





'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowercase__		:     Union[str, Any]			=							logging.get_logger(__name__)


lowercase__		:     int			=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
lowercase__		:     List[str]			=							{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

lowercase__		:     Any			=							{
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}







@lru_cache()
def    a__							(							)						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			(
         list(range(ord('''!''' ),   ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ),   ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ),   ord('''ÿ''' ) + 1 ) )
     )
     _UpperCamelCase							       =			bs[:]
     _UpperCamelCase							       =			0
     for b in range(2**8 ):
          if b not in bs:
               bs.append(lowercase )
               cs.append(2**8 + n )
               n += 1
     _UpperCamelCase							       =			[chr(lowercase ) for n in cs]
     return dict(zip(lowercase,   lowercase ) )







def    a__							(							lowercase   :	Any )						->						Optional[Any]:





     """simple docstring"""

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




class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       VOCAB_FILES_NAMES
       _snake_case      :	List[Any]									=       PRETRAINED_VOCAB_FILES_MAP
       _snake_case      :	Dict									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case      :	List[Any]									=       ['input_ids', 'attention_mask']





       def __init__(				self  :						List[str]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]="replace"			,					lowerCAmelCase__  :						Optional[int]="<s>"			,					lowerCAmelCase__  :						List[str]="</s>"			,					lowerCAmelCase__  :						Optional[Any]="</s>"			,					lowerCAmelCase__  :						List[str]="<s>"			,					lowerCAmelCase__  :						Union[str, Any]="<unk>"			,					lowerCAmelCase__  :						str="<pad>"			,					lowerCAmelCase__  :						Optional[Any]="<mask>"			,					lowerCAmelCase__  :						int=False			,					**lowerCAmelCase__  :						List[Any]			,					)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__	) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) else bos_token
            _UpperCamelCase							       =			AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__	) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) else eos_token
            _UpperCamelCase							       =			AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__	) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) else sep_token
            _UpperCamelCase							       =			AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__	) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) else cls_token
            _UpperCamelCase							       =			AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__	) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) else unk_token
            _UpperCamelCase							       =			AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__	) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) else pad_token

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

            super().__init__(
                errors=lowerCAmelCase__			,					bos_token=lowerCAmelCase__			,					eos_token=lowerCAmelCase__			,					unk_token=lowerCAmelCase__			,					sep_token=lowerCAmelCase__			,					cls_token=lowerCAmelCase__			,					pad_token=lowerCAmelCase__			,					mask_token=lowerCAmelCase__			,					add_prefix_space=lowerCAmelCase__			,					**lowerCAmelCase__			,					)

            with open(lowerCAmelCase__			,					encoding='''utf-8'''	) as vocab_handle:
                 _UpperCamelCase							       =			json.load(lowerCAmelCase__	)
            _UpperCamelCase							       =			{v: k for k, v in self.encoder.items()}
            _UpperCamelCase							       =			errors  # how to handle errors in decoding
            _UpperCamelCase							       =			bytes_to_unicode()
            _UpperCamelCase							       =			{v: k for k, v in self.byte_encoder.items()}
            with open(lowerCAmelCase__			,					encoding='''utf-8'''	) as merges_handle:
                 _UpperCamelCase							       =			merges_handle.read().split('''\n'''	)[1:-1]
            _UpperCamelCase							       =			[tuple(merge.split()	) for merge in bpe_merges]
            _UpperCamelCase							       =			dict(zip(lowerCAmelCase__			,					range(len(lowerCAmelCase__	)	)	)	)
            _UpperCamelCase							       =			{}
            _UpperCamelCase							       =			add_prefix_space

            # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
            _UpperCamelCase							       =			re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''	)





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

            '''simple docstring'''




            return len(self.encoder	)





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

            '''simple docstring'''




            return dict(self.encoder			,					**self.added_tokens_encoder	)





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

            '''simple docstring'''




            if token in self.cache:
                 return self.cache[token]
            _UpperCamelCase							       =			tuple(lowerCAmelCase__	)
            _UpperCamelCase							       =			get_pairs(lowerCAmelCase__	)

