
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


_snake_case      = logging.get_logger(__name__)


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

_snake_case      = {
    '''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''',
    },
}

_snake_case      = {
    '''allenai/led-base-16384''': 16_384,
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_				:					str       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_				:					Tuple       						=LEDTokenizer
 SCREAMING_SNAKE_CASE_				:					List[str]       						=["input_ids", "attention_mask"]

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






      """simple docstring"""



      super().__init__(
          SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, tokenizer_file=SCREAMING_SNAKE_CASE__					, errors=SCREAMING_SNAKE_CASE__					, bos_token=SCREAMING_SNAKE_CASE__					, eos_token=SCREAMING_SNAKE_CASE__					, sep_token=SCREAMING_SNAKE_CASE__					, cls_token=SCREAMING_SNAKE_CASE__					, unk_token=SCREAMING_SNAKE_CASE__					, pad_token=SCREAMING_SNAKE_CASE__					, mask_token=SCREAMING_SNAKE_CASE__					, add_prefix_space=SCREAMING_SNAKE_CASE__					, trim_offsets=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )

      UpperCamelCase						    =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
      if pre_tok_state.get('add_prefix_space'					, SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
           UpperCamelCase						    =					getattr(SCREAMING_SNAKE_CASE__					, pre_tok_state.pop('type'   )   )
           UpperCamelCase						    =					add_prefix_space
           UpperCamelCase						    =					pre_tok_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					add_prefix_space

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

           UpperCamelCase						    =					False

           if state.get('add_prefix_space'					, SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
                UpperCamelCase						    =					add_prefix_space
                UpperCamelCase						    =					True

           if state.get('trim_offsets'					, SCREAMING_SNAKE_CASE__   ) != trim_offsets:
                UpperCamelCase						    =					trim_offsets
                UpperCamelCase						    =					True

           if changes_to_apply:
                UpperCamelCase						    =					getattr(SCREAMING_SNAKE_CASE__					, state.pop('type'   )   )
                UpperCamelCase						    =					component_class(**SCREAMING_SNAKE_CASE__   )
                setattr(self.backend_tokenizer					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def      __lowerCAmelCase  (						self			:			Union[str, 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      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					AddedToken(SCREAMING_SNAKE_CASE__					, lstrip=SCREAMING_SNAKE_CASE__					, rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) else value
      UpperCamelCase						    =					value

 def      __lowerCAmelCase  (						self			:			List[str]					, *SCREAMING_SNAKE_CASE__			:			int					, **SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					kwargs.get('is_split_into_words'					, SCREAMING_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(*SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Dict					, *SCREAMING_SNAKE_CASE__			:			Any					, **SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					kwargs.get('is_split_into_words'					, SCREAMING_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(*SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Optional[str] = None   ):






      """simple docstring"""



      UpperCamelCase						    =					self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					, name=SCREAMING_SNAKE_CASE__   )
      return tuple(SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[Any]=None   ):






      """simple docstring"""



      UpperCamelCase						    =					[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			:			int					, SCREAMING_SNAKE_CASE__			:			List[int]					, SCREAMING_SNAKE_CASE__			:			Optional[List[int]] = None   ):






      """simple docstring"""



      UpperCamelCase						    =					[self.sep_token_id]
      UpperCamelCase						    =					[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			:			Dict					, SCREAMING_SNAKE_CASE__			:			Union[Dict[str, EncodedInput], BatchEncoding]					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None					, SCREAMING_SNAKE_CASE__			:			PaddingStrategy = PaddingStrategy.DO_NOT_PAD					, SCREAMING_SNAKE_CASE__			:			Optional[int] = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, ):






      """simple docstring"""



      UpperCamelCase						    =					super()._pad(
          encoded_inputs=SCREAMING_SNAKE_CASE__					, max_length=SCREAMING_SNAKE_CASE__					, padding_strategy=SCREAMING_SNAKE_CASE__					, pad_to_multiple_of=SCREAMING_SNAKE_CASE__					, return_attention_mask=SCREAMING_SNAKE_CASE__					, )

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

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

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

      return encoded_inputs




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=None

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )
      UpperCamelCase						    =					json.loads(feat_extract.to_json_string()   )
      for key, value in self.feat_extract_dict.items():
           self.assertEqual(obj[key]					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					os.path.join(SCREAMING_SNAKE_CASE__					, 'feat_extract.json'   )
           feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE__   )

      self.assertEqual(feat_extract_second.to_dict()					, feat_extract_first.to_dict()   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE__   )[0]
           check_json_file_has_correct_format(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE__   )

      self.assertEqual(feat_extract_second.to_dict()					, feat_extract_first.to_dict()   )






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class()
      self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )


