
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowerCamelCase__          =		logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowerCamelCase__          =		{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase__          =		{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase__          =		sorted(arg_to_scheduler.keys())
lowerCamelCase__          =		"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class      A__							(     pl.LightningModule	):






				def __init__(						self					:      List[str]    ,     a					:      argparse.Namespace    ,     a					:      Optional[Any]=None    ,     a					:      str="base"    ,     a					:      Tuple=None    ,     a					:      Tuple=None    ,     a					:      List[Any]=None    ,     **a					:      int    ,     ):






							'''simple docstring'''







							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(a   )
							lowerCAmelCase__      :      Tuple         =		0
							lowerCAmelCase__      :      int         =		Path(self.hparams.output_dir   )
							lowerCAmelCase__      :      int         =		self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										lowerCAmelCase__      :      str         =		AutoConfig.from_pretrained(
										    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,     **({'num_labels': num_labels} if num_labels is not None else {})    ,     cache_dir=a    ,     **a    ,     )
							else:
										lowerCAmelCase__      :      PretrainedConfig         =		config

							lowerCAmelCase__      :      int         =		('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
										if getattr(self.hparams    ,     a    ,     a   ):
													assert hasattr(self.config    ,     a   ), f'''model config doesn\'t have a `{p}` attribute'''
													setattr(self.config    ,     a    ,     getattr(self.hparams    ,     a   )   )

							if tokenizer is None:
										lowerCAmelCase__      :      Optional[Any]         =		AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,     cache_dir=a    ,     )
							else:
										lowerCAmelCase__      :      PreTrainedTokenizer         =		tokenizer
							lowerCAmelCase__      :      Optional[int]         =		MODEL_MODES[mode]
							if model is None:
										lowerCAmelCase__      :      Any         =		self.model_type.from_pretrained(
										    self.hparams.model_name_or_path    ,     from_tf=bool('.ckpt' in self.hparams.model_name_or_path   )    ,     config=self.config    ,     cache_dir=a    ,     )
							else:
										lowerCAmelCase__      :      Tuple         =		model






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     *a					:      Any    ,     **a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		self.model_type.from_pretrained(*a    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		arg_to_scheduler[self.hparams.lr_scheduler]
							lowerCAmelCase__      :      Optional[Any]         =		get_schedule_func(
							    self.opt    ,     num_warmup_steps=self.hparams.warmup_steps    ,     num_training_steps=self.total_steps()   )
							lowerCAmelCase__      :      Any         =		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
							return scheduler






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.model
							lowerCAmelCase__      :      List[Any]         =		['bias', 'LayerNorm.weight']
							lowerCAmelCase__      :      int         =		[
							    {
							        'params': [
							            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )
							        ],  # check this named paramters
							        'weight_decay': self.hparams.weight_decay,
							    },
							    {
							        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
							        'weight_decay': 0.0,
							    },
							]
							if self.hparams.adafactor:
										lowerCAmelCase__      :      List[Any]         =		Adafactor(
										    a    ,     lr=self.hparams.learning_rate    ,     scale_parameter=a    ,     relative_step=a   )

							else:
										lowerCAmelCase__      :      Any         =		AdamW(
										    a    ,     lr=self.hparams.learning_rate    ,     eps=self.hparams.adam_epsilon   )
							lowerCAmelCase__      :      Dict         =		optimizer

							lowerCAmelCase__      :      str         =		self.get_lr_scheduler()

							return [optimizer], [scheduler]






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Any    ,     a					:      List[str]   ):






							'''simple docstring'''







							return self.validation_step(a    ,     a   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]   ):






							'''simple docstring'''







							return self.validation_end(a   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		max(1    ,     self.hparams.gpus   )  # TODO: consider num_tpu_cores
							lowerCAmelCase__      :      Optional[int]         =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      str   ):






							'''simple docstring'''







							if stage == "test":
										lowerCAmelCase__      :      str         =		len(self.test_dataloader().dataset   )
							else:
										lowerCAmelCase__      :      Dict         =		self.get_dataloader('train'    ,     self.hparams.train_batch_size    ,     shuffle=a   )
										lowerCAmelCase__      :      Union[str, Any]         =		len(self.train_dataloader().dataset   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      str    ,     a					:      int    ,     a					:      bool = False   ):






