


"""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


UpperCAmelCase__  							=logging.get_logger(__name__)


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

UpperCAmelCase__  							={
    "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__  							={
    "allenai/led-base-16384": 1_6384,
}





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






       '''simple docstring'''


       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									=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get("""add_prefix_space"""      ,       A_							) != add_prefix_space:
        __lowercase									=	getattr(A_      ,       pre_tok_state.pop("""type"""							)							)
        __lowercase									=	add_prefix_space
        __lowercase									=	pre_tok_class(**A_							)

       __lowercase									=	add_prefix_space

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

        __lowercase									=	False

        if state.get("""add_prefix_space"""      ,       A_							) != add_prefix_space:
         __lowercase									=	add_prefix_space
         __lowercase									=	True

        if state.get("""trim_offsets"""      ,       A_							) != trim_offsets:
         __lowercase									=	trim_offsets
         __lowercase									=	True

        if changes_to_apply:
         __lowercase									=	getattr(A_      ,       state.pop("""type"""							)							)
         __lowercase									=	component_class(**A_							)
         setattr(self.backend_tokenizer      ,       A_      ,       A_							)
      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	AddedToken(A_      ,       lstrip=A_      ,       rstrip=A_							) if isinstance(A_      ,       A_							) else value
       __lowercase									=	value
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       *A_	:	List[str]      ,       **A_	:	Any							):






       '''simple docstring'''


       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       *A_	:	List[str]      ,       **A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	str      ,       A_	:	Optional[str] = None							):






       '''simple docstring'''


       __lowercase									=	self._tokenizer.model.save(A_      ,       name=A_							)
       return tuple(A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	str      ,       A_	:	List[str]=None							):






       '''simple docstring'''


       __lowercase									=	[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 SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	List[int]      ,       A_	:	Optional[List[int]] = None							):






       '''simple docstring'''


       __lowercase									=	[self.sep_token_id]
       __lowercase									=	[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 SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	Union[Dict[str, EncodedInput], BatchEncoding]      ,       A_	:	Optional[int] = None      ,       A_	:	PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[bool] = None      ,       ):






       '''simple docstring'''


       __lowercase									=	super()._pad(
           encoded_inputs=A_      ,       max_length=A_      ,       padding_strategy=A_      ,       pad_to_multiple_of=A_      ,       return_attention_mask=A_      ,       )

       # Load from model defaults
       if return_attention_mask is None:
        __lowercase									=	"""attention_mask""" in self.model_input_names

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

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

       return encoded_inputs




"""simple docstring"""

import random


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: float				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







 __lowercase									=	{i: [] for i in range(UpperCamelCase__ )}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(UpperCamelCase__ )
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(UpperCamelCase__ ):
  for j in range(i + 1				,   UpperCamelCase__ ):
   if random.random() < probability:
    graph[i].append(UpperCamelCase__ )
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(UpperCamelCase__ )
 return graph


def lowerCAmelCase_      (				UpperCamelCase__	: int ):






 """simple docstring"""







 return {
     i: [j for j in range(UpperCamelCase__ ) if i != j] for i in range(UpperCamelCase__ )
 }


if __name__ == "__main__":
       import doctest

       doctest.testmod()
