




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




class     A					(				_A       ):







							def        lowercase__							(    self   :	Dict   ,     __UpperCAmelCase   :	Union[str, Any]   ,     __UpperCAmelCase   :	List[Any]=False				)		->					int:

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_     				= (
										    filename_skip_take['filename'],
										    filename_skip_take['skip'] if 'skip' in filename_skip_take else None,
										    filename_skip_take['take'] if 'take' in filename_skip_take else None,
										)
										open(os.path.join(__A				)   ,     'wb'				).close()
										UpperCamelCase_     				= pa.Table.from_pydict({'filename': [Path(__A				).name] * 100}				)
										if take == -1:
													UpperCamelCase_     				= len(__A				) - skip
										if skip is not None and take is not None:
													UpperCamelCase_     				= pa_table.slice(__A   ,     __A				)
										return pa_table







class     A					(				_A       ):







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

										"""simple docstring"""



										UpperCamelCase_     				= 'my_name'
										UpperCamelCase_     				= SplitInfo(name='train'   ,     num_examples=100				)
										UpperCamelCase_     				= SplitInfo(name='test'   ,     num_examples=100				)
										UpperCamelCase_     				= [train_info, test_info]
										UpperCamelCase_     				= SplitDict()
										split_dict.add(__A				)
										split_dict.add(__A				)
										UpperCamelCase_     				= DatasetInfo(splits=__A				)

										with tempfile.TemporaryDirectory() as tmp_dir:
													UpperCamelCase_     				= ReaderTest(__A   ,     __A				)

													UpperCamelCase_     				= 'test[:33%]'
													UpperCamelCase_     				= Dataset(**reader.read(__A   ,     __A   ,     __A				)				)
													self.assertEqual(dset['filename'][0]   ,     f'''{name}-test'''				)
													self.assertEqual(dset.num_rows   ,     33				)
													self.assertEqual(dset.num_columns   ,     1				)

													UpperCamelCase_     				= ['train', 'test[:33%]']
													UpperCamelCase_     				= [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'				)]
													for instructions in [instructionsa, instructionsa]:
																UpperCamelCase_     				= [reader.read(__A   ,     __A   ,     __A				) for instr in instructions]
																UpperCamelCase_				,   UpperCamelCase_     				= (Dataset(**__A				) for dataset_kwargs in datasets_kwargs)
																self.assertEqual(train_dset['filename'][0]   ,     f'''{name}-train'''				)
																self.assertEqual(train_dset.num_rows   ,     100				)
																self.assertEqual(train_dset.num_columns   ,     1				)
																self.assertIsInstance(train_dset.split   ,     __A				)
																self.assertEqual(str(train_dset.split				)   ,     'train'				)
																self.assertEqual(test_dset['filename'][0]   ,     f'''{name}-test'''				)
																self.assertEqual(test_dset.num_rows   ,     33				)
																self.assertEqual(test_dset.num_columns   ,     1				)
																self.assertIsInstance(test_dset.split   ,     __A				)
																self.assertEqual(str(test_dset.split				)   ,     'test[:33%]'				)
																del train_dset, test_dset







							def        lowercase__							(    self   :	int				)		->					Dict:

										"""simple docstring"""



										UpperCamelCase_     				= 'my_name'
										UpperCamelCase_     				= SplitInfo(name='train'   ,     num_examples=1000   ,     shard_lengths=[100] * 10				)
										UpperCamelCase_     				= [train_info]
										UpperCamelCase_     				= SplitDict()
										split_dict.add(__A				)
										UpperCamelCase_     				= DatasetInfo(splits=__A				)

										with tempfile.TemporaryDirectory() as tmp_dir:
													UpperCamelCase_     				= ReaderTest(__A   ,     __A				)

													UpperCamelCase_     				= 'train[:33%]'
													UpperCamelCase_     				= Dataset(**reader.read(__A   ,     __A   ,     __A				)				)
													self.assertEqual(dset['filename'][0]   ,     f'''{name}-train-00000-of-00010'''				)
													self.assertEqual(dset['filename'][-1]   ,     f'''{name}-train-00003-of-00010'''				)
													self.assertEqual(dset.num_rows   ,     330				)
													self.assertEqual(dset.num_columns   ,     1				)







							def        lowercase__							(    self   :	Optional[int]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= SplitInfo(name='train'   ,     num_examples=100				)
										UpperCamelCase_     				= SplitInfo(name='test'   ,     num_examples=100				)
										UpperCamelCase_     				= SplitDict()
										split_dict.add(__A				)
										split_dict.add(__A				)
										UpperCamelCase_     				= DatasetInfo(splits=__A				)

										with tempfile.TemporaryDirectory() as tmp_dir:
													UpperCamelCase_     				= ReaderTest(__A   ,     __A				)

