

'''simple docstring'''





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase_    						=       logging.getLogger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						return (preds == labels).mean()





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()      )}      )
				lowerCamelCase_		       =    field(metadata={'help': 'Should contain the data files for the task.'}      )
				lowerCamelCase_		       =    field(
				    default=1_28						,							metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )






def 				_lowerCAmelCase			(   )				->  int:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						lowercase :							Dict    		=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
						lowercase		,	lowercase		,	lowercase :							List[Any]    		=parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir						)
						    and os.listdir(training_args.output_dir						)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    ''' --overwrite_output_dir to overcome.'''						)

						# Setup logging
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   , datefmt='''%m/%d/%Y %H:%M:%S'''   , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   , )
						logger.warning(
						    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''   , training_args.local_rank   , training_args.device   , training_args.n_gpu   , bool(training_args.local_rank != -1						)   , training_args.fpaa   , )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank						):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('''Training/evaluation parameters %s'''   , __magic_name__						)

						# Set seed
						set_seed(training_args.seed						)

						try:
												lowercase :							Any    		=processors[data_args.task_name]()
												lowercase :							Optional[int]    		=processor.get_labels()
												lowercase :							str    		=len(__magic_name__						)
						except KeyError:
												raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase :							List[str]    		=AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   , num_labels=__magic_name__   , finetuning_task=data_args.task_name   , cache_dir=model_args.cache_dir   , )
						lowercase :							int    		=AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   , cache_dir=model_args.cache_dir   , )
						lowercase :							Any    		=AutoModelForMultipleChoice.from_pretrained(
						    model_args.model_name_or_path   , from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)   , config=__magic_name__   , cache_dir=model_args.cache_dir   , )

						# Get datasets
						lowercase :							int    		=(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir   , tokenizer=__magic_name__   , task=data_args.task_name   , max_seq_length=data_args.max_seq_length   , overwrite_cache=data_args.overwrite_cache   , mode=Split.train   , )
						    if training_args.do_train
						    else None
						)
						lowercase :							Union[str, Any]    		=(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir   , tokenizer=__magic_name__   , task=data_args.task_name   , max_seq_length=data_args.max_seq_length   , overwrite_cache=data_args.overwrite_cache   , mode=Split.dev   , )
						    if training_args.do_eval
						    else None
						)

						def compute_metrics(__magic_name__						:     EvalPrediction						) -> Dict:
												lowercase :							Dict    		=np.argmax(p.predictions   , axis=1						)
												return {"acc": simple_accuracy(__magic_name__   , p.label_ids						)}

						# Data collator
						lowercase :							List[str]    		=DataCollatorWithPadding(__magic_name__   , pad_to_multiple_of=8						) if training_args.fpaa else None

						# Initialize our Trainer
						lowercase :							Dict    		=Trainer(
						    model=__magic_name__   , args=__magic_name__   , train_dataset=__magic_name__   , eval_dataset=__magic_name__   , compute_metrics=__magic_name__   , data_collator=__magic_name__   , )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
						lowercase :							Optional[Any]    		={}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''						)

												lowercase :							List[Any]    		=trainer.evaluate()

												lowercase :							Any    		=os.path.join(training_args.output_dir   , '''eval_results.txt'''						)
												if trainer.is_world_master():
																		with open(__magic_name__   , '''w'''						) as writer:
																								logger.info('''***** Eval results *****'''						)
																								for key, value in result.items():
																														logger.info('''  %s = %s'''   , __magic_name__   , __magic_name__						)
																														writer.write('''%s = %s\n''' % (key, value)						)

																								results.update(__magic_name__						)

						return results






def 				_lowerCAmelCase			(   __magic_name__						:     Any						)				->  Optional[Any]:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()








