





'''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


_lowerCamelCase					:						str					     =							logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_lowerCamelCase					:						List[str]					     =							{
    '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					:						Optional[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
}
_lowerCamelCase					:						List[str]					     =							sorted(arg_to_scheduler.keys())
_lowerCamelCase					:						List[str]					     =							'{' + ', '.join(arg_to_scheduler_choices) + '}'



class 				__UpperCAmelCase	(							pl.LightningModule    ):







		'''simple docstring'''







		def __init__(self    :   List[Any]   ,   _lowerCAmelCase    :   argparse.Namespace   ,   _lowerCAmelCase    :   Dict=None   ,   _lowerCAmelCase    :   Dict="base"   ,   _lowerCAmelCase    :   List[Any]=None   ,   _lowerCAmelCase    :   Optional[Any]=None   ,   _lowerCAmelCase    :   Dict=None   ,   **_lowerCAmelCase    :   Union[str, Any]   ,   ):
			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   )
			A					=   0
			A					=   Path(self.hparams.output_dir   )
			A					=   self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
				A					=   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:
				A					=   config

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







		def 							A  (self    :   Optional[int]   ,   *_lowerCAmelCase    :   Union[str, Any]   ,   **_lowerCAmelCase    :   List[str]   ):
			A					=   self.model_type.from_pretrained(*_lowerCAmelCase   ,   **_lowerCAmelCase   )







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







		def 							A  (self    :   Optional[Any]   ):
			A					=   self.model
			A					=   ["""bias""", """LayerNorm.weight"""]
			A					=   [
			    {
			        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
			        """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:
				A					=   Adafactor(
				    _lowerCAmelCase   ,   lr=self.hparams.learning_rate   ,   scale_parameter=_lowerCAmelCase   ,   relative_step=_lowerCAmelCase   )

			else:
				A					=   AdamW(
				    _lowerCAmelCase   ,   lr=self.hparams.learning_rate   ,   eps=self.hparams.adam_epsilon   )
			A					=   optimizer

			A					=   self.get_lr_scheduler()

			return [optimizer], [scheduler]







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   int   ):
			return self.validation_step(_lowerCAmelCase   ,   _lowerCAmelCase   )







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			return self.validation_end(_lowerCAmelCase   )







		def 							A  (self    :   List[Any]   ):
			A					=   max(1   ,   self.hparams.gpus   )  # TODO: consider num_tpu_cores
			A					=   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
			return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Any   ):
			if mode == "test":
				A					=   len(self.test_dataloader().dataset   )
			else:
				A					=   self.get_dataloader("""train"""   ,   self.hparams.train_batch_size   ,   shuffle=_lowerCAmelCase   )
				A					=   len(self.train_dataloader().dataset   )







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   bool = False   ):
			raise NotImplementedError("""You must implement this for your task"""   )







		def 							A  (self    :   Optional[Any]   ):
			return self.train_loader







		def 							A  (self    :   List[Any]   ):
			return self.get_dataloader("""dev"""   ,   self.hparams.eval_batch_size   ,   shuffle=_lowerCAmelCase   )







		def 							A  (self    :   Tuple   ):
			return self.get_dataloader("""test"""   ,   self.hparams.eval_batch_size   ,   shuffle=_lowerCAmelCase   )







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   List[str]   ):
			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 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   Dict[str, Any]   ):
			A					=   self.output_dir.joinpath("""best_tfmr"""   )
			A					=   self.step_count
			self.model.save_pretrained(_lowerCAmelCase   )
			self.tokenizer.save_pretrained(_lowerCAmelCase   )







		@staticmethod
		def 							A  (_lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   int   ):
			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=""""""   ,   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 				__UpperCAmelCase	(							pl.Callback    ):







		'''simple docstring'''







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Any   ):
			A					=   trainer.lr_schedulers[0]["""scheduler"""]
			A					=   {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
			pl_module.logger.log_metrics(_lowerCAmelCase   )