													UpperCamelCase_     				= [
													    {'filename': os.path.join(__A   ,     'train'				)},
													    {'filename': os.path.join(__A   ,     'test'				), 'skip': 10, 'take': 10},
													]
													UpperCamelCase_     				= Dataset(**reader.read_files(__A   ,     original_instructions='train+test[10:20]'				)				)
													self.assertEqual(dset.num_rows   ,     110				)
													self.assertEqual(dset.num_columns   ,     1				)
													del dset







@pytest.mark.parametrize('in_memory'     ,		[False, True]					)
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Tuple:

			'''simple docstring'''


			UpperCamelCase_     				= arrow_file
			with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
						UpperCamelCase_     				= ArrowReader.read_table(UpperCAmelCase_     ,		in_memory=UpperCAmelCase_					)
			assert table.shape == dataset.data.shape
			assert set(table.column_names					) == set(dataset.data.column_names					)
			assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




@pytest.mark.parametrize('in_memory'     ,		[False, True]					)
def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			Dict:

			'''simple docstring'''


			UpperCamelCase_     				= arrow_file
			UpperCamelCase_     				= ArrowReader(''     ,		UpperCAmelCase_					)
			with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
						UpperCamelCase_     				= reader.read_files([{'filename': filename}]     ,		in_memory=UpperCAmelCase_					)
			assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
			UpperCamelCase_     				= dataset_kwargs['arrow_table']
			assert table.shape == dataset.data.shape
			assert set(table.column_names					) == set(dataset.data.column_names					)
			assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




def  a_		(							)	->			Dict:

			'''simple docstring'''


			assert ReadInstruction('train'     ,		to=1_0     ,		unit='abs'					).to_spec() == "train[:10]"
			assert ReadInstruction('train'     ,		from_=-8_0     ,		to=1_0     ,		unit='%'					).to_spec() == "train[-80%:10%]"

			UpperCamelCase_     				= 'train+test'
			assert ReadInstruction.from_spec(UpperCAmelCase_					).to_spec() == spec_train_test

			UpperCamelCase_     				= 'train[2:10]'
			assert ReadInstruction.from_spec(UpperCAmelCase_					).to_spec() == spec_train_abs

			UpperCamelCase_     				= 'train[15%:-20%]'
			assert ReadInstruction.from_spec(UpperCAmelCase_					).to_spec() == spec_train_pct

			UpperCamelCase_     				= 'train[:10%](closest)'
			assert ReadInstruction.from_spec(UpperCAmelCase_					).to_spec() == "train[:10%]"

			UpperCamelCase_     				= 'train[:10%](pct1_dropremainder)'
			assert ReadInstruction.from_spec(UpperCAmelCase_					).to_spec() == spec_train_pct_rounding

			UpperCamelCase_     				= 'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
			assert ReadInstruction.from_spec(UpperCAmelCase_					).to_spec() == spec_train_test_pct_rounding




def  a_		(							)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= 'dummy'
			UpperCamelCase_     				= [SplitInfo(name='train'     ,		num_examples=1_0_0					)]
			UpperCamelCase_     				= 'train[:33%]'
			UpperCamelCase_     				= 'arrow'
			UpperCamelCase_     				= 'prefix'

			UpperCamelCase_     				= make_file_instructions(UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_					)
			assert isinstance(UpperCAmelCase_     ,		UpperCAmelCase_					)
			assert file_instructions.num_examples == 3_3
			assert file_instructions.file_instructions == [
			    {"filename": os.path.join(UpperCAmelCase_     ,		F'''{name}-train.arrow'''					), "skip": 0, "take": 3_3}
			]

			UpperCamelCase_     				= [SplitInfo(name='train'     ,		num_examples=1_0_0     ,		shard_lengths=[1_0] * 1_0					)]
			UpperCamelCase_     				= make_file_instructions(UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_					)
			assert isinstance(UpperCAmelCase_     ,		UpperCAmelCase_					)
			assert file_instructions.num_examples == 3_3
			assert file_instructions.file_instructions == [
			    {"filename": os.path.join(UpperCAmelCase_     ,		F'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
			    {"filename": os.path.join(UpperCAmelCase_     ,		F'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
			    {"filename": os.path.join(UpperCAmelCase_     ,		F'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
			    {"filename": os.path.join(UpperCAmelCase_     ,		F'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
			]





@pytest.mark.parametrize('name, expected_exception'     ,		[(None, TypeError), ('', ValueError)]					)
def  a_		(							__snake_case     ,		__snake_case					)	->			Tuple:

