelCase__			:Dict=True			,			lowerCAmelCase__			:List[str]=True			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[Any]=99			,			lowerCAmelCase__			:List[str]=32			,			lowerCAmelCase__			:Any=5			,			lowerCAmelCase__			:List[str]=4			,			lowerCAmelCase__			:int=37			,			lowerCAmelCase__			:Optional[int]="gelu"			,			lowerCAmelCase__			:str=0.1			,			lowerCAmelCase__			:str=0.1			,			lowerCAmelCase__			:Optional[Any]=512			,			lowerCAmelCase__			:Union[str, Any]=16			,			lowerCAmelCase__			:Dict=2			,			lowerCAmelCase__			:Tuple=0.02			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Tuple=4			,			lowerCAmelCase__			:int=None			,			)		->			int:
										__SCREAMING_SNAKE_CASE						:     Dict					=		parent
										__SCREAMING_SNAKE_CASE						:     Any					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		seq_length
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		is_training
										__SCREAMING_SNAKE_CASE						:     int					=		use_token_type_ids
										__SCREAMING_SNAKE_CASE						:     Any					=		use_labels
										__SCREAMING_SNAKE_CASE						:     Any					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     int					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     str					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Tuple					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Dict					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     int					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		type_vocab_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Tuple					=		num_labels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_choices
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		scope
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.vocab_size - 1


				def 		__magic_name__(       self			:Optional[Any]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										if self.use_token_type_ids:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		None
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		None
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		ids_tensor([self.batch_size]			,			self.type_sequence_label_size	)
																__SCREAMING_SNAKE_CASE						:     Tuple					=		ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels	)
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		ids_tensor([self.batch_size]			,			self.num_choices	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		OpenAIGPTConfig(
										    vocab_size=self.vocab_size			,			n_embd=self.hidden_size			,			n_layer=self.num_hidden_layers			,			n_head=self.num_attention_heads			,			n_positions=self.max_position_embeddings			,			pad_token_id=self.pad_token_id			,			)

										__SCREAMING_SNAKE_CASE						:     Any					=		ids_tensor([self.num_hidden_layers, self.num_attention_heads]			,			2	)

										return (
										    config,
										    input_ids,
										    head_mask,
										    token_type_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Any			,			*lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Any					=		OpenAIGPTModel(config=lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			head_mask=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		model(lowerCAmelCase__	)

										self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)	)


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:Dict			,			*lowerCAmelCase__			:List[Any]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		OpenAIGPTLMHeadModel(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			labels=lowerCAmelCase__	)
										self.parent.assertEqual(result.loss.shape			,			()	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)	)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:List[str]			,			*lowerCAmelCase__			:Optional[Any]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Any					=		OpenAIGPTDoubleHeadsModel(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     Any					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			labels=lowerCAmelCase__	)
										self.parent.assertEqual(result.loss.shape			,			()	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)	)


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:str			,			*lowerCAmelCase__			:Optional[int]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.num_labels
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		OpenAIGPTForSequenceClassification(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     List[str]					=		ids_tensor([self.batch_size]			,			self.type_sequence_label_size	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			labels=lowerCAmelCase__	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)	)



				def 		__magic_name__(       self			:Optional[Any]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     str					=		self.prepare_config_and_inputs()
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			
										)						:     List[str]					=		config_and_inputs
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{
										    '''input_ids''': input_ids,
										    '''token_type_ids''': token_type_ids,
										    '''head_mask''': head_mask,
										}

										return config, inputs_dict




@require_torch
class        _lowercase      (			A__						,			A__						,			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		(
				    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__				:			str						=		(
				    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
				SCREAMING_SNAKE_CASE__				:			str						=		(
				    {
				        '''feature-extraction''': OpenAIGPTModel,
				        '''text-classification''': OpenAIGPTForSequenceClassification,
				        '''text-generation''': OpenAIGPTLMHeadModel,
				        '''zero-shot''': OpenAIGPTForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)


				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Union[str, Any]	)		->			Tuple:
										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																# tiny config could not be created.
																return True

										return False


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:int=False	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		super()._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__			,			return_labels=lowerCAmelCase__	)

										if return_labels:
																if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																						__SCREAMING_SNAKE_CASE						:     Any					=		torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)			,			dtype=torch.long			,			device=lowerCAmelCase__			,			)
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		inputs_dict['''labels''']
																						__SCREAMING_SNAKE_CASE						:     Dict					=		inputs_dict['''labels''']
																						__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices)			,			dtype=torch.long			,			device=lowerCAmelCase__			,			)
																						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.zeros(
																						    self.model_tester.batch_size			,			dtype=torch.long			,			device=lowerCAmelCase__	)
										return inputs_dict


				def 		__magic_name__(       self			:Optional[int]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     int					=		OpenAIGPTModelTester(self	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		ConfigTester(self			,			config_class=lowerCAmelCase__			,			n_embd=37	)


				def 		__magic_name__(       self			:Any	)		->			Optional[Any]:
										self.config_tester.run_common_tests()


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase__	)


				def 		__magic_name__(       self			:int	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase__	)



				@slow
				def 		__magic_name__(       self			:Any	)		->			List[Any]:
										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:     Dict					=		OpenAIGPTModel.from_pretrained(lowerCAmelCase__	)
																self.assertIsNotNone(lowerCAmelCase__	)




@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				@slow
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''	)
										model.to(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.tensor([[481, 4_735, 544]]			,			dtype=torch.long			,			device=lowerCAmelCase__	)  # the president is
										__SCREAMING_SNAKE_CASE						:     Dict					=		[
										    481,
										    4_735,
										    544,
										    246,
										    963,
										    870,
										    762,
										    239,
										    244,
										    40_477,
										    244,
										    249,
										    719,
										    881,
										    487,
										    544,
										    240,
										    244,
										    603,
										    481,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										__SCREAMING_SNAKE_CASE						:     Dict					=		model.generate(lowerCAmelCase__			,			do_sample=lowerCAmelCase__	)
										self.assertListEqual(output_ids[0].tolist()			,			lowerCAmelCase__	)