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   pl.Trainer   ,   _lowerCAmelCase    :   pl.LightningModule   ):
			rank_zero_info("""***** Validation results *****"""   )
			A					=   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 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   pl.Trainer   ,   _lowerCAmelCase    :   pl.LightningModule   ):
			rank_zero_info("""***** Test results *****"""   )
			A					=   trainer.callback_metrics
			# Log and save results to file
			A					=   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			(  UpperCAmelCase							,      UpperCAmelCase     )   ->None:



	"""simple docstring"""







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




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=None							,      UpperCAmelCase=True							,      UpperCAmelCase=[]							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      **UpperCAmelCase							,      )   ->int:



	"""simple docstring"""







	pl.seed_everything(args.seed     )

	# init model
	A					=   Path(model.hparams.output_dir     )
	odir.mkdir(exist_ok=UpperCAmelCase     )

	# add custom checkpoints
	if checkpoint_callback is None:
		A					=   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(UpperCAmelCase     )
	if logging_callback is None:
		A					=   LoggingCallback()

	A					=   {}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		A					=   16
		A					=   args.fpaa_opt_level

	if args.gpus > 1:
		A					=   """ddp"""

	A					=   args.accumulate_grad_batches
	A					=   extra_train_kwargs.get("""accelerator"""							,      UpperCAmelCase     )
	A					=   extra_train_kwargs.get("""profiler"""							,      UpperCAmelCase     )

	A					=   pl.Trainer.from_argparse_args(
	    UpperCAmelCase							,      weights_summary=UpperCAmelCase							,      callbacks=[logging_callback] + extra_callbacks							,      logger=UpperCAmelCase							,      checkpoint_callback=UpperCAmelCase							,      **UpperCAmelCase							,      )

	if args.do_train:
		trainer.fit(UpperCAmelCase     )

	return trainer








'''simple docstring'''


import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						Dict					     =							{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCamelCase					:						str					     =							{
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

_lowerCamelCase					:						Union[str, Any]					     =							{
    'openai-gpt': 512,
}




def  __a			(  UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   text.strip()
	if not text:
		return []
	A					=   text.split()
	return tokens



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Optional[int]   ,   _lowerCAmelCase    :   int=True   ,   _lowerCAmelCase    :   List[str]=None   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   int=None   ,   _lowerCAmelCase    :   Optional[Any]=True   ,   ):
			if never_split is None:
				A					=   []
			A					=   do_lower_case
			A					=   set(_lowerCAmelCase   )
			A					=   tokenize_chinese_chars
			A					=   strip_accents
			A					=   do_split_on_punc







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Any=None   ):
			A					=   self.never_split.union(set(_lowerCAmelCase   )   ) if never_split else self.never_split
			A					=   self._clean_text(_lowerCAmelCase   )

			# This was added on November 1st, 2018 for the multilingual and Chinese
			# models. This is also applied to the English models now, but it doesn't
			# matter since the English models were not trained on any Chinese data
			# and generally don't have any Chinese data in them (there are Chinese
			# characters in the vocabulary because Wikipedia does have some Chinese
			# words in the English Wikipedia.).
			if self.tokenize_chinese_chars:
				A					=   self._tokenize_chinese_chars(_lowerCAmelCase   )
			# prevents treating the same character with different unicode codepoints as different characters
			A					=   unicodedata.normalize("""NFC"""   ,   _lowerCAmelCase   )
			A					=   whitespace_tokenize(_lowerCAmelCase   )
			A					=   []
			for token in orig_tokens:
				if token not in never_split:
					if self.do_lower_case:
						A					=   token.lower()
						if self.strip_accents is not False:
							A					=   self._run_strip_accents(_lowerCAmelCase   )
					elif self.strip_accents:
						A					=   self._run_strip_accents(_lowerCAmelCase   )
				split_tokens.extend(self._run_split_on_punc(_lowerCAmelCase   ,   _lowerCAmelCase   )   )

			A					=   whitespace_tokenize(""" """.join(_lowerCAmelCase   )   )
			return output_tokens







