
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_bart import BartTokenizer


UpperCAmelCase__		:     Optional[Any]     			=      logging.get_logger(__name__)


UpperCAmelCase__		:     List[Any]     			=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase__		:     int     			=      {
    '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',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase__		:     str     			=      {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    VOCAB_FILES_NAMES
   __UpperCamelCase		:    Tuple 				=    PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase		:    List[str] 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase		:    Any 				=    ['''input_ids''', '''attention_mask''']
   __UpperCamelCase		:    Dict 				=    BartTokenizer


   def __init__(					self     :							Optional[int]			,			lowerCAmelCase_     :							Any=None			,			lowerCAmelCase_     :							Optional[int]=None			,			lowerCAmelCase_     :							int=None			,			lowerCAmelCase_     :							str="replace"			,			lowerCAmelCase_     :							List[str]="<s>"			,			lowerCAmelCase_     :							Union[str, Any]="</s>"			,			lowerCAmelCase_     :							Any="</s>"			,			lowerCAmelCase_     :							Optional[Any]="<s>"			,			lowerCAmelCase_     :							Optional[Any]="<unk>"			,			lowerCAmelCase_     :							str="<pad>"			,			lowerCAmelCase_     :							int="<mask>"			,			lowerCAmelCase_     :							int=False			,			lowerCAmelCase_     :							Optional[int]=True			,			**lowerCAmelCase_     :							List[Any]			,			):

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

      _A:  List[Any]					=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
      if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase_     ) != add_prefix_space:
         _A:  Optional[Any]					=       getattr(lowerCAmelCase_			,			pre_tok_state.pop('''type'''     )     )
         _A:  List[Any]					=       add_prefix_space
         _A:  Any					=       pre_tok_class(**lowerCAmelCase_     )

      _A:  List[Any]					=       add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      _A:  Optional[Any]					=       '''post_processor'''
      _A:  Dict					=       getattr(self.backend_tokenizer			,			lowerCAmelCase_			,			lowerCAmelCase_     )
      if tokenizer_component_instance:
         _A:  Optional[int]					=       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:
            _A:  List[Any]					=       tuple(state['''sep''']     )
         if "cls" in state:
            _A:  Union[str, Any]					=       tuple(state['''cls''']     )

         _A:  List[str]					=       False

         if state.get('''add_prefix_space'''			,			lowerCAmelCase_     ) != add_prefix_space:
            _A:  List[Any]					=       add_prefix_space
            _A:  Dict					=       True

         if state.get('''trim_offsets'''			,			lowerCAmelCase_     ) != trim_offsets:
            _A:  Optional[int]					=       trim_offsets
            _A:  Union[str, Any]					=       True

         if changes_to_apply:
            _A:  Tuple					=       getattr(lowerCAmelCase_			,			state.pop('''type'''     )     )
            _A:  str					=       component_class(**lowerCAmelCase_     )
            setattr(self.backend_tokenizer			,			lowerCAmelCase_			,			lowerCAmelCase_     )


   @property
   def  __magic_name__				(					self     :							int     ):

      """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  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       AddedToken(lowerCAmelCase_			,			lstrip=lowerCAmelCase_			,			rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ) else value
      _A:  Optional[Any]					=       value


   def  __magic_name__				(					self     :							Dict			,			*lowerCAmelCase_     :							str			,			**lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       kwargs.get('''is_split_into_words'''			,			lowerCAmelCase_     )

      if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             '''to use it with pretokenized inputs.'''     )

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


   def  __magic_name__				(					self     :							Any			,			*lowerCAmelCase_     :							Dict			,			**lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Dict					=       kwargs.get('''is_split_into_words'''			,			lowerCAmelCase_     )

      if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             '''to use it with pretokenized inputs.'''     )

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


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str] = None     ):

      """simple docstring"""
      _A:  int					=       self._tokenizer.model.save(lowerCAmelCase_			,			name=lowerCAmelCase_     )
      return tuple(lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Union[str, Any]=None     ):

      """simple docstring"""
      _A:  List[Any]					=       [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  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       [self.sep_token_id]
      _A:  str					=       [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]





