


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


UpperCamelCase					:       Union[str, Any]			    =     logging.get_logger(__name__)


UpperCamelCase					:       Optional[Any]			    =     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase					:       Optional[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""",
    },
}

UpperCamelCase					:       Dict			    =     {
    """allenai/led-base-16384""": 1_6384,
}



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		LEDTokenizer
  _lowercase									=		['input_ids', 'attention_mask']

  def __init__( self			:  Tuple				,				lowerCamelCase__			:  Any=None				,				lowerCamelCase__			:  List[str]=None				,				lowerCamelCase__			:  Any=None				,				lowerCamelCase__			:  int="replace"				,				lowerCamelCase__			:  Union[str, Any]="<s>"				,				lowerCamelCase__			:  Union[str, Any]="</s>"				,				lowerCamelCase__			:  Tuple="</s>"				,				lowerCamelCase__			:  Optional[int]="<s>"				,				lowerCamelCase__			:  str="<unk>"				,				lowerCamelCase__			:  Any="<pad>"				,				lowerCamelCase__			:  Any="<mask>"				,				lowerCamelCase__			:  Optional[int]=False				,				lowerCamelCase__			:  int=True				,				**lowerCamelCase__			:  Union[str, Any]				,				):
        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__	:  List[str]     =   getattr(lowerCamelCase__				,				pre_tok_state.pop("type"       )       )
              a__	:  Optional[Any]     =   add_prefix_space
              a__	:  List[str]     =   pre_tok_class(**lowerCamelCase__       )

        a__	:  Optional[int]     =   add_prefix_space

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

              a__	:  Optional[int]     =   False

              if state.get("add_prefix_space"				,				lowerCamelCase__       ) != add_prefix_space:
                    a__	:  Dict     =   add_prefix_space
                    a__	:  int     =   True

              if state.get("trim_offsets"				,				lowerCamelCase__       ) != trim_offsets:
                    a__	:  List[Any]     =   trim_offsets
                    a__	:  List[str]     =   True

              if changes_to_apply:
                    a__	:  int     =   getattr(lowerCamelCase__				,				state.pop("type"       )       )
                    a__	:  int     =   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    _UpperCamelCase( self			:  Union[str, Any]       ):
        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			:  Optional[int]				,				lowerCamelCase__			:  Union[str, Any]       ):
        a__	:  Any     =   AddedToken(lowerCamelCase__				,				lstrip=lowerCamelCase__				,				rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__				,				lowerCamelCase__       ) else value
        a__	:  Union[str, Any]     =   value

  def    _UpperCamelCase( self			:  Any				,				*lowerCamelCase__			:  Optional[int]				,				**lowerCamelCase__			:  Optional[Any]       ):
        a__	:  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    _UpperCamelCase( self			:  Any				,				*lowerCamelCase__			:  Dict				,				**lowerCamelCase__			:  Optional[Any]       ):
        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    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[str] = None       ):
        a__	:  List[str]     =   self._tokenizer.model.save(lowerCamelCase__				,				name=lowerCamelCase__       )
        return tuple(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  Optional[Any]=None       ):
        a__	:  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    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        a__	:  List[str]     =   [self.sep_token_id]
        a__	:  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    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  Union[Dict[str, EncodedInput], BatchEncoding]				,				lowerCamelCase__			:  Optional[int] = None				,				lowerCamelCase__			:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD				,				lowerCamelCase__			:  Optional[int] = None				,				lowerCamelCase__			:  Optional[bool] = None				,				):
        a__	:  str     =   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:
              a__	:  Optional[int]     =   "attention_mask" in self.model_input_names

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

              if needs_to_be_padded:
                    a__	:  Union[str, 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`
                          a__	:  List[Any]     =   (
                              encoded_inputs["global_attention_mask"] + [-1] * difference
                          )
                    elif self.padding_side == "left":
                          a__	:  Any     =   [-1] * difference + encoded_inputs[
                              "global_attention_mask"
                          ]
                    else:
                          raise ValueError("Invalid padding strategy:" + str(self.padding_side       )       )

        return encoded_inputs



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						="roberta"

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple=5_02_65					, SCREAMING_SNAKE_CASE__			:			List[str]=7_68					, SCREAMING_SNAKE_CASE__			:			List[str]=12					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			Tuple=30_72					, SCREAMING_SNAKE_CASE__			:			str="gelu"					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.1					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			int=5_12					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			Dict=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=1e-12					, SCREAMING_SNAKE_CASE__			:			Any=1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0					, SCREAMING_SNAKE_CASE__			:			str=2					, SCREAMING_SNAKE_CASE__			:			Any="absolute"					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__					, bos_token_id=SCREAMING_SNAKE_CASE__					, eos_token_id=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					intermediate_size
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					max_position_embeddings
      UpperCamelCase						    =					type_vocab_size
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					position_embedding_type
      UpperCamelCase						    =					use_cache
      UpperCamelCase						    =					classifier_dropout




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      if self.task == "multiple-choice":
           UpperCamelCase						    =					{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
           UpperCamelCase						    =					{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]   )