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A					=   unicodedata.normalize("""NFD"""   ,   _lowerCAmelCase   )
			A					=   []
			for char in text:
				A					=   unicodedata.category(_lowerCAmelCase   )
				if cat == "Mn":
					continue
				output.append(_lowerCAmelCase   )
			return "".join(_lowerCAmelCase   )







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[str]=None   ):
			if not self.do_split_on_punc or (never_split is not None and text in never_split):
				return [text]
			A					=   list(_lowerCAmelCase   )
			A					=   0
			A					=   True
			A					=   []
			while i < len(_lowerCAmelCase   ):
				A					=   chars[i]
				if _is_punctuation(_lowerCAmelCase   ):
					output.append([char]   )
					A					=   True
				else:
					if start_new_word:
						output.append([]   )
					A					=   False
					output[-1].append(_lowerCAmelCase   )
				i += 1

			return ["".join(_lowerCAmelCase   ) for x in output]







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Any   ):
			A					=   []
			for char in text:
				A					=   ord(_lowerCAmelCase   )
				if self._is_chinese_char(_lowerCAmelCase   ):
					output.append(""" """   )
					output.append(_lowerCAmelCase   )
					output.append(""" """   )
				else:
					output.append(_lowerCAmelCase   )
			return "".join(_lowerCAmelCase   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   Any   ):
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4_e00 and cp <= 0X9_fff)
			    or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
			    or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
			    or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
			    or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
			    or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
			    or (cp >= 0Xf_900 and cp <= 0Xf_aff)
			    or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
			):  #
				return True

			return False







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Any   ):
			A					=   []
			for char in text:
				A					=   ord(_lowerCAmelCase   )
				if cp == 0 or cp == 0Xf_ffd or _is_control(_lowerCAmelCase   ):
					continue
				if _is_whitespace(_lowerCAmelCase   ):
					output.append(""" """   )
				else:
					output.append(_lowerCAmelCase   )
			return "".join(_lowerCAmelCase   )






def  __a			(  UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   set()
	A					=   word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		A					=   char
	return pairs




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



	"""simple docstring"""







	A					=   text.replace("""—"""							,      """-"""     )
	A					=   text.replace("""–"""							,      """-"""     )
	A					=   text.replace("""―"""							,      """-"""     )
	A					=   text.replace("""…"""							,      """..."""     )
	A					=   text.replace("""´"""							,      """'"""     )
	A					=   re.sub(R"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"""							,      R""" \1 """							,      UpperCAmelCase     )
	A					=   re.sub(R"""\s*\n\s*"""							,      """ \n """							,      UpperCAmelCase     )
	A					=   re.sub(R"""[^\S\n]+"""							,      """ """							,      UpperCAmelCase     )
	return text.strip()



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     VOCAB_FILES_NAMES
		__lowerCAmelCase								=     PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase								=     ['''input_ids''', '''attention_mask''']







		def __init__(self    :   Dict   ,   _lowerCAmelCase    :   Optional[int]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Dict="<unk>"   ,   **_lowerCAmelCase    :   Dict   ):
			super().__init__(unk_token=_lowerCAmelCase   ,   **_lowerCAmelCase   )

			try:
				import ftfy
				from spacy.lang.en import English

				A					=   English()
				A					=   _nlp.tokenizer
				A					=   ftfy.fix_text
			except ImportError:
				logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""   )
				A					=   BasicTokenizer(do_lower_case=_lowerCAmelCase   )
				A					=   None

			with open(_lowerCAmelCase   ,   encoding="""utf-8"""   ) as vocab_handle:
				A					=   json.load(_lowerCAmelCase   )
			A					=   {v: k for k, v in self.encoder.items()}
			with open(_lowerCAmelCase   ,   encoding="""utf-8"""   ) as merges_handle:
				A					=   merges_handle.read().split("""\n"""   )[1:-1]
			A					=   [tuple(merge.split()   ) for merge in merges]
			A					=   dict(zip(_lowerCAmelCase   ,   range(len(_lowerCAmelCase   )   )   )   )
			A					=   {}







