
"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_a														=		logging.getLogger(__name__)






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				42
		lowercase__      =				42






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				42
		lowercase__      =				None
		lowercase__      =				None






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'train'
		lowercase__      =				'dev'
		lowercase__      =				'test'






class    _UpperCAmelCase:



		@staticmethod
		def 						UpperCAmelCase							( __a    ,		__a)	->	List[InputExample]:






							'''simple docstring'''
							raise NotImplementedError



		@staticmethod
		def 						UpperCAmelCase							( __a)	->	List[str]:






							'''simple docstring'''
							raise NotImplementedError




		@staticmethod
		def 						UpperCAmelCase							( __a    ,		__a    ,		__a    ,		__a    ,		__a=False    ,		__a="[CLS]"    ,		__a=1    ,		__a="[SEP]"    ,		__a=False    ,		__a=False    ,		__a=0    ,		__a=0    ,		__a=-1_00    ,		__a=0    ,		__a=True    ,		)	->	List[InputFeatures]:






							'''simple docstring'''
							_UpperCamelCase										=  {label: i for i, label in enumerate(__a)}

							_UpperCamelCase										=  []
							for ex_index, example in enumerate(__a):
												if ex_index % 1_00_00 == 0:
																	logger.info('''Writing example %d of %d'''    ,		__a    ,		len(__a))

												_UpperCamelCase										=  []
												_UpperCamelCase										=  []
												for word, label in zip(example.words    ,		example.labels):
																	_UpperCamelCase										=  tokenizer.tokenize(__a)

																	# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																	if len(__a) > 0:
																						tokens.extend(__a)
																						# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																						label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__a) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
												_UpperCamelCase										=  tokenizer.num_special_tokens_to_add()
												if len(__a) > max_seq_length - special_tokens_count:
																	_UpperCamelCase										=  tokens[: (max_seq_length - special_tokens_count)]
																	_UpperCamelCase										=  label_ids[: (max_seq_length - special_tokens_count)]

												# The convention in BERT is:
												# (a) For sequence pairs:
												#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
												#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
												# (b) For single sequences:
												#  tokens:   [CLS] the dog is hairy . [SEP]
												#  type_ids:   0   0   0   0  0     0   0
												#
												# Where "type_ids" are used to indicate whether this is the first
												# sequence or the second sequence. The embedding vectors for `type=0` and
												# `type=1` were learned during pre-training and are added to the wordpiece
												# embedding vector (and position vector). This is not *strictly* necessary
												# since the [SEP] token unambiguously separates the sequences, but it makes
												# it easier for the model to learn the concept of sequences.
												#
												# For classification tasks, the first vector (corresponding to [CLS]) is
												# used as the "sentence vector". Note that this only makes sense because
												# the entire model is fine-tuned.
												tokens += [sep_token]
												label_ids += [pad_token_label_id]
												if sep_token_extra:
																	# roberta uses an extra separator b/w pairs of sentences
																	tokens += [sep_token]
																	label_ids += [pad_token_label_id]
												_UpperCamelCase										=  [sequence_a_segment_id] * len(__a)

												if cls_token_at_end:
																	tokens += [cls_token]
																	label_ids += [pad_token_label_id]
																	segment_ids += [cls_token_segment_id]
												else:
																	_UpperCamelCase										=  [cls_token] + tokens
																	_UpperCamelCase										=  [pad_token_label_id] + label_ids
																	_UpperCamelCase										=  [cls_token_segment_id] + segment_ids

												_UpperCamelCase										=  tokenizer.convert_tokens_to_ids(__a)

												# The mask has 1 for real tokens and 0 for padding tokens. Only real
												# tokens are attended to.
												_UpperCamelCase										=  [1 if mask_padding_with_zero else 0] * len(__a)

												# Zero-pad up to the sequence length.
												_UpperCamelCase										=  max_seq_length - len(__a)
												if pad_on_left:
																	_UpperCamelCase										=  ([pad_token] * padding_length) + input_ids
																	_UpperCamelCase										=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																	_UpperCamelCase										=  ([pad_token_segment_id] * padding_length) + segment_ids
																	_UpperCamelCase										=  ([pad_token_label_id] * padding_length) + label_ids
												else:
																	input_ids += [pad_token] * padding_length
																	input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																	segment_ids += [pad_token_segment_id] * padding_length
																	label_ids += [pad_token_label_id] * padding_length