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict






@pytest.mark.parametrize(
    '''files'''		,		[
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]		,		)
def   lowerCamelCase__							(					a		,		a      )      ->   Any:
   _A:  Any					=       tmp_path_factory.mktemp('''dset_infos_dir'''      )
   if "full:README.md" in files:
      with open(dataset_infos_dir / '''README.md'''		,		'''w'''      ) as f:
         f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''      )
   if "empty:README.md" in files:
      with open(dataset_infos_dir / '''README.md'''		,		'''w'''      ) as f:
         f.write(''''''      )
    # we want to support dataset_infos.json for backward compatibility
   if "dataset_infos.json" in files:
      with open(dataset_infos_dir / '''dataset_infos.json'''		,		'''w'''      ) as f:
         f.write('''{"default": {"dataset_size": 42}}'''      )
   _A:  Optional[int]					=       DatasetInfosDict.from_directory(a      )
   assert dataset_infos
   assert dataset_infos["default"].dataset_size == 42






@pytest.mark.parametrize(
    '''dataset_info'''		,		[
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''		,		features=Features({'''a''': Value('''int32'''      )}      )		,		builder_name='''builder'''		,		config_name='''config'''		,		version='''1.0.0'''		,		splits=[{'''name''': '''train'''}]		,		download_size=42		,		),
    ]		,		)
def   lowerCamelCase__							(					a		,		a      )      ->   Any:
   _A:  int					=       str(a      )
   dataset_info.write_to_directory(a      )
   _A:  str					=       DatasetInfo.from_directory(a      )
   assert dataset_info == reloaded
   assert os.path.exists(os.path.join(a		,		'''dataset_info.json'''      )      )






def   lowerCamelCase__							(					)      ->   Any:
   _A:  int					=       DatasetInfo(
       description='''foo'''		,		citation='''bar'''		,		homepage='''https://foo.bar'''		,		license='''CC0'''		,		features=Features({'''a''': Value('''int32'''      )}      )		,		post_processed={}		,		supervised_keys=()		,		task_templates=[]		,		builder_name='''builder'''		,		config_name='''config'''		,		version='''1.0.0'''		,		splits=[{'''name''': '''train''', '''num_examples''': 42}]		,		download_checksums={}		,		download_size=13_37		,		post_processing_size=4_42		,		dataset_size=12_34		,		size_in_bytes=13_37 + 4_42 + 12_34		,		)
   _A:  Optional[Any]					=       dataset_info._to_yaml_dict()
   assert sorted(a      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      assert key in dataset_info_yaml_dict
      assert isinstance(dataset_info_yaml_dict[key]		,		(list, dict, int, str)      )
   _A:  str					=       yaml.safe_dump(a      )
   _A:  Optional[int]					=       yaml.safe_load(a      )
   assert dataset_info_yaml_dict == reloaded






def   lowerCamelCase__							(					)      ->   int:
   _A:  Union[str, Any]					=       DatasetInfo()
   _A:  Union[str, Any]					=       dataset_info._to_yaml_dict()
   assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    '''dataset_infos_dict'''		,		[
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}      ),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''		,		features=Features({'''a''': Value('''int32'''      )}      )		,		builder_name='''builder'''		,		config_name='''config'''		,		version='''1.0.0'''		,		splits=[{'''name''': '''train'''}]		,		download_size=42		,		)
            }      ),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42      ),
                '''v2''': DatasetInfo(dataset_size=13_37      ),
            }      ),
    ]		,		)
def   lowerCamelCase__							(					a		,		a      )      ->   Optional[int]:
   _A:  Optional[int]					=       str(a      )
   dataset_infos_dict.write_to_directory(a      )
   _A:  Union[str, Any]					=       DatasetInfosDict.from_directory(a      )

   # the config_name of the dataset_infos_dict take over the attribute
   for config_name, dataset_info in dataset_infos_dict.items():
      _A:  Optional[Any]					=       config_name
      # the yaml representation doesn't include fields like description or citation
      # so we just test that we can recover what we can from the yaml
      _A:  Any					=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
   assert dataset_infos_dict == reloaded

   if dataset_infos_dict:
      assert os.path.exists(os.path.join(a		,		'''README.md'''      )      )



