



"""simple docstring"""

import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    AlbertForMaskedLM,
							    AlbertForMultipleChoice,
							    AlbertForPreTraining,
							    AlbertForQuestionAnswering,
							    AlbertForSequenceClassification,
							    AlbertForTokenClassification,
							    AlbertModel,
							)
							from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=13     ,       _SCREAMING_SNAKE_CASE				:       Tuple=7     ,       _SCREAMING_SNAKE_CASE				:       Tuple=True     ,       _SCREAMING_SNAKE_CASE				:       List[str]=True     ,       _SCREAMING_SNAKE_CASE				:       int=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=True     ,       _SCREAMING_SNAKE_CASE				:       int=99     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=16     ,       _SCREAMING_SNAKE_CASE				:       int=36     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=6     ,       _SCREAMING_SNAKE_CASE				:       Tuple=6     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=6     ,       _SCREAMING_SNAKE_CASE				:       str=37     ,       _SCREAMING_SNAKE_CASE				:       List[str]="gelu"     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       str=0.1     ,       _SCREAMING_SNAKE_CASE				:       Tuple=512     ,       _SCREAMING_SNAKE_CASE				:       str=16     ,       _SCREAMING_SNAKE_CASE				:       str=2     ,       _SCREAMING_SNAKE_CASE				:       str=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=3     ,       _SCREAMING_SNAKE_CASE				:       Any=4     ,       _SCREAMING_SNAKE_CASE				:       List[str]=None     ,       )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     parent
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     batch_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     seq_length
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     is_training
					SCREAMING_SNAKE_CASE						:							List[Any]							=     use_input_mask
					SCREAMING_SNAKE_CASE						:							Tuple							=     use_token_type_ids
					SCREAMING_SNAKE_CASE						:							str							=     use_labels
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     vocab_size
					SCREAMING_SNAKE_CASE						:							List[Any]							=     embedding_size
					SCREAMING_SNAKE_CASE						:							Tuple							=     hidden_size
					SCREAMING_SNAKE_CASE						:							int							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							str							=     num_hidden_groups
					SCREAMING_SNAKE_CASE						:							int							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							Tuple							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Dict							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							List[str]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							List[Any]							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     type_sequence_label_size
					SCREAMING_SNAKE_CASE						:							Any							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     num_labels
					SCREAMING_SNAKE_CASE						:							Tuple							=     num_choices
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     scope





	def 			_lowerCAmelCase     (      self				:       str    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size    )

					SCREAMING_SNAKE_CASE						:							int							=     None
					if self.use_input_mask:
									SCREAMING_SNAKE_CASE						:							str							=     random_attention_mask([self.batch_size, self.seq_length]    )

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

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

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_config()

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





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Optional[int]:





					"""simple docstring"""


					return AlbertConfig(
					    vocab_size=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     ,       num_hidden_groups=self.num_hidden_groups     ,       )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     AlbertModel(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Dict							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     model(_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     model(_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)    )
					self.parent.assertEqual(result.pooler_output.shape     ,       (self.batch_size, self.hidden_size)    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     AlbertForPreTraining(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							List[Any]							=     model(
					    _SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE     ,       sentence_order_label=_SCREAMING_SNAKE_CASE     ,       )
					self.parent.assertEqual(result.prediction_logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)    )
					self.parent.assertEqual(result.sop_logits.shape     ,       (self.batch_size, config.num_labels)    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     AlbertForMaskedLM(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							str							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[Any]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     AlbertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     model(
					    _SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       start_positions=_SCREAMING_SNAKE_CASE     ,       end_positions=_SCREAMING_SNAKE_CASE     ,       )
					self.parent.assertEqual(result.start_logits.shape     ,       (self.batch_size, self.seq_length)    )
					self.parent.assertEqual(result.end_logits.shape     ,       (self.batch_size, self.seq_length)    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.num_labels
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AlbertForSequenceClassification(_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.num_labels
					SCREAMING_SNAKE_CASE						:							List[Any]							=     AlbertForTokenClassification(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							List[Any]							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     self.num_choices
					SCREAMING_SNAKE_CASE						:							Any							=     AlbertForMultipleChoice(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							int							=     input_ids.unsqueeze(1    ).expand(-1     ,       self.num_choices     ,       -1    ).contiguous()
					SCREAMING_SNAKE_CASE						:							Any							=     token_type_ids.unsqueeze(1    ).expand(-1     ,       self.num_choices     ,       -1    ).contiguous()
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     input_mask.unsqueeze(1    ).expand(-1     ,       self.num_choices     ,       -1    ).contiguous()
					SCREAMING_SNAKE_CASE						:							Dict							=     model(
					    _SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE     ,       )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_choices)    )







