



"""simple docstring"""

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


A_						:    Dict				=      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A_						:    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
A_						:    List[Any]				=      {
    '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
}
A_						:    Dict				=      sorted(arg_to_scheduler.keys())
A_						:    Dict				=      '{' + ', '.join(arg_to_scheduler_choices) + '}'
class     lowerCAmelCase__						(	pl.LightningModule ):


	'''simple docstring'''







	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       argparse.Namespace     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       _SCREAMING_SNAKE_CASE				:       int="base"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=None     ,       _SCREAMING_SNAKE_CASE				:       Any=None     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       **_SCREAMING_SNAKE_CASE				:       int     ,       )						->	Any:





					"""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(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     0
					SCREAMING_SNAKE_CASE						:							List[Any]							=     Path(self.hparams.output_dir    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.hparams.cache_dir if self.hparams.cache_dir else None
					if config is None:
									SCREAMING_SNAKE_CASE						:							Any							=     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=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )
					else:
									SCREAMING_SNAKE_CASE						:							PretrainedConfig							=     config

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

					if tokenizer is None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     AutoTokenizer.from_pretrained(
									    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,       cache_dir=_SCREAMING_SNAKE_CASE     ,       )
					else:
									SCREAMING_SNAKE_CASE						:							PreTrainedTokenizer							=     tokenizer
					SCREAMING_SNAKE_CASE						:							List[Any]							=     MODEL_MODES[mode]
					if model is None:
									SCREAMING_SNAKE_CASE						:							Optional[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=_SCREAMING_SNAKE_CASE     ,       )
					else:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     model





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       *_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.model
					SCREAMING_SNAKE_CASE						:							int							=     ['bias', 'LayerNorm.weight']
					SCREAMING_SNAKE_CASE						:							Dict							=     [
					    {
					        '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:
									SCREAMING_SNAKE_CASE						:							Tuple							=     Adafactor(
									    _SCREAMING_SNAKE_CASE     ,       lr=self.hparams.learning_rate     ,       scale_parameter=_SCREAMING_SNAKE_CASE     ,       relative_step=_SCREAMING_SNAKE_CASE    )

					else:
									SCREAMING_SNAKE_CASE						:							Any							=     AdamW(
									    _SCREAMING_SNAKE_CASE     ,       lr=self.hparams.learning_rate     ,       eps=self.hparams.adam_epsilon    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     optimizer

					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_lr_scheduler()

					return [optimizer], [scheduler]





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	Any:





					"""simple docstring"""


					return self.validation_step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					return self.validation_end(_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     max(1     ,       self.hparams.gpus    )  # TODO: consider num_tpu_cores
					SCREAMING_SNAKE_CASE						:							Dict							=     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[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	str:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	Union[str, Any]:





					"""simple docstring"""


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





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





					"""simple docstring"""


					return self.train_loader





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Tuple:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Any    )						->	int:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]    )						->	int:





					"""simple docstring"""


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





	@pl.utilities.rank_zero_only
	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, Any]    )						->	None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     self.output_dir.joinpath('best_tfmr'    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.step_count
					self.model.save_pretrained(_SCREAMING_SNAKE_CASE    )
					self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )







	@staticmethod
	def 			_lowerCAmelCase     (      _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	List[Any]:





					"""simple docstring"""


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




class     lowerCAmelCase__						(	pl.Callback ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	str:





					"""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 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	Tuple:





					"""simple docstring"""


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




class     lowerCAmelCase__						(	pl.Callback ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	Tuple:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       pl.Trainer     ,       _SCREAMING_SNAKE_CASE				:       pl.LightningModule    )						->	Any:





					"""simple docstring"""


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







	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       pl.Trainer     ,       _SCREAMING_SNAKE_CASE				:       pl.LightningModule    )						->	List[str]:





					"""simple docstring"""


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




def  __snake_case							(	__A      :						Dict							,		__A      :						Optional[int]     )     ->       None:






				'''simple docstring'''
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '--output_dir'							,		default=str(Path(__A     ).parent / 'test_run' / 'model_checkpoints'     )							,		type=__A							,		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=__A							,		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=__A     )
				parser.add_argument('--max_grad_norm'							,		dest='gradient_clip_val'							,		default=1.0							,		type=__A							,		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=__A							,		default=1							,		help='Number of updates steps to accumulate before performing a backward/update pass.'							,		)
				parser.add_argument('--seed'							,		type=__A							,		default=42							,		help='random seed for initialization'     )
				parser.add_argument(
				    '--data_dir'							,		default=str(Path(__A     ).parent / 'test_run' / 'dummy-train-data'     )							,		type=__A							,		help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'							,		)







def  __snake_case							(	__A      :						BaseTransformer							,		__A      :						argparse.Namespace							,		__A      :						str=None							,		__A      :						int=True							,		__A      :						str=[]							,		__A      :						Any=None							,		__A      :						Optional[int]=None							,		**__A      :						Union[str, Any]							,		)     ->       Optional[int]:






