
"""simple docstring"""




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


_A			:       Any   		=       logging.get_logger(__name__)


_A			:       List[str]   		=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
_A			:       Any   		=       {
    """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""",
    },
}

_A			:       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,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				BartTokenizer
   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	,    ):
          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	,    )

          lowercase    :				Any       		=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
          if pre_tok_state.get("add_prefix_space"	,    _a				) != add_prefix_space:
                 lowercase    :				Union[str, Any]       		=							getattr(_a	,    pre_tok_state.pop("type"				)				)
                 lowercase    :				Optional[int]       		=							add_prefix_space
                 lowercase    :				int       		=							pre_tok_class(**_a				)

          lowercase    :				Optional[Any]       		=							add_prefix_space

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

                 lowercase    :				int       		=							False

                 if state.get("add_prefix_space"	,    _a				) != add_prefix_space:
                        lowercase    :				int       		=							add_prefix_space
                        lowercase    :				List[str]       		=							True

                 if state.get("trim_offsets"	,    _a				) != trim_offsets:
                        lowercase    :				Dict       		=							trim_offsets
                        lowercase    :				List[str]       		=							True

                 if changes_to_apply:
                        lowercase    :				List[Any]       		=							getattr(_a	,    state.pop("type"				)				)
                        lowercase    :				Union[str, Any]       		=							component_class(**_a				)
                        setattr(self.backend_tokenizer	,    _a	,    _a				)
   @property
   def 			__magic_name__			(			self				):
          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	,    _a				):
          lowercase    :				List[str]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else value
          lowercase    :				List[Any]       		=							value
   def 			__magic_name__			(			self	,    *_a	,    **_a				):
          lowercase    :				Dict       		=							kwargs.get("is_split_into_words"	,    _a				)

          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(*_a	,    **_a				)
   def 			__magic_name__			(			self	,    *_a	,    **_a				):
          lowercase    :				Tuple       		=							kwargs.get("is_split_into_words"	,    _a				)

          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(*_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				str       		=							self._tokenizer.model.save(_a	,    name=_a				)
          return tuple(_a				)
   def 			__magic_name__			(			self	,    _a	,    _a=None				):
          lowercase    :				Tuple       		=							[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	,    _a	,    _a = None				):
          lowercase    :				Union[str, Any]       		=							[self.sep_token_id]
          lowercase    :				Dict       		=							[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]




"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   _A			:       Union[str, Any]   		=       None

_A			:       str   		=       logging.get_logger(__name__)
_A			:       Tuple   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Optional[Any]   		=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_A			:       Optional[Any]   		=       {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_A			:       List[str]   		=       """▁"""

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase            =				FNetTokenizer
   def __init__(			self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=True	,    _a="<unk>"	,    _a="[SEP]"	,    _a="<pad>"	,    _a="[CLS]"	,    _a="[MASK]"	,    **_a	,    ):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          lowercase    :				int       		=							(
              AddedToken(_a	,    lstrip=_a	,    rstrip=_a	,    normalized=_a				)
              if isinstance(_a	,    _a				)
              else mask_token
          )

          super().__init__(
              _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    **_a	,    )

          lowercase    :				Dict       		=							do_lower_case
          lowercase    :				Union[str, Any]       		=							remove_space
          lowercase    :				Any       		=							keep_accents
          lowercase    :				List[Any]       		=							vocab_file
          lowercase    :				Union[str, Any]       		=							False if not self.vocab_file else True
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[Any]       		=							[self.sep_token_id]
          lowercase    :				str       		=							[self.cls_token_id]
          if token_ids_a is None:
                 return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Any       		=							[self.sep_token_id]
          lowercase    :				Dict       		=							[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				) * [0] + len(token_ids_a + sep				) * [1]

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file	,    _a				)

          return (out_vocab_file,)