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     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
					)				,						
					)						:							Tuple							=     config_and_inputs
					SCREAMING_SNAKE_CASE						:							List[str]							=     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
					return config, inputs_dict




@require_torch
class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= (
	    (
	        AlbertModel,
	        AlbertForPreTraining,
	        AlbertForMaskedLM,
	        AlbertForMultipleChoice,
	        AlbertForSequenceClassification,
	        AlbertForTokenClassification,
	        AlbertForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	_SCREAMING_SNAKE_CASE	:		str  						= (
	    {
	        '''feature-extraction''': AlbertModel,
	        '''fill-mask''': AlbertForMaskedLM,
	        '''question-answering''': AlbertForQuestionAnswering,
	        '''text-classification''': AlbertForSequenceClassification,
	        '''token-classification''': AlbertForTokenClassification,
	        '''zero-shot''': AlbertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= True





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Any=False    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     super()._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       return_labels=_SCREAMING_SNAKE_CASE    )

					if return_labels:
									if model_class in get_values(_SCREAMING_SNAKE_CASE    ):
													SCREAMING_SNAKE_CASE						:							str							=     torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Dict							=     torch.zeros(
													    self.model_tester.batch_size     ,       dtype=torch.long     ,       device=_SCREAMING_SNAKE_CASE    )
					return inputs_dict





	def 			_lowerCAmelCase     (      self				:       int    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     AlbertModelTester(self    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       hidden_size=37    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Any:





					"""simple docstring"""


					self.config_tester.run_common_tests()





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE						:							Any							=     type
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )







	@slow
	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	str:





					"""simple docstring"""


					for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AlbertModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )




@require_torch
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	@slow
	def 			_lowerCAmelCase     (      self				:       str    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     AlbertModel.from_pretrained('albert-base-v2'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
					with torch.no_grad():
									SCREAMING_SNAKE_CASE						:							str							=     model(_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )[0]
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.Size((1, 11, 768)    )
					self.assertEqual(output.shape     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.tensor(
					    [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]    )

					self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1E-4    )    )










"""simple docstring"""

import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


A_						:    Optional[int]				=      get_logger(__name__)
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     (
					    os.path.join(_SCREAMING_SNAKE_CASE     ,       config.EXTRACTED_DATASETS_DIR    ) if cache_dir else config.EXTRACTED_DATASETS_PATH
					)
					SCREAMING_SNAKE_CASE						:							Dict							=     Extractor





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	str:





					"""simple docstring"""


					from .file_utils import hash_url_to_filename

					# Path where we extract compressed archives
					# We extract in the cache dir, and get the extracted path name by hashing the original path"
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     os.path.abspath(_SCREAMING_SNAKE_CASE    )
					return os.path.join(self.extract_dir     ,       hash_url_to_filename(_SCREAMING_SNAKE_CASE    )    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool    )						->	bool:





					"""simple docstring"""


					return force_extract or (
					    not os.path.isfile(_SCREAMING_SNAKE_CASE    ) and not (os.path.isdir(_SCREAMING_SNAKE_CASE    ) and os.listdir(_SCREAMING_SNAKE_CASE    ))
					)







	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.extractor.infer_extractor_format(_SCREAMING_SNAKE_CASE    )
					if not extractor_format:
									return input_path
					SCREAMING_SNAKE_CASE						:							List[str]							=     self._get_output_path(_SCREAMING_SNAKE_CASE    )
					if self._do_extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									self.extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					return output_path




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@classmethod
	@abstractmethod
	def 			_lowerCAmelCase     (      cls				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	bool:





					"""simple docstring"""


					...