												assert len(__a) == max_seq_length
												assert len(__a) == max_seq_length
												assert len(__a) == max_seq_length
												assert len(__a) == max_seq_length

												if ex_index < 5:
																	logger.info('''*** Example ***''')
																	logger.info('''guid: %s'''    ,		example.guid)
																	logger.info('''tokens: %s'''    ,		''' '''.join([str(__a) for x in tokens]))
																	logger.info('''input_ids: %s'''    ,		''' '''.join([str(__a) for x in input_ids]))
																	logger.info('''input_mask: %s'''    ,		''' '''.join([str(__a) for x in input_mask]))
																	logger.info('''segment_ids: %s'''    ,		''' '''.join([str(__a) for x in segment_ids]))
																	logger.info('''label_ids: %s'''    ,		''' '''.join([str(__a) for x in label_ids]))

												if "token_type_ids" not in tokenizer.model_input_names:
																	_UpperCamelCase										=  None

												features.append(
												    InputFeatures(
												        input_ids=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		label_ids=__a))
							return features


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset






		class    _UpperCAmelCase(   lowerCamelCase     ):
				lowercase__      =				42
				lowercase__      =				nn.CrossEntropyLoss().ignore_index



				def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		__a=False    ,		__a = Split.train    ,		)	->	Optional[Any]:






									'''simple docstring'''
									_UpperCamelCase										=  os.path.join(
									    __a    ,		'''cached_{}_{}_{}'''.format(mode.value    ,		tokenizer.__class__.__name__    ,		str(__a))    ,		)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									_UpperCamelCase										=  cached_features_file + '''.lock'''
									with FileLock(__a):
														if os.path.exists(__a) and not overwrite_cache:
																			logger.info(F'''Loading features from cached file {cached_features_file}''')
																			_UpperCamelCase										=  torch.load(__a)
														else:
																			logger.info(F'''Creating features from dataset file at {data_dir}''')
																			_UpperCamelCase										=  token_classification_task.read_examples_from_file(__a    ,		__a)
																			# TODO clean up all this to leverage built-in features of tokenizers
																			_UpperCamelCase										=  token_classification_task.convert_examples_to_features(
																			    __a    ,		__a    ,		__a    ,		__a    ,		cls_token_at_end=bool(model_type in ['''xlnet'''])    ,		cls_token=tokenizer.cls_token    ,		cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0    ,		sep_token=tokenizer.sep_token    ,		sep_token_extra=__a    ,		pad_on_left=bool(tokenizer.padding_side == '''left''')    ,		pad_token=tokenizer.pad_token_id    ,		pad_token_segment_id=tokenizer.pad_token_type_id    ,		pad_token_label_id=self.pad_token_label_id    ,		)
																			logger.info(F'''Saving features into cached file {cached_features_file}''')
																			torch.save(self.features    ,		__a)



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






									'''simple docstring'''
									return len(self.features)




				def __getitem__( self    ,		__a)	->	InputFeatures:






									'''simple docstring'''
									return self.features[i]


if is_tf_available():
		import tensorflow as tf






		class    _UpperCAmelCase:
				lowercase__      =				42
				lowercase__      =				-1_00



				def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		__a=False    ,		__a = Split.train    ,		)	->	str:






									'''simple docstring'''
									_UpperCamelCase										=  token_classification_task.read_examples_from_file(__a    ,		__a)
									# TODO clean up all this to leverage built-in features of tokenizers
									_UpperCamelCase										=  token_classification_task.convert_examples_to_features(
									    __a    ,		__a    ,		__a    ,		__a    ,		cls_token_at_end=bool(model_type in ['''xlnet'''])    ,		cls_token=tokenizer.cls_token    ,		cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0    ,		sep_token=tokenizer.sep_token    ,		sep_token_extra=__a    ,		pad_on_left=bool(tokenizer.padding_side == '''left''')    ,		pad_token=tokenizer.pad_token_id    ,		pad_token_segment_id=tokenizer.pad_token_type_id    ,		pad_token_label_id=self.pad_token_label_id    ,		)

									def gen():
														for ex in self.features:
																			if ex.token_type_ids is None:
																								yield (
																								    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																								    ex.label_ids,
																								)
																			else:
																								yield (
																								    {
																								        "input_ids": ex.input_ids,
																								        "attention_mask": ex.attention_mask,
																								        "token_type_ids": ex.token_type_ids,
																								    },
																								    ex.label_ids,
																								)