							'''simple docstring'''







							raise NotImplementedError('You must implement this for your task'   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return self.train_loader






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return self.get_dataloader('dev'    ,     self.hparams.eval_batch_size    ,     shuffle=a   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return self.get_dataloader('test'    ,     self.hparams.eval_batch_size    ,     shuffle=a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Any   ):






							'''simple docstring'''







							return os.path.join(
							    self.hparams.data_dir    ,     'cached_{}_{}_{}'.format(
							        a    ,     list(filter(a    ,     self.hparams.model_name_or_path.split('/'   )   )   ).pop()    ,     str(self.hparams.max_seq_length   )    ,     )    ,     )






				@pl.utilities.rank_zero_only
				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Dict[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.output_dir.joinpath('best_tfmr'   )
							lowerCAmelCase__      :      Tuple         =		self.step_count
							self.model.save_pretrained(a   )
							self.tokenizer.save_pretrained(a   )






				@staticmethod
				def 							_lowerCamelCase		(						a					:      Dict    ,     a					:      Optional[int]   ):






							'''simple docstring'''







							parser.add_argument(
							    '--model_name_or_path'    ,     default=a    ,     type=a    ,     required=a    ,     help='Path to pretrained model or model identifier from huggingface.co/models'    ,     )
							parser.add_argument(
							    '--config_name'    ,     default=''    ,     type=a    ,     help='Pretrained config name or path if not the same as model_name'   )
							parser.add_argument(
							    '--tokenizer_name'    ,     default=a    ,     type=a    ,     help='Pretrained tokenizer name or path if not the same as model_name'    ,     )
							parser.add_argument(
							    '--cache_dir'    ,     default=str(Path(a   ).parent / 'test_run' / 'cache'   )    ,     type=a    ,     help='Where do you want to store the pre-trained models downloaded from huggingface.co'    ,     )
							parser.add_argument(
							    '--encoder_layerdrop'    ,     type=a    ,     help='Encoder layer dropout probability (Optional). Goes into model.config'    ,     )
							parser.add_argument(
							    '--decoder_layerdrop'    ,     type=a    ,     help='Decoder layer dropout probability (Optional). Goes into model.config'    ,     )
							parser.add_argument(
							    '--dropout'    ,     type=a    ,     help='Dropout probability (Optional). Goes into model.config'    ,     )
							parser.add_argument(
							    '--attention_dropout'    ,     type=a    ,     help='Attention dropout probability (Optional). Goes into model.config'    ,     )
							parser.add_argument('--learning_rate'    ,     default=5E-5    ,     type=a    ,     help='The initial learning rate for Adam.'   )
							parser.add_argument(
							    '--lr_scheduler'    ,     default='linear'    ,     choices=a    ,     metavar=a    ,     type=a    ,     help='Learning rate scheduler'    ,     )
							parser.add_argument('--weight_decay'    ,     default=0.0    ,     type=a    ,     help='Weight decay if we apply some.'   )
							parser.add_argument('--adam_epsilon'    ,     default=1E-8    ,     type=a    ,     help='Epsilon for Adam optimizer.'   )
							parser.add_argument('--warmup_steps'    ,     default=0    ,     type=a    ,     help='Linear warmup over warmup_steps.'   )
							parser.add_argument('--num_workers'    ,     default=4    ,     type=a    ,     help='kwarg passed to DataLoader'   )
							parser.add_argument('--num_train_epochs'    ,     dest='max_epochs'    ,     default=3    ,     type=a   )
							parser.add_argument('--train_batch_size'    ,     default=32    ,     type=a   )
							parser.add_argument('--eval_batch_size'    ,     default=32    ,     type=a   )
							parser.add_argument('--adafactor'    ,     action='store_true'   )







class      A__							(     pl.Callback	):






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Tuple    ,     a					:      Any   ):






							'''simple docstring'''







							if (
							    trainer.is_global_zero and trainer.global_rank == 0
							):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
										pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class      A__							(     pl.Callback	):






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      List[str]    ,     a					:      int   ):






							'''simple docstring'''







							for name, param in pl_module.model.rag.named_parameters():
										if param.grad is None:
													print(a   )







class      A__							(     pl.Callback	):






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		trainer.lr_schedulers[0]['scheduler']
							lowerCAmelCase__      :      Union[str, Any]         =		{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
							pl_module.logger.log_metrics(a   )