	@staticmethod
	@abstractmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					...




class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[bytes]  						= []





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	Any:





					"""simple docstring"""


					with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as f:
									return f.read(_SCREAMING_SNAKE_CASE    )







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bytes = b""    )						->	bool:





					"""simple docstring"""


					if not magic_number:
									SCREAMING_SNAKE_CASE						:							Any							=     max(len(_SCREAMING_SNAKE_CASE    ) for cls_magic_number in cls.magic_numbers    )
									try:
													SCREAMING_SNAKE_CASE						:							int							=     cls.read_magic_number(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									except OSError:
													return False
					return any(magic_number.startswith(_SCREAMING_SNAKE_CASE    ) for cls_magic_number in cls.magic_numbers    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]    )						->	bool:





					"""simple docstring"""


					return tarfile.is_tarfile(_SCREAMING_SNAKE_CASE    )





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Dict:





					"""simple docstring"""


					def resolved(_SCREAMING_SNAKE_CASE				:       str    ) -> str:
									return os.path.realpath(os.path.abspath(_SCREAMING_SNAKE_CASE    )    )

					def badpath(_SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       str    ) -> bool:
									# joinpath will ignore base if path is absolute
									return not resolved(os.path.join(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    ).startswith(_SCREAMING_SNAKE_CASE    )

					def badlink(_SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    ) -> bool:
									# Links are interpreted relative to the directory containing the link
									SCREAMING_SNAKE_CASE						:							List[str]							=     resolved(os.path.join(_SCREAMING_SNAKE_CASE     ,       os.path.dirname(info.name    )    )    )
									return badpath(info.linkname     ,       base=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     resolved(_SCREAMING_SNAKE_CASE    )

					for finfo in members:
									if badpath(finfo.name     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)"""    )
									elif finfo.issym() and badlink(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"""    )
									elif finfo.islnk() and badlink(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"""    )
									else:
													yield finfo







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     tarfile.open(_SCREAMING_SNAKE_CASE    )
					tar_file.extractall(_SCREAMING_SNAKE_CASE     ,       members=TarExtractor.safemembers(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )
					tar_file.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= [B'''\x1F\x8B''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with gzip.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as gzip_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= [
	    B'''PK\x03\x04''',
	    B'''PK\x05\x06''',  # empty archive
	    B'''PK\x07\x08''',  # spanned archive
	]





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bytes = b""    )						->	bool:





					"""simple docstring"""


					if super().is_extractable(_SCREAMING_SNAKE_CASE     ,       magic_number=_SCREAMING_SNAKE_CASE    ):
									return True
					try:
									# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
									# From: https://github.com/python/cpython/pull/5053
									from zipfile import (
									    _CD_SIGNATURE,
									    _ECD_DISK_NUMBER,
									    _ECD_DISK_START,
									    _ECD_ENTRIES_TOTAL,
									    _ECD_OFFSET,
									    _ECD_SIZE,
									    _EndRecData,
									    sizeCentralDir,
									    stringCentralDir,
									    structCentralDir,
									)

									with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as fp:
													SCREAMING_SNAKE_CASE						:							List[str]							=     _EndRecData(_SCREAMING_SNAKE_CASE    )
													if endrec:
																	if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
																					return True  # Empty zipfiles are still zipfiles
																	elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
																					fp.seek(endrec[_ECD_OFFSET]    )  # Central directory is on the same disk
																					if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
																									SCREAMING_SNAKE_CASE						:							str							=     fp.read(_SCREAMING_SNAKE_CASE    )  # CD is where we expect it to be
																									if len(_SCREAMING_SNAKE_CASE    ) == sizeCentralDir:
																													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     struct.unpack(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )  # CD is the right size
																													if centdir[_CD_SIGNATURE] == stringCentralDir:
																																	return True  # First central directory entry  has correct magic number
									return False
					except Exception:  # catch all errors in case future python versions change the zipfile internals
									return False







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					with zipfile.ZipFile(_SCREAMING_SNAKE_CASE     ,       'r'    ) as zip_file:
									zip_file.extractall(_SCREAMING_SNAKE_CASE    )
									zip_file.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= [B'''\xFD\x37\x7A\x58\x5A\x00''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with lzma.open(_SCREAMING_SNAKE_CASE    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00''']  # RAR_ID  # RAR5_ID





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.RARFILE_AVAILABLE:
									raise ImportError('Please pip install rarfile'    )
					import rarfile

					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     rarfile.RarFile(_SCREAMING_SNAKE_CASE    )
					rf.extractall(_SCREAMING_SNAKE_CASE    )
					rf.close()