			'''simple docstring'''


			UpperCamelCase_     				= [SplitInfo(name='train'     ,		num_examples=1_0_0					)]
			UpperCamelCase_     				= 'train'
			UpperCamelCase_     				= 'arrow'
			UpperCamelCase_     				= 'prefix_path'
			with pytest.raises(UpperCAmelCase_					):
						UpperCamelCase_     				= make_file_instructions(UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_     ,		UpperCAmelCase_					)





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
		from transformers.models.albert.modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertModel,
		)

class      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Dict   ,		__A		:  Optional[Any]=13   ,		__A		:  Dict=7   ,		__A		:  Dict=True   ,		__A		:  Any=True   ,		__A		:  Any=True   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=99   ,		__A		:  Optional[int]=16   ,		__A		:  Tuple=32   ,		__A		:  Optional[int]=2   ,		__A		:  Dict=4   ,		__A		:  Dict=37   ,		__A		:  Any="gelu"   ,		__A		:  Union[str, Any]=0.1   ,		__A		:  List[str]=0.1   ,		__A		:  Any=512   ,		__A		:  Optional[Any]=16   ,		__A		:  List[str]=2   ,		__A		:  List[str]=0.0_2   ,		__A		:  List[str]=3   ,		__A		:  Dict=4   ,		__A		:  List[str]=None   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   13
								lowerCAmelCase__  =   7
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   99
								lowerCAmelCase__  =   16
								lowerCAmelCase__  =   32
								lowerCAmelCase__  =   2
								lowerCAmelCase__  =   4
								lowerCAmelCase__  =   37
								lowerCAmelCase__  =   """gelu"""
								lowerCAmelCase__  =   0.1
								lowerCAmelCase__  =   0.1
								lowerCAmelCase__  =   512
								lowerCAmelCase__  =   16
								lowerCAmelCase__  =   2
								lowerCAmelCase__  =   0.0_2
								lowerCAmelCase__  =   3
								lowerCAmelCase__  =   4
								lowerCAmelCase__  =   None




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




								'''simple docstring'''





								lowerCAmelCase__  =   ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size					)

								lowerCAmelCase__  =   None
								if self.use_input_mask:
															lowerCAmelCase__  =   random_attention_mask([self.batch_size, self.seq_length]					)

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

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

								lowerCAmelCase__  =   AlbertConfig(
								    vocab_size=self.vocab_size   ,		hidden_size=self.hidden_size   ,		embedding_size=self.embedding_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      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Optional[Any]   ,		__A		:  Tuple   ,		__A		:  List[Any]   ,		__A		:  Tuple   ,		__A		:  Dict   ,		__A		:  Optional[int]   ,		__A		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertModel(config=__A					)
								# inputs = {'input_ids': input_ids,
								#           'attention_mask': input_mask,
								#           'token_type_ids': token_type_ids}
								# sequence_output, pooled_output = model(**inputs)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)

								lowerCAmelCase__  =   [input_ids, input_mask]
								lowerCAmelCase__  =   model(__A					)

								lowerCAmelCase__  =   model(__A					)

								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      lowercase__							(  self		:  List[str]   ,		__A		:  Optional[int]   ,		__A		:  Any   ,		__A		:  List[str]   ,		__A		:  Optional[int]   ,		__A		:  List[Any]   ,		__A		:  Optional[int]   ,		__A		:  Union[str, Any]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   TFAlbertForPreTraining(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								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, self.num_labels)					)




	def      lowercase__							(  self		:  str   ,		__A		:  Dict   ,		__A		:  Any   ,		__A		:  List[Any]   ,		__A		:  Tuple   ,		__A		:  Union[str, Any]   ,		__A		:  int   ,		__A		:  str					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertForMaskedLM(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.vocab_size)					)




	def      lowercase__							(  self		:  Dict   ,		__A		:  List[Any]   ,		__A		:  Union[str, Any]   ,		__A		:  Union[str, Any]   ,		__A		:  Optional[Any]   ,		__A		:  Optional[Any]   ,		__A		:  str   ,		__A		:  List[str]					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   TFAlbertForSequenceClassification(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels)					)




