






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase	:	int				=      {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase	:	Any				=      [
     """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BigBirdPegasusForCausalLM""",
     """BigBirdPegasusForConditionalGeneration""",
     """BigBirdPegasusForQuestionAnswering""",
     """BigBirdPegasusForSequenceClassification""",
     """BigBirdPegasusModel""",
     """BigBirdPegasusPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

 _lowerCAmelCase	:	Tuple				=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""



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	:	str				=      logging.get_logger(__name__)


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

_lowerCAmelCase	:	List[Any]				=      {
    """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	:	List[str]				=      {
    """allenai/led-base-16384""": 16_384,
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_    =LEDTokenizer
   SCREAMING_SNAKE_CASE_    =['''input_ids''', '''attention_mask''']
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	str=None							,			snake_case__      :	List[Any]=None							,			snake_case__      :	Dict=None							,			snake_case__      :	List[str]="replace"							,			snake_case__      :	Optional[int]="<s>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Union[str, Any]="</s>"							,			snake_case__      :	Dict="<s>"							,			snake_case__      :	Tuple="<unk>"							,			snake_case__      :	Any="<pad>"							,			snake_case__      :	Dict="<mask>"							,			snake_case__      :	int=False							,			snake_case__      :	Optional[int]=True							,			**snake_case__      :	List[Any]							,			):


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

        UpperCAmelCase__						:    str							=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
             UpperCAmelCase__						:    Dict							=							getattr(snake_case__							,			pre_tok_state.pop("type"							)							)
             UpperCAmelCase__						:    str							=							add_prefix_space
             UpperCAmelCase__						:    Any							=							pre_tok_class(**snake_case__							)

        UpperCAmelCase__						:    Dict							=							add_prefix_space

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

             UpperCAmelCase__						:    Any							=							False

             if state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
                  UpperCAmelCase__						:    Union[str, Any]							=							add_prefix_space
                  UpperCAmelCase__						:    List[Any]							=							True

             if state.get("trim_offsets"							,			snake_case__							) != trim_offsets:
                  UpperCAmelCase__						:    Optional[int]							=							trim_offsets
                  UpperCAmelCase__						:    List[Any]							=							True

             if changes_to_apply:
                  UpperCAmelCase__						:    List[str]							=							getattr(snake_case__							,			state.pop("type"							)							)
                  UpperCAmelCase__						:    Optional[int]							=							component_class(**snake_case__							)
                  setattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def       __a							(		self      :	Any							):


        '''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       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__							) if isinstance(snake_case__							,			snake_case__							) else value
        UpperCAmelCase__						:    Dict							=							value
   def       __a							(		self      :	str							,			*snake_case__      :	Any							,			**snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							kwargs.get("is_split_into_words"							,			snake_case__							)

        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(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	List[str]							,			*snake_case__      :	Union[str, Any]							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.get("is_split_into_words"							,			snake_case__							)

        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(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self._tokenizer.model.save(snake_case__							,			name=snake_case__							)
        return tuple(snake_case__							)
   def       __a							(		self      :	str							,			snake_case__      :	List[Any]							,			snake_case__      :	str=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[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       __a							(		self      :	str							,			snake_case__      :	List[int]							,			snake_case__      :	Optional[List[int]] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							[self.sep_token_id]
        UpperCAmelCase__						:    Optional[Any]							=							[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       __a							(		self      :	Any							,			snake_case__      :	Union[Dict[str, EncodedInput], BatchEncoding]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[bool] = None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							super()._pad(
            encoded_inputs=snake_case__							,			max_length=snake_case__							,			padding_strategy=snake_case__							,			pad_to_multiple_of=snake_case__							,			return_attention_mask=snake_case__							,			)

        # Load from model defaults
        if return_attention_mask is None:
             UpperCAmelCase__						:    Optional[int]							=							"attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
             UpperCAmelCase__						:    List[Any]							=							encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             UpperCAmelCase__						:    Any							=							len(encoded_inputs["global_attention_mask"]							) != len(snake_case__							)

             if needs_to_be_padded:
                  UpperCAmelCase__						:    List[str]							=							len(snake_case__							) - 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`
                       UpperCAmelCase__						:    Dict							=							(
                           encoded_inputs["global_attention_mask"] + [-1] * difference
                       )
                  elif self.padding_side == "left":
                       UpperCAmelCase__						:    Dict							=							[-1] * difference + encoded_inputs[
                           "global_attention_mask"
                       ]
                  else:
                       raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

        return encoded_inputs