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= [B'''\x28\xb5\x2F\xFD''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.ZSTANDARD_AVAILABLE:
									raise ImportError('Please pip install zstandard'    )
					import zstandard as zstd

					SCREAMING_SNAKE_CASE						:							Any							=     zstd.ZstdDecompressor()
					with open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as ifh, open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as ofh:
									dctx.copy_stream(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= [B'''\x42\x5A\x68''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					with bza.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= [B'''\x37\x7A\xBC\xAF\x27\x1C''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.PY7ZR_AVAILABLE:
									raise ImportError('Please pip install py7zr'    )
					import pyazr

					os.makedirs(_SCREAMING_SNAKE_CASE     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE     ,       'r'    ) as archive:
									archive.extractall(_SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= [B'''\x04\x22\x4D\x18''']





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	None:





					"""simple docstring"""


					if not config.LZ4_AVAILABLE:
									raise ImportError('Please pip install lz4'    )
					import lza.frame

					with lza.frame.open(_SCREAMING_SNAKE_CASE     ,       'rb'    ) as compressed_file:
									with open(_SCREAMING_SNAKE_CASE     ,       'wb'    ) as extracted_file:
													shutil.copyfileobj(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict[str, Type[BaseExtractor]]  						= {
	    "tar": TarExtractor,
	    "gzip": GzipExtractor,
	    "zip": ZipExtractor,
	    "xz": XzExtractor,
	    "rar": RarExtractor,
	    "zstd": ZstdExtractor,
	    "bz2": BzipaExtractor,
	    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
	    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
	}





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					return max(
					    len(_SCREAMING_SNAKE_CASE    )
					    for extractor in cls.extractors.values()
					    if issubclass(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					    for extractor_magic_number in extractor.magic_numbers    )





	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	List[str]:





					"""simple docstring"""


					try:
									return MagicNumberBaseExtractor.read_magic_number(_SCREAMING_SNAKE_CASE     ,       magic_number_length=_SCREAMING_SNAKE_CASE    )
					except OSError:
									return b""





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	bool:





					"""simple docstring"""


					warnings.warn(
					    'Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
					    'Use \'infer_extractor_format\' instead.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     cls.infer_extractor_format(_SCREAMING_SNAKE_CASE    )
					if extractor_format:
									return True if not return_extractor else (True, cls.extractors[extractor_format])
					return False if not return_extractor else (False, None)





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]    )						->	str:  # <Added version="2.4.0"/>





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     cls._get_magic_number_max_length()
					SCREAMING_SNAKE_CASE						:							str							=     cls._read_magic_number(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					for extractor_format, extractor in cls.extractors.items():
									if extractor.is_extractable(_SCREAMING_SNAKE_CASE     ,       magic_number=_SCREAMING_SNAKE_CASE    ):
													return extractor_format







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Union[Path, str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[BaseExtractor] = "deprecated"     ,       )						->	None:





					"""simple docstring"""


					os.makedirs(os.path.dirname(_SCREAMING_SNAKE_CASE    )     ,       exist_ok=_SCREAMING_SNAKE_CASE    )
					# Prevent parallel extractions
					SCREAMING_SNAKE_CASE						:							List[str]							=     str(Path(_SCREAMING_SNAKE_CASE    ).with_suffix('.lock'    )    )
					with FileLock(_SCREAMING_SNAKE_CASE    ):
									shutil.rmtree(_SCREAMING_SNAKE_CASE     ,       ignore_errors=_SCREAMING_SNAKE_CASE    )
									if extractor_format or extractor != "deprecated":
													if extractor != "deprecated" or not isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):  # passed as positional arg
																	warnings.warn(
																	    'Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '
																	    'Use \'extractor_format\' instead.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
																	SCREAMING_SNAKE_CASE						:							int							=     extractor if extractor != 'deprecated' else extractor_format
													else:
																	SCREAMING_SNAKE_CASE						:							int							=     cls.extractors[extractor_format]
													return extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									else:
													warnings.warn(
													    'Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '
													    'exception in 3.0.0.'     ,       category=_SCREAMING_SNAKE_CASE     ,       )
													for extractor in cls.extractors.values():
																	if extractor.is_extractable(_SCREAMING_SNAKE_CASE    ):
																					return extractor.extract(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





