
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


lowercase__       =logging.get_logger(__name__)


lowercase__       ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__       ={
    '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',
    },
}

lowercase__       ={
    'allenai/led-base-16384': 16384,
}



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

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

    __a :  Optional[int]						=  add_prefix_space

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

     __a :  Union[str, Any]						=  False

     if state.get('''add_prefix_space'''					,				snake_case_   ) != add_prefix_space:
      __a :  Optional[int]						=  add_prefix_space
      __a :  List[str]						=  True

     if state.get('''trim_offsets'''					,				snake_case_   ) != trim_offsets:
      __a :  Tuple						=  trim_offsets
      __a :  List[str]						=  True

     if changes_to_apply:
      __a :  Union[str, Any]						=  getattr(snake_case_					,				state.pop('''type'''   )   )
      __a :  Tuple						=  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       lowerCAmelCase			(self :					Optional[int]   ):
    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       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Any   ):
    __a :  Dict						=  AddedToken(snake_case_					,				lstrip=snake_case_					,				rstrip=snake_case_   ) if isinstance(snake_case_					,				snake_case_   ) else value
    __a :  Union[str, Any]						=  value
   def       lowerCAmelCase			(self :					Dict					,				*snake_case_ :					Dict					,				**snake_case_ :					Dict   ):
    __a :  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       lowerCAmelCase			(self :					List[Any]					,				*snake_case_ :					Optional[int]					,				**snake_case_ :					List[Any]   ):
    __a :  Optional[int]						=  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       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  str						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None   ):
    __a :  Dict						=  [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       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  Tuple						=  [self.sep_token_id]
    __a :  Optional[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       lowerCAmelCase			(self :					List[str]					,				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					,				):
    __a :  int						=  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:
     __a :  List[str]						=  '''attention_mask''' in self.model_input_names

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

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

    return encoded_inputs


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowercase__       =logging.get_logger(__name__)

lowercase__       ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__       ={
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

lowercase__       ={
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowercase__       ={
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

    __a :  Optional[int]						=  json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
    if (
        pre_tok_state.get('''lowercase'''					,				snake_case_   ) != do_lower_case
        or pre_tok_state.get('''strip_accents'''					,				snake_case_   ) != strip_accents
    ):
     __a :  List[str]						=  getattr(snake_case_					,				pre_tok_state.pop('''type'''   )   )
     __a :  Optional[Any]						=  do_lower_case
     __a :  Optional[int]						=  strip_accents
     __a :  List[str]						=  pre_tok_class(**snake_case_   )

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

    return output
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[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   ) * [0] + len(token_ids_a + sep   ) * [1]
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