            if not pairs:
                 return token

            while True:
                 _UpperCamelCase							       =			min(lowerCAmelCase__			,					key=lambda lowerCAmelCase__	: self.bpe_ranks.get(lowerCAmelCase__			,					float('''inf'''	)	)	)
                 if bigram not in self.bpe_ranks:
                      break
                 _UpperCamelCase       ,							_UpperCamelCase							       =			bigram
                 _UpperCamelCase							       =			[]
                 _UpperCamelCase							       =			0
                 while i < len(lowerCAmelCase__	):
                      try:
                           _UpperCamelCase							       =			word.index(lowerCAmelCase__			,					lowerCAmelCase__	)
                      except ValueError:
                           new_word.extend(word[i:]	)
                           break
                      else:
                           new_word.extend(word[i:j]	)
                           _UpperCamelCase							       =			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
                 _UpperCamelCase							       =			tuple(lowerCAmelCase__	)
                 _UpperCamelCase							       =			new_word
                 if len(lowerCAmelCase__	) == 1:
                      break
                 else:
                      _UpperCamelCase							       =			get_pairs(lowerCAmelCase__	)
            _UpperCamelCase							       =			''' '''.join(lowerCAmelCase__	)
            _UpperCamelCase							       =			word
            return word





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

            '''simple docstring'''




            _UpperCamelCase							       =			[]
            for token in re.findall(self.pat			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			''''''.join(
                     self.byte_encoder[b] for b in token.encode('''utf-8'''	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__	).split(''' '''	)	)
            return bpe_tokens





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

            '''simple docstring'''




            return self.encoder.get(lowerCAmelCase__			,					self.encoder.get(self.unk_token	)	)





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

            '''simple docstring'''




            return self.decoder.get(lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			''''''.join(lowerCAmelCase__	)
            _UpperCamelCase							       =			bytearray([self.byte_decoder[c] for c in text]	).decode('''utf-8'''			,					errors=self.errors	)
            return text





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Optional[str] = None	)       ->	Tuple[str]:

            '''simple docstring'''




            if not os.path.isdir(lowerCAmelCase__	):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
                 return
            _UpperCamelCase							       =			os.path.join(
                lowerCAmelCase__			,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
            _UpperCamelCase							       =			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'''	)

            _UpperCamelCase							       =			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!'''	)
                           _UpperCamelCase							       =			token_index
                      writer.write(''' '''.join(lowerCAmelCase__	) + '''\n'''	)
                      index += 1

            return vocab_file, merge_file





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

            '''simple docstring'''




            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            _UpperCamelCase							       =			[self.cls_token_id]
            _UpperCamelCase							       =			[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						List[int]			,					lowerCAmelCase__  :						Optional[List[int]] = None			,					lowerCAmelCase__  :						bool = False	)       ->	List[int]:

            '''simple docstring'''




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

            if token_ids_a is None:
                 return [1] + ([0] * len(lowerCAmelCase__	)) + [1]
            return [1] + ([0] * len(lowerCAmelCase__	)) + [1, 1] + ([0] * len(lowerCAmelCase__	)) + [1]





       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						List[int]			,					lowerCAmelCase__  :						Optional[List[int]] = None	)       ->	List[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			[self.sep_token_id]
            _UpperCamelCase							       =			[self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep	) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Tuple=False			,					**lowerCAmelCase__  :						Optional[Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			kwargs.pop('''add_prefix_space'''			,					self.add_prefix_space	)
            if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__	) > 0 and not text[0].isspace()):
                 _UpperCamelCase							       =			''' ''' + text
            return (text, kwargs)







'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = True   ,   __lowercase = False    ):

  """simple docstring"""







  __A	:		List[str]	  =      scheduler
  __A	:		Dict	  =      optimizers if isinstance(__lowercase   ,   (list, tuple)    ) else [optimizers]
  __A	:		List[Any]	  =      split_batches
  __A	:		Any	  =      step_with_optimizer
  __A	:		List[Any]	  =      GradientState()


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  if not self.step_with_optimizer:
   # No link between scheduler and optimizer -> just step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
   return

  # Otherwise, first make sure the optimizer was stepped.
  if not self.gradient_state.sync_gradients:
   if self.gradient_state.adjust_scheduler:
    self.scheduler._step_count += 1
   return

  for opt in self.optimizers:
   if opt.step_was_skipped:
    return
  if self.split_batches:
   # Split batches -> the training dataloader batch size is not changed so one step per training step
   self.scheduler.step(*__lowercase   ,   **__lowercase    )
  else:
   # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
   # num_processes steps per training step
   __A	:		Tuple	  =      AcceleratorState().num_processes
   for _ in range(__lowercase    ):
    # Special case when using OneCycle and `drop_last` was not used
    if hasattr(self.scheduler   ,   'total_steps'    ):
     if self.scheduler._step_count <= self.scheduler.total_steps:
      self.scheduler.step(*__lowercase   ,   **__lowercase    )
    else:
     self.scheduler.step(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_last_lr()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.state_dict()


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.scheduler.get_lr()







 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  return self.scheduler.print_lr(*__lowercase   ,   **__lowercase    )






