import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


snake_case__				= logging.get_logger(__name__)

snake_case__				= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case__				= {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

snake_case__				= {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_										=  ['input_ids', 'attention_mask']
     A_										=  RobertaTokenizer



     def __init__(					self				,				A_=None				,				A_=None				,				A_=None				,				A_="replace"				,				A_="<s>"				,				A_="</s>"				,				A_="</s>"				,				A_="<s>"				,				A_="<unk>"				,				A_="<pad>"				,				A_="<mask>"				,				A_=False				,				A_=True				,				**A_				,				) ->				List[Any]:






           """simple docstring"""
           super().__init__(
               A_				,				A_				,				tokenizer_file=A_				,				errors=A_				,				bos_token=A_				,				eos_token=A_				,				sep_token=A_				,				cls_token=A_				,				unk_token=A_				,				pad_token=A_				,				mask_token=A_				,				add_prefix_space=A_				,				trim_offsets=A_				,				**A_				,				)

           _lowerCamelCase			    =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
           if pre_tok_state.get('''add_prefix_space'''				,				A_						) != add_prefix_space:
                 _lowerCamelCase			    =				getattr(A_				,				pre_tok_state.pop('''type'''						)						)
                 _lowerCamelCase			    =				add_prefix_space
                 _lowerCamelCase			    =				pre_tok_class(**A_						)

           _lowerCamelCase			    =				add_prefix_space

           _lowerCamelCase			    =				'''post_processor'''
           _lowerCamelCase			    =				getattr(self.backend_tokenizer				,				A_				,				A_						)
           if tokenizer_component_instance:
                 _lowerCamelCase			    =				json.loads(tokenizer_component_instance.__getstate__()						)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       _lowerCamelCase			    =				tuple(state['''sep''']						)
                 if "cls" in state:
                       _lowerCamelCase			    =				tuple(state['''cls''']						)

                 _lowerCamelCase			    =				False

                 if state.get('''add_prefix_space'''				,				A_						) != add_prefix_space:
                       _lowerCamelCase			    =				add_prefix_space
                       _lowerCamelCase			    =				True

                 if state.get('''trim_offsets'''				,				A_						) != trim_offsets:
                       _lowerCamelCase			    =				trim_offsets
                       _lowerCamelCase			    =				True

                 if changes_to_apply:
                       _lowerCamelCase			    =				getattr(A_				,				state.pop('''type'''						)						)
                       _lowerCamelCase			    =				component_class(**A_						)
                       setattr(self.backend_tokenizer				,				A_				,				A_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           if self._mask_token is None:
                 if self.verbose:
                       logger.error('''Using mask_token, but it is not set yet.'''						)
                 return None
           return str(self._mask_token						)



     @mask_token.setter
     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				AddedToken(A_				,				lstrip=A_				,				rstrip=A_						) if isinstance(A_				,				A_						) else value
           _lowerCamelCase			    =				value



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				BatchEncoding:






           """simple docstring"""
           _lowerCamelCase			    =				kwargs.get('''is_split_into_words'''				,				A_						)
           assert self.add_prefix_space or not is_split_into_words, (
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				BatchEncoding:






           """simple docstring"""
           _lowerCamelCase			    =				kwargs.get('''is_split_into_words'''				,				A_						)

           assert self.add_prefix_space or not is_split_into_words, (
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           _lowerCamelCase			    =				self._tokenizer.model.save(A_				,				name=A_						)
           return tuple(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_=None						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]
           _lowerCamelCase			    =				[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]
from typing import List

import numpy as np
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				{key: len(__UpperCAmelCase		) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      if len(set(lists_lengths.values()		)		) > 1:
            raise RuntimeError(
                (
                    '''Sharding is ambiguous for this dataset: '''
                    + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
                    + '''\n'''.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()		)
                    + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
                    + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
                )		)
      _lowerCamelCase			    =				max(lists_lengths.values() ,						default=0		)
      return max(1 ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[range]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for group_idx in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            _lowerCamelCase			    =				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            _lowerCamelCase			    =				range(__UpperCAmelCase ,						start + num_shards_to_add		)
            shards_indices_per_group.append(__UpperCAmelCase		)
      return shards_indices_per_group
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[dict]:



      '''simple docstring'''

      _lowerCamelCase			    =				_number_of_shards_in_gen_kwargs(__UpperCAmelCase		)
      if num_shards == 1:
            return [dict(__UpperCAmelCase		)]
      else:
            _lowerCamelCase			    =				_distribute_shards(num_shards=__UpperCAmelCase ,						max_num_jobs=__UpperCAmelCase		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(__UpperCAmelCase		)		)
            ]
def       __magic_name__(  __UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key] ,						__UpperCAmelCase		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    dict:



      '''simple docstring'''

      _lowerCamelCase			    =				{len(__UpperCAmelCase		) for value in gen_kwargs.values() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      _lowerCamelCase			    =				{}
      for size in list_sizes:
            _lowerCamelCase			    =				list(range(__UpperCAmelCase		)		)
            rng.shuffle(indices_per_size[size]		)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      _lowerCamelCase			    =				dict(__UpperCAmelCase		)
      for key, value in shuffled_kwargs.items():
            if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		):
                  _lowerCamelCase			    =				[value[i] for i in indices_per_size[len(__UpperCAmelCase		)]]
      return shuffled_kwargs