'''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 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Dict		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		str=13		,					UpperCAmelCase__     :		Tuple=7		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		Tuple=True		,					UpperCAmelCase__     :		Optional[Any]=99		,					UpperCAmelCase__     :		Any=16		,					UpperCAmelCase__     :		Optional[Any]=36		,					UpperCAmelCase__     :		str=6		,					UpperCAmelCase__     :		Any=6		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		Any=37		,					UpperCAmelCase__     :		List[str]="gelu"		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		Optional[int]=0.1		,					UpperCAmelCase__     :		List[str]=512		,					UpperCAmelCase__     :		List[str]=16		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		Any=0.02		,					UpperCAmelCase__     :		Union[str, Any]=3		,					UpperCAmelCase__     :		List[Any]=4		,					UpperCAmelCase__     :		Tuple=None		,					):
										'''simple docstring'''
										lowercase :							str    		=parent
										lowercase :							int    		=batch_size
										lowercase :							Any    		=seq_length
										lowercase :							int    		=is_training
										lowercase :							str    		=use_input_mask
										lowercase :							int    		=use_token_type_ids
										lowercase :							Dict    		=use_labels
										lowercase :							int    		=vocab_size
										lowercase :							str    		=embedding_size
										lowercase :							Union[str, Any]    		=hidden_size
										lowercase :							Tuple    		=num_hidden_layers
										lowercase :							Any    		=num_hidden_groups
										lowercase :							Union[str, Any]    		=num_attention_heads
										lowercase :							Any    		=intermediate_size
										lowercase :							Tuple    		=hidden_act
										lowercase :							Optional[int]    		=hidden_dropout_prob
										lowercase :							Union[str, Any]    		=attention_probs_dropout_prob
										lowercase :							List[Any]    		=max_position_embeddings
										lowercase :							int    		=type_vocab_size
										lowercase :							int    		=type_sequence_label_size
										lowercase :							Any    		=initializer_range
										lowercase :							List[Any]    		=num_labels
										lowercase :							int    		=num_choices
										lowercase :							Optional[int]    		=scope







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		=ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size   )

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

										lowercase :							Dict    		=None
										if self.use_token_type_ids:
																lowercase :							List[str]    		=ids_tensor([self.batch_size, self.seq_length]		,					self.type_vocab_size   )

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

										lowercase :							Any    		=self.get_config()

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''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     :		Union[str, Any]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										lowercase :							int    		=AlbertModel(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							int    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__   )
										lowercase :							Dict    		=model(UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__   )
										lowercase :							int    		=model(UpperCAmelCase__   )
										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     :		Optional[Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Tuple    		=AlbertForPreTraining(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							int    		=model(
										    UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__		,					sentence_order_label=UpperCAmelCase__		,					)
										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     :		List[Any]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							Tuple    		=AlbertForMaskedLM(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							str    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)   )







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=AlbertForQuestionAnswering(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							List[str]    		=model(
										    UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					start_positions=UpperCAmelCase__		,					end_positions=UpperCAmelCase__		,					)
										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     :		List[str]		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.num_labels
										lowercase :							Any    		=AlbertForSequenceClassification(UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							Dict    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.num_labels
										lowercase :							str    		=AlbertForTokenClassification(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							int    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__   )
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.num_choices
										lowercase :							List[Any]    		=AlbertForMultipleChoice(config=UpperCAmelCase__   )
										model.to(UpperCAmelCase__   )
										model.eval()
										lowercase :							List[Any]    		=input_ids.unsqueeze(1   ).expand(-1		,					self.num_choices		,					-1   ).contiguous()
										lowercase :							int    		=token_type_ids.unsqueeze(1   ).expand(-1		,					self.num_choices		,					-1   ).contiguous()
										lowercase :							int    		=input_mask.unsqueeze(1   ).expand(-1		,					self.num_choices		,					-1   ).contiguous()
										lowercase :							List[str]    		=model(
										    UpperCAmelCase__		,					attention_mask=UpperCAmelCase__		,					token_type_ids=UpperCAmelCase__		,					labels=UpperCAmelCase__		,					)
										self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.prepare_config_and_inputs()
										(
										    (
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	(
										    lowercase
										)		,	
										) :							Dict    		=config_and_inputs
										lowercase :							Optional[Any]    		={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





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







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Any		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		int=False   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=super()._prepare_for_class(UpperCAmelCase__		,					UpperCAmelCase__		,					return_labels=UpperCAmelCase__   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=AlbertModelTester(self   )
										lowercase :							Optional[Any]    		=ConfigTester(self		,					config_class=UpperCAmelCase__		,					hidden_size=37   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							int    		=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																lowercase :							Tuple    		=type
																self.model_tester.create_and_check_model(*UpperCAmelCase__   )







				@slow
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase :							str    		=AlbertModel.from_pretrained(UpperCAmelCase__   )
																self.assertIsNotNone(UpperCAmelCase__   )





@require_torch
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							int    		=AlbertModel.from_pretrained('''albert-base-v2'''   )
										lowercase :							Optional[int]    		=torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
										lowercase :							Any    		=torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
										with torch.no_grad():
																lowercase :							Any    		=model(UpperCAmelCase__		,					attention_mask=UpperCAmelCase__   )[0]
										lowercase :							int    		=torch.Size((1, 11, 768)   )
										self.assertEqual(output.shape		,					UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=torch.tensor(
										    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]   )

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