				'''simple docstring'''
				pl.seed_everything(args.seed     )

				# init model
				SCREAMING_SNAKE_CASE						:							List[Any]							=     Path(model.hparams.output_dir     )
				odir.mkdir(exist_ok=__A     )

				# add custom checkpoints
				if checkpoint_callback is None:
								SCREAMING_SNAKE_CASE						:							Tuple							=     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(__A     )
				if logging_callback is None:
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     LoggingCallback()

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     {}

				if args.fpaa:
								SCREAMING_SNAKE_CASE						:							int							=     16

				if args.gpus > 1:
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'auto'
								SCREAMING_SNAKE_CASE						:							str							=     'ddp'

				SCREAMING_SNAKE_CASE						:							str							=     args.accumulate_grad_batches
				SCREAMING_SNAKE_CASE						:							Dict							=     None
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     'auto'

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     pl.Trainer.from_argparse_args(
				    __A							,		weights_summary=__A							,		callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]							,		logger=__A							,		val_check_interval=1							,		num_sanity_val_steps=2							,		**__A							,		)

				if args.do_train:
								trainer.fit(__A     )

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










"""simple docstring"""

from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_						:    Tuple				=      logging.get_logger(__name__)


A_						:    int				=      {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
A_						:    List[Any]				=      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A_						:    Optional[Any]				=      0

A_						:    int				=      0xE000
A_						:    Any				=      0xE001
A_						:    List[Any]				=      0xE002
A_						:    Optional[Any]				=      0xE003
A_						:    int				=      0xE004

# Maps special codepoints to human-readable names.
A_						:    Dict[int, str]				=      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
A_						:    Dict[str, int]				=      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Tuple=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       int=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Any=chr(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=False     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=2_048     ,       **_SCREAMING_SNAKE_CASE				:       Tuple     ,       )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else bos_token
					SCREAMING_SNAKE_CASE						:							Tuple							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else eos_token
					SCREAMING_SNAKE_CASE						:							Any							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else sep_token
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else cls_token
					SCREAMING_SNAKE_CASE						:							Dict							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					SCREAMING_SNAKE_CASE						:							int							=     AddedToken(_SCREAMING_SNAKE_CASE     ,       lstrip=_SCREAMING_SNAKE_CASE     ,       rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else mask_token

					super().__init__(
					    bos_token=_SCREAMING_SNAKE_CASE     ,       eos_token=_SCREAMING_SNAKE_CASE     ,       sep_token=_SCREAMING_SNAKE_CASE     ,       cls_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       mask_token=_SCREAMING_SNAKE_CASE     ,       add_prefix_space=_SCREAMING_SNAKE_CASE     ,       model_max_length=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

					# Creates a mapping for looking up the IDs of special symbols.
					SCREAMING_SNAKE_CASE						:							Dict[str, int]							=     {}
					for codepoint, name in SPECIAL_CODEPOINTS.items():
									SCREAMING_SNAKE_CASE						:							str							=     codepoint

					# Creates a mapping for looking up the string forms of special symbol IDs.
					SCREAMING_SNAKE_CASE						:							Dict[int, str]							=     {
					    codepoint: name for name, codepoint in self._special_codepoints.items()
					}

					SCREAMING_SNAKE_CASE						:							Dict							=     UNICODE_VOCAB_SIZE
					SCREAMING_SNAKE_CASE						:							List[str]							=     len(self._special_codepoints    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	int:





					"""simple docstring"""


					return self._unicode_vocab_size





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





					"""simple docstring"""


					return list(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	int:





					"""simple docstring"""


					try:
									return ord(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid token: '{token}'"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	str:





					"""simple docstring"""


					try:
									if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
									return chr(_SCREAMING_SNAKE_CASE    )
					except TypeError:
									raise ValueError(f"""invalid id: {index}"""    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	List[str]:





					"""simple docstring"""


					return "".join(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Tuple							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Any							=     cls + token_ids_a + sep
					if token_ids_a is not None:
									result += token_ids_a + sep
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False    )						->	List[int]:





					"""simple docstring"""


					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_SCREAMING_SNAKE_CASE     ,       token_ids_a=_SCREAMING_SNAKE_CASE     ,       already_has_special_tokens=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							int							=     [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					if token_ids_a is not None:
									result += ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
					return result





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[int]] = None    )						->	List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [self.sep_token_id]
					SCREAMING_SNAKE_CASE						:							Dict							=     [self.cls_token_id]

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     len(cls + token_ids_a + sep    ) * [0]
					if token_ids_a is not None:
									result += len(token_ids_a + sep    ) * [1]
					return result







	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	Any:





					"""simple docstring"""


					return ()





