





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


lowercase__						:      int	=    logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowercase__						:      Optional[int]	=    {
    '''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
lowercase__						:      int	=    {
    '''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
}
lowercase__						:      Optional[Any]	=    sorted(arg_to_scheduler.keys())
lowercase__						:      List[str]	=    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class     __lowerCAmelCase					(   pl.LightningModule			):






       """simple docstring"""





       def __init__(							self       :		List[str]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Tuple=None	,			lowerCAmelCase__       :		Union[str, Any]="base"	,			lowerCAmelCase__       :		int=None	,			lowerCAmelCase__       :		Optional[Any]=None	,			lowerCAmelCase__       :		List[str]=None	,			**lowerCAmelCase__       :		Union[str, Any]	,			)   ->					Dict:

            '''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(lowerCAmelCase__					)
            _UpperCamelCase       =	0
            _UpperCamelCase       =	Path(self.hparams.output_dir					)
            _UpperCamelCase       =	self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 _UpperCamelCase       =	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=lowerCAmelCase__	,			**lowerCAmelCase__	,			)
            else:
                 _UpperCamelCase       =	config

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

            if tokenizer is None:
                 _UpperCamelCase       =	AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,			cache_dir=lowerCAmelCase__	,			)
            else:
                 _UpperCamelCase       =	tokenizer
            _UpperCamelCase       =	MODEL_MODES[mode]
            if model is None:
                 _UpperCamelCase       =	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=lowerCAmelCase__	,			)
            else:
                 _UpperCamelCase       =	model





       def snake_case__						(							self       :		Optional[Any]	,			*lowerCAmelCase__       :		Any	,			**lowerCAmelCase__       :		List[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_type.from_pretrained(*lowerCAmelCase__	,			**lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	arg_to_scheduler[self.hparams.lr_scheduler]
            _UpperCamelCase       =	get_schedule_func(
                self.opt	,			num_warmup_steps=self.hparams.warmup_steps	,			num_training_steps=self.total_steps()					)
            _UpperCamelCase       =	{"scheduler": scheduler, "interval": "step", "frequency": 1}
            return scheduler





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model
            _UpperCamelCase       =	["bias", "LayerNorm.weight"]
            _UpperCamelCase       =	[
                {
                    "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:
                 _UpperCamelCase       =	Adafactor(
                     lowerCAmelCase__	,			lr=self.hparams.learning_rate	,			scale_parameter=lowerCAmelCase__	,			relative_step=lowerCAmelCase__					)

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

            _UpperCamelCase       =	self.get_lr_scheduler()

            return [optimizer], [scheduler]





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            return self.validation_step(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					str:

            '''simple docstring'''




            return self.validation_end(lowerCAmelCase__					)





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

            '''simple docstring'''




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





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




            if stage == "test":
                 _UpperCamelCase       =	len(self.test_dataloader().dataset					)
            else:
                 _UpperCamelCase       =	self.get_dataloader('''train'''	,			self.hparams.train_batch_size	,			shuffle=lowerCAmelCase__					)
                 _UpperCamelCase       =	len(self.train_dataloader().dataset					)





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Dict = False					)   ->					List[str]:

            '''simple docstring'''




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





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

            '''simple docstring'''




            return self.train_loader





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

            '''simple docstring'''




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





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

            '''simple docstring'''




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





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




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





       @pl.utilities.rank_zero_only
       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		List[Any]					)   ->					None:

            '''simple docstring'''




            _UpperCamelCase       =	self.output_dir.joinpath('''best_tfmr'''					)
            _UpperCamelCase       =	self.step_count
            self.model.save_pretrained(lowerCAmelCase__					)
            self.tokenizer.save_pretrained(lowerCAmelCase__					)







       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




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

class     __lowerCAmelCase					(   pl.Callback			):






       """simple docstring"""





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		str					)   ->					Optional[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     __lowerCAmelCase					(   pl.Callback			):






       """simple docstring"""





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					List[Any]:

            '''simple docstring'''




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

class     __lowerCAmelCase					(   pl.Callback			):






       """simple docstring"""





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Optional[int]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	trainer.lr_schedulers[0]["scheduler"]
            _UpperCamelCase       =	{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
            pl_module.logger.log_metrics(lowerCAmelCase__					)





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Tuple					)   ->					Optional[int]:

            '''simple docstring'''




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







       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Dict					)   ->					List[str]:

            '''simple docstring'''




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







def  a__    (    lowercase :					Tuple, lowercase :					Tuple				)		->		None:





     """simple docstring"""

     parser.add_argument(
         '''--output_dir''', default=str(Path(_lowercase				).parent / '''test_run''' / '''model_checkpoints'''				), type=_lowercase, 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=_lowercase, 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=_lowercase				)
     parser.add_argument('''--max_grad_norm''', dest='''gradient_clip_val''', default=1.0, type=_lowercase, 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=_lowercase, default=1, help='''Number of updates steps to accumulate before performing a backward/update pass.''', )
     parser.add_argument('''--seed''', type=_lowercase, default=42, help='''random seed for initialization'''				)
     parser.add_argument(
         '''--data_dir''', default=str(Path(_lowercase				).parent / '''test_run''' / '''dummy-train-data'''				), type=_lowercase, help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''', )







def  a__    (    lowercase :					BaseTransformer, lowercase :					argparse.Namespace, lowercase :					Tuple=None, lowercase :					int=True, lowercase :					List[str]=[], lowercase :					Union[str, Any]=None, lowercase :					Any=None, **lowercase :					Any, )		->		Tuple:





     """simple docstring"""

     pl.seed_everything(args.seed				)

     # init model
     _UpperCamelCase       =	Path(model.hparams.output_dir				)
     odir.mkdir(exist_ok=_lowercase				)

     # add custom checkpoints
     if checkpoint_callback is None:
          _UpperCamelCase       =	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(_lowercase				)
     if logging_callback is None:
          _UpperCamelCase       =	LoggingCallback()

     _UpperCamelCase       =	{}

     if args.fpaa:
          _UpperCamelCase       =	16

     if args.gpus > 1:
          _UpperCamelCase       =	"auto"
          _UpperCamelCase       =	"ddp"

     _UpperCamelCase       =	args.accumulate_grad_batches
     _UpperCamelCase       =	None
     _UpperCamelCase       =	"auto"

     _UpperCamelCase       =	pl.Trainer.from_argparse_args(
         _lowercase, weights_summary=_lowercase, callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback], logger=_lowercase, val_check_interval=1, num_sanity_val_steps=2, **_lowercase, )

     if args.do_train:
          trainer.fit(_lowercase				)

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










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





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

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

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





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

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





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

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''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, 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, 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]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



