



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCamelCase						    =  logging.get_logger(__name__)


_lowerCamelCase						    =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase						    =  {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCamelCase						    =  {
    """allenai/led-base-16384""": 16384,
}

class       _SCREAMING_SNAKE_CASE	(_A ):
     lowerCAmelCase					=			VOCAB_FILES_NAMES
     lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase					=			LEDTokenizer
     lowerCAmelCase					=			["""input_ids""", """attention_mask"""]





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

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

      __SCREAMING_SNAKE_CASE       :    str       =	add_prefix_space

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

       __SCREAMING_SNAKE_CASE       :    int       =	False

       if state.get("add_prefix_space"     ,  __lowerCamelCase				) != add_prefix_space:
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	add_prefix_space
        __SCREAMING_SNAKE_CASE       :    Dict       =	True

       if state.get("trim_offsets"     ,  __lowerCamelCase				) != trim_offsets:
        __SCREAMING_SNAKE_CASE       :    Dict       =	trim_offsets
        __SCREAMING_SNAKE_CASE       :    str       =	True

       if changes_to_apply:
        __SCREAMING_SNAKE_CASE       :    int       =	getattr(__lowerCamelCase     ,  state.pop("type"				)				)
        __SCREAMING_SNAKE_CASE       :    Dict       =	component_class(**__lowerCamelCase				)
        setattr(self.backend_tokenizer     ,  __lowerCamelCase     ,  __lowerCamelCase				)





     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def 	__snake_case    (       self :							List[Any]				)->str:
      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 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Any				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase     ,  __lowerCamelCase				) else value
      __SCREAMING_SNAKE_CASE       :    Tuple       =	value





     def 	__snake_case    (       self :							Optional[Any]     ,  *UpperCamelCase :							Optional[Any]     ,  **UpperCamelCase :							List[str]				)->BatchEncoding:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	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 	__snake_case    (       self :							Dict     ,  *UpperCamelCase :							str     ,  **UpperCamelCase :							Optional[Any]				)->BatchEncoding:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	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 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Optional[str] = None				)->Tuple[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self._tokenizer.model.save(__lowerCamelCase     ,  name=__lowerCamelCase				)
      return tuple(__lowerCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							List[Any]=None				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[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 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None				)->List[int]:
      __SCREAMING_SNAKE_CASE       :    str       =	[self.sep_token_id]
      __SCREAMING_SNAKE_CASE       :    int       =	[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 :							str     ,  UpperCamelCase :							Union[Dict[str, EncodedInput], BatchEncoding]     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[bool] = None     ,  )->dict:
      __SCREAMING_SNAKE_CASE       :    Dict       =	super()._pad(
          encoded_inputs=__lowerCamelCase     ,  max_length=__lowerCamelCase     ,  padding_strategy=__lowerCamelCase     ,  pad_to_multiple_of=__lowerCamelCase     ,  return_attention_mask=__lowerCamelCase     ,  )

      # Load from model defaults
      if return_attention_mask is None:
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	"attention_mask" in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	encoded_inputs[self.model_input_names[0]]
       # `global_attention_mask` need to have the same length as other (sequential) inputs.
       __SCREAMING_SNAKE_CASE       :    Tuple       =	len(encoded_inputs["global_attention_mask"]				) != len(__lowerCamelCase				)

       if needs_to_be_padded:
        __SCREAMING_SNAKE_CASE       :    Any       =	len(__lowerCamelCase				) - len(encoded_inputs["global_attention_mask"]				)

        if self.padding_side == "right":
         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
         __SCREAMING_SNAKE_CASE       :    Tuple       =	(
             encoded_inputs["global_attention_mask"] + [-1] * difference
         )
        elif self.padding_side == "left":
         __SCREAMING_SNAKE_CASE       :    Tuple       =	[-1] * difference + encoded_inputs[
             "global_attention_mask"
         ]
        else:
         raise ValueError("Invalid padding strategy:" + str(self.padding_side				)				)

      return encoded_inputs











from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Optional[NestedDataStructureLike[PathLike]] = None     ,  UpperCamelCase :							Optional[NamedSplit] = None     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	path_or_paths
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split if split or isinstance(UpperCamelCase     ,  UpperCamelCase				) else "train"
      __SCREAMING_SNAKE_CASE       :    Dict       =	features
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Tuple       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_proc
      __SCREAMING_SNAKE_CASE       :    int       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							Optional[int]				)->Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
      pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	features
      __SCREAMING_SNAKE_CASE       :    str       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Any       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    int       =	num_proc
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							str				)->Union[Dataset, IterableDataset]:
      pass