									if "token_type_ids" not in tokenizer.model_input_names:
														_UpperCamelCase										=  tf.data.Dataset.from_generator(
														    __a    ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,		(
														        {'''input_ids''': tf.TensorShape([None]), '''attention_mask''': tf.TensorShape([None])},
														        tf.TensorShape([None]),
														    )    ,		)
									else:
														_UpperCamelCase										=  tf.data.Dataset.from_generator(
														    __a    ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)    ,		(
														        {
														            '''input_ids''': tf.TensorShape([None]),
														            '''attention_mask''': tf.TensorShape([None]),
														            '''token_type_ids''': tf.TensorShape([None]),
														        },
														        tf.TensorShape([None]),
														    )    ,		)



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






									'''simple docstring'''
									_UpperCamelCase										=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

									return self.dataset



				def __len__( self)	->	Any:






									'''simple docstring'''
									return len(self.features)




				def __getitem__( self    ,		__a)	->	InputFeatures:






									'''simple docstring'''
									return self.features[i]








"""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    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=99    ,		__a=16    ,		__a=36    ,		__a=6    ,		__a=6    ,		__a=6    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=16    ,		__a=2    ,		__a=0.02    ,		__a=3    ,		__a=4    ,		__a=None    ,		)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  seq_length
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_input_mask
							_UpperCamelCase										=  use_token_type_ids
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  embedding_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_hidden_groups
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  type_sequence_label_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  num_labels
							_UpperCamelCase										=  num_choices
							_UpperCamelCase										=  scope



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






							'''simple docstring'''
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size)

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

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

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

							_UpperCamelCase										=  self.get_config()

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



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






							'''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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  model(__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertForPreTraining(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(
							    __a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a    ,		sentence_order_label=__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, config.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertForMaskedLM(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertForQuestionAnswering(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(
							    __a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		start_positions=__a    ,		end_positions=__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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  AlbertForSequenceClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  AlbertForTokenClassification(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_choices
							_UpperCamelCase										=  AlbertForMultipleChoice(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  input_ids.unsqueeze(1).expand(-1    ,		self.num_choices    ,		-1).contiguous()
							_UpperCamelCase										=  token_type_ids.unsqueeze(1).expand(-1    ,		self.num_choices    ,		-1).contiguous()
							_UpperCamelCase										=  input_mask.unsqueeze(1).expand(-1    ,		self.num_choices    ,		-1).contiguous()
							_UpperCamelCase										=  model(
							    __a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a    ,		)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_choices))




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






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  
							)										=  config_and_inputs
							_UpperCamelCase										=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_torch
class    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (
		        AlbertModel,
		        AlbertForPreTraining,
		        AlbertForMaskedLM,
		        AlbertForMultipleChoice,
		        AlbertForSequenceClassification,
		        AlbertForTokenClassification,
		        AlbertForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__      =				(
		    {
		        'feature-extraction': AlbertModel,
		        'fill-mask': AlbertForMaskedLM,
		        'question-answering': AlbertForQuestionAnswering,
		        'text-classification': AlbertForSequenceClassification,
		        'token-classification': AlbertForTokenClassification,
		        'zero-shot': AlbertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowercase__      =				True



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a=False)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  super()._prepare_for_class(__a    ,		__a    ,		return_labels=__a)

							if return_labels:
												if model_class in get_values(__a):
																	_UpperCamelCase										=  torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)    ,		dtype=torch.long    ,		device=__a)
																	_UpperCamelCase										=  torch.zeros(
																	    self.model_tester.batch_size    ,		dtype=torch.long    ,		device=__a)
							return inputs_dict



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



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






							'''simple docstring'''
							self.config_tester.run_common_tests()



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






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*__a)



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






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__a)



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



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






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
												_UpperCamelCase										=  type
												self.model_tester.create_and_check_model(*__a)




		@slow
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  AlbertModel.from_pretrained(__a)
												self.assertIsNotNone(__a)






@require_torch
class    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  AlbertModel.from_pretrained('''albert-base-v2''')
							_UpperCamelCase										=  torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]])
							_UpperCamelCase										=  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
							with torch.no_grad():
												_UpperCamelCase										=  model(__a    ,		attention_mask=__a)[0]
							_UpperCamelCase										=  torch.Size((1, 11, 7_68))
							self.assertEqual(output.shape    ,		__a)
							_UpperCamelCase										=  torch.tensor(
							    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]])

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