		@property
		def 							A  (self    :   Dict   ):
			return True







		@property
		def 							A  (self    :   Dict   ):
			return len(self.encoder   )







		def 							A  (self    :   str   ):
			return dict(self.encoder   ,   **self.added_tokens_encoder   )







		def 							A  (self    :   Optional[Any]   ,   _lowerCAmelCase    :   List[Any]   ):
			A					=   tuple(token[:-1]   ) + (token[-1] + """</w>""",)
			if token in self.cache:
				return self.cache[token]
			A					=   get_pairs(_lowerCAmelCase   )

			if not pairs:
				return token + "</w>"

			while True:
				A					=   min(_lowerCAmelCase   ,   key=lambda _lowerCAmelCase   : self.bpe_ranks.get(_lowerCAmelCase   ,   float("""inf"""   )   )   )
				if bigram not in self.bpe_ranks:
					break
				A	,     A					=   bigram
				A					=   []
				A					=   0
				while i < len(_lowerCAmelCase   ):
					try:
						A					=   word.index(_lowerCAmelCase   ,   _lowerCAmelCase   )
					except ValueError:
						new_word.extend(word[i:]   )
						break
					else:
						new_word.extend(word[i:j]   )
						A					=   j

					if word[i] == first and i < len(_lowerCAmelCase   ) - 1 and word[i + 1] == second:
						new_word.append(first + second   )
						i += 2
					else:
						new_word.append(word[i]   )
						i += 1
				A					=   tuple(_lowerCAmelCase   )
				A					=   new_word
				if len(_lowerCAmelCase   ) == 1:
					break
				else:
					A					=   get_pairs(_lowerCAmelCase   )
			A					=   """ """.join(_lowerCAmelCase   )
			if word == "\n  </w>":
				A					=   """\n</w>"""
			A					=   word
			return word







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A					=   []
			if self.fix_text is None:
				# Using BERT's BasicTokenizer
				A					=   self.nlp.tokenize(_lowerCAmelCase   )
				for token in text:
					split_tokens.extend(list(self.bpe(_lowerCAmelCase   ).split(""" """   )   )   )
			else:
				# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
				A					=   self.nlp(text_standardize(self.fix_text(_lowerCAmelCase   )   )   )
				for token in text:
					split_tokens.extend(list(self.bpe(token.text.lower()   ).split(""" """   )   )   )
			return split_tokens







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ):
			return self.encoder.get(_lowerCAmelCase   ,   self.encoder.get(self.unk_token   )   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   Tuple   ):
			return self.decoder.get(_lowerCAmelCase   ,   self.unk_token   )







		def 							A  (self    :   Optional[int]   ,   _lowerCAmelCase    :   int   ):
			A					=   """""".join(_lowerCAmelCase   ).replace("""</w>"""   ,   """ """   ).strip()
			return out_string







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   Optional[str] = None   ):
			if not os.path.isdir(_lowerCAmelCase   ):
				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
				return
			A					=   os.path.join(
			    _lowerCAmelCase   ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
			A					=   os.path.join(
			    _lowerCAmelCase   ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

			with open(_lowerCAmelCase   ,   """w"""   ,   encoding="""utf-8"""   ) as f:
				f.write(json.dumps(self.encoder   ,   indent=2   ,   sort_keys=_lowerCAmelCase   ,   ensure_ascii=_lowerCAmelCase   ) + """\n"""   )

			A					=   0
			with open(_lowerCAmelCase   ,   """w"""   ,   encoding="""utf-8"""   ) as writer:
				writer.write("""#version: 0.2\n"""   )
				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,   key=lambda _lowerCAmelCase   : kv[1]   ):
					if index != token_index:
						logger.warning(
						    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
						    """ Please check that the tokenizer is not corrupted!"""   )
						A					=   token_index
					writer.write(""" """.join(_lowerCAmelCase   ) + """\n"""   )
					index += 1

			return vocab_file, merge_file