	def      lowercase__							(  self		:  Dict   ,		__A		:  Any   ,		__A		:  Tuple   ,		__A		:  Union[str, Any]   ,		__A		:  List[Any]   ,		__A		:  List[Any]   ,		__A		:  Any   ,		__A		:  Any					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertForQuestionAnswering(config=__A					)
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
								lowerCAmelCase__  =   model(__A					)
								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      lowercase__							(  self		:  List[Any]   ,		__A		:  Union[str, Any]   ,		__A		:  List[Any]   ,		__A		:  Tuple   ,		__A		:  Dict   ,		__A		:  Tuple   ,		__A		:  List[str]   ,		__A		:  Dict					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_choices
								lowerCAmelCase__  =   TFAlbertForMultipleChoice(config=__A					)
								lowerCAmelCase__  =   tf.tile(tf.expand_dims(__A   ,		1					)   ,		(1, self.num_choices, 1)					)
								lowerCAmelCase__  =   tf.tile(tf.expand_dims(__A   ,		1					)   ,		(1, self.num_choices, 1)					)
								lowerCAmelCase__  =   tf.tile(tf.expand_dims(__A   ,		1					)   ,		(1, self.num_choices, 1)					)
								lowerCAmelCase__  =   {
								    """input_ids""": multiple_choice_inputs_ids,
								    """attention_mask""": multiple_choice_input_mask,
								    """token_type_ids""": multiple_choice_token_type_ids,
								}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertListEqual(list(result["""logits"""].shape					)   ,		[self.batch_size, self.num_choices]					)




	def      lowercase__							(  self		:  int   ,		__A		:  Tuple   ,		__A		:  Optional[Any]   ,		__A		:  Any   ,		__A		:  Union[str, Any]   ,		__A		:  Tuple   ,		__A		:  str   ,		__A		:  Optional[Any]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   self.num_labels
								lowerCAmelCase__  =   TFAlbertForTokenClassification(config=__A					)
								lowerCAmelCase__  =   {
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertListEqual(list(result["""logits"""].shape					)   ,		[self.batch_size, self.seq_length, self.num_labels]					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   self.prepare_config_and_inputs()
								(
								    (
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,(
								    lowerCAmelCase__
								)  ,
								)  =   config_and_inputs
								lowerCAmelCase__  =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict





@require_tf
class      lowerCamelCase__     (     _A,     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	(
	    (
	        TFAlbertModel,
	        TFAlbertForPreTraining,
	        TFAlbertForMaskedLM,
	        TFAlbertForSequenceClassification,
	        TFAlbertForQuestionAnswering,
	        TFAlbertForTokenClassification,
	        TFAlbertForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	A__		=	(
	    {
	        '''feature-extraction''': TFAlbertModel,
	        '''fill-mask''': TFAlbertForMaskedLM,
	        '''question-answering''': TFAlbertForQuestionAnswering,
	        '''text-classification''': TFAlbertForSequenceClassification,
	        '''token-classification''': TFAlbertForTokenClassification,
	        '''zero-shot''': TFAlbertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	A__		=	False
	A__		=	False




	def      lowercase__							(  self		:  Any   ,		__A		:  List[str]   ,		__A		:  int   ,		__A		:  Union[str, Any]=False					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   super()._prepare_for_class(__A   ,		__A   ,		return_labels=__A					)

								if return_labels:
															if model_class in get_values(__A					):
																						lowerCAmelCase__  =   tf.zeros(self.model_tester.batch_size   ,		dtype=tf.intaa					)

								return inputs_dict




	def      lowercase__							(  self		:  Union[str, Any]					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertModelTester(self					)
								lowerCAmelCase__  =   ConfigTester(self   ,		config_class=__A   ,		hidden_size=37					)




	def      lowercase__							(  self		:  List[str]					)     ->			str:




								'''simple docstring'''





								self.config_tester.run_common_tests()




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_model(*__A					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_pretraining(*__A					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_masked_lm(*__A					)




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_multiple_choice(*__A					)




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_sequence_classification(*__A					)




	def      lowercase__							(  self		:  Any					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_albert_for_question_answering(*__A					)




	@slow
	def      lowercase__							(  self		:  Tuple					)     ->			Dict:




								'''simple docstring'''





								for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase__  =   TFAlbertModel.from_pretrained(__A					)
															self.assertIsNotNone(__A					)





@require_tf
class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	@slow
	def      lowercase__							(  self		:  Optional[int]					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""					)
								lowerCAmelCase__  =   tf.constant([[0, 1, 2, 3, 4, 5]]					)
								lowerCAmelCase__  =   model(__A					)[0]

								lowerCAmelCase__  =   [1, 6, 3_0000]
								self.assertEqual(output.shape   ,		__A					)

								lowerCAmelCase__  =   tf.constant(
								    [
								        [
								            [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
								            [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
								            [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
								        ]
								    ]					)
								tf.debugging.assert_near(output[:, :3, :3]   ,		__A   ,		atol=1E-4					)