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      pl.Trainer    ,     a					:      pl.LightningModule   ):






							'''simple docstring'''







							rank_zero_info('***** Validation results *****'   )
							lowerCAmelCase__      :      Optional[int]         =		trainer.callback_metrics
							# Log results
							for key in sorted(a   ):
										if key not in ["log", "progress_bar"]:
													rank_zero_info('{} = {}\n'.format(a    ,     str(metrics[key]   )   )   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      pl.Trainer    ,     a					:      pl.LightningModule   ):






							'''simple docstring'''







							rank_zero_info('***** Test results *****'   )
							lowerCAmelCase__      :      Any         =		trainer.callback_metrics
							# Log and save results to file
							lowerCAmelCase__      :      Optional[int]         =		os.path.join(pl_module.hparams.output_dir    ,     'test_results.txt'   )
							with open(a    ,     'w'   ) as writer:
										for key in sorted(a   ):
													if key not in ["log", "progress_bar"]:
																rank_zero_info('{} = {}\n'.format(a    ,     str(metrics[key]   )   )   )
																writer.write('{} = {}\n'.format(a    ,     str(metrics[key]   )   )   )






def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		None:
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    '--output_dir'       ,							default=str(Path(SCREAMING_SNAKE_CASE_     ).parent / 'test_run' / 'model_checkpoints'     )       ,							type=SCREAMING_SNAKE_CASE_       ,							help='The output directory where the model predictions and checkpoints will be written.'       ,							)
			parser.add_argument(
			    '--fp16'       ,							action='store_true'       ,							help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'       ,							)

			parser.add_argument(
			    '--fp16_opt_level'       ,							type=SCREAMING_SNAKE_CASE_       ,							default='O2'       ,							help=(
			        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
			        'See details at https://nvidia.github.io/apex/amp.html'
			    )       ,							)
			parser.add_argument('--n_tpu_cores'       ,							dest='tpu_cores'       ,							type=SCREAMING_SNAKE_CASE_     )
			parser.add_argument('--max_grad_norm'       ,							dest='gradient_clip_val'       ,							default=1.0       ,							type=SCREAMING_SNAKE_CASE_       ,							help='Max gradient norm'     )
			parser.add_argument('--do_train'       ,							action='store_true'       ,							help='Whether to run training.'     )
			parser.add_argument('--do_predict'       ,							action='store_true'       ,							help='Whether to run predictions on the test set.'     )
			parser.add_argument(
			    '--gradient_accumulation_steps'       ,							dest='accumulate_grad_batches'       ,							type=SCREAMING_SNAKE_CASE_       ,							default=1       ,							help='Number of updates steps to accumulate before performing a backward/update pass.'       ,							)
			parser.add_argument('--seed'       ,							type=SCREAMING_SNAKE_CASE_       ,							default=42       ,							help='random seed for initialization'     )
			parser.add_argument(
			    '--data_dir'       ,							default=str(Path(SCREAMING_SNAKE_CASE_     ).parent / 'test_run' / 'dummy-train-data'     )       ,							type=SCREAMING_SNAKE_CASE_       ,							help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'       ,							)







def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_=[]       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							**SCREAMING_SNAKE_CASE_       ,							)				->		Optional[Any]:
			pl.seed_everything(args.seed     )

			# init model
			lowerCAmelCase__      :      Optional[Any]         =		Path(model.hparams.output_dir     )
			odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )

			# add custom checkpoints
			if checkpoint_callback is None:
						lowerCAmelCase__      :      Optional[int]         =		pl.callbacks.ModelCheckpoint(
						    filepath=args.output_dir       ,							prefix='checkpoint'       ,							monitor='val_loss'       ,							mode='min'       ,							save_top_k=1     )
			if early_stopping_callback:
						extra_callbacks.append(SCREAMING_SNAKE_CASE_     )
			if logging_callback is None:
						lowerCAmelCase__      :      List[Any]         =		LoggingCallback()

			lowerCAmelCase__      :      Any         =		{}

			if args.fpaa:
						lowerCAmelCase__      :      Tuple         =		16

			if args.gpus > 1:
						lowerCAmelCase__      :      Tuple         =		'auto'
						lowerCAmelCase__      :      List[str]         =		'ddp'

			lowerCAmelCase__      :      Optional[int]         =		args.accumulate_grad_batches
			lowerCAmelCase__      :      Union[str, Any]         =		None
			lowerCAmelCase__      :      Optional[Any]         =		'auto'

			lowerCAmelCase__      :      str         =		pl.Trainer.from_argparse_args(
			    SCREAMING_SNAKE_CASE_       ,							weights_summary=SCREAMING_SNAKE_CASE_       ,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]       ,							logger=SCREAMING_SNAKE_CASE_       ,							val_check_interval=1       ,							num_sanity_val_steps=2       ,							**SCREAMING_SNAKE_CASE_       ,							)

			if args.do_train:
						trainer.fit(SCREAMING_SNAKE_CASE_     )

			else:
						print('RAG modeling tests with new set functions successfuly executed!'     )
			return trainer

import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__          =		get_tests_dir("""fixtures/spiece.model""")







@require_sentencepiece
@require_tokenizers
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  AlbertTokenizer
				lowercase			    =  AlbertTokenizerFast
				lowercase			    =  True
				lowercase			    =  True
				lowercase			    =  True






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__      :      int         =		AlbertTokenizer(a   )
							tokenizer.save_pretrained(self.tmpdirname   )






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		'this is a test'
							lowerCAmelCase__      :      List[Any]         =		'this is a test'
							return input_text, output_text






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		'<pad>'
							lowerCAmelCase__      :      Optional[Any]         =		0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(a   )    ,     a   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(a   )    ,     a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0]    ,     '<pad>'   )
							self.assertEqual(vocab_keys[1]    ,     '<unk>'   )
							self.assertEqual(vocab_keys[-1]    ,     '▁eloquent'   )
							self.assertEqual(len(a   )    ,     30_000   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							self.assertEqual(self.get_tokenizer().vocab_size    ,     30_000   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							if not self.test_rust_tokenizer:
										return

							lowerCAmelCase__      :      str         =		self.get_tokenizer()
							lowerCAmelCase__      :      str         =		self.get_rust_tokenizer()

							lowerCAmelCase__      :      List[Any]         =		'I was born in 92000, and this is falsé.'

							lowerCAmelCase__      :      str         =		tokenizer.tokenize(a   )
							lowerCAmelCase__      :      Optional[int]         =		rust_tokenizer.tokenize(a   )
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      Tuple         =		tokenizer.encode(a    ,     add_special_tokens=a   )
							lowerCAmelCase__      :      Union[str, Any]         =		rust_tokenizer.encode(a    ,     add_special_tokens=a   )
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      Optional[Any]         =		self.get_rust_tokenizer()
							lowerCAmelCase__      :      Dict         =		tokenizer.encode(a   )
							lowerCAmelCase__      :      List[Any]         =		rust_tokenizer.encode(a   )
							self.assertListEqual(a    ,     a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		AlbertTokenizer(a    ,     keep_accents=a   )

							lowerCAmelCase__      :      Union[str, Any]         =		tokenizer.tokenize('This is a test'   )
							self.assertListEqual(a    ,     ['▁this', '▁is', '▁a', '▁test']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(a   )    ,     [48, 25, 21, 1_289]   )

							lowerCAmelCase__      :      Tuple         =		tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
							self.assertListEqual(
							    a    ,     ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']   )
							lowerCAmelCase__      :      Any         =		tokenizer.convert_tokens_to_ids(a   )
							self.assertListEqual(a    ,     [31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]   )

							lowerCAmelCase__      :      Any         =		tokenizer.convert_ids_to_tokens(a   )
							self.assertListEqual(
							    a    ,     ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']    ,     )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		AlbertTokenizer(a   )

							lowerCAmelCase__      :      Tuple         =		tokenizer.encode('sequence builders'   )
							lowerCAmelCase__      :      Any         =		tokenizer.encode('multi-sequence build'   )

							lowerCAmelCase__      :      Dict         =		tokenizer.build_inputs_with_special_tokens(a   )
							lowerCAmelCase__      :      Tuple         =		tokenizer.build_inputs_with_special_tokens(a    ,     a   )

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
							    tokenizer.sep_token_id
							]






				@slow
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		{'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=a    ,     model_name='albert-base-v2'    ,     revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'    ,     )