


"""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


snake_case__		:     Any    				=				logging.get_logger(__name__)


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

snake_case__		:     Optional[int]    				=				{
    '''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__		:     Any    				=				{
    '''allenai/led-base-16384''': 16_384,
}



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					LEDTokenizer
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']






     def __init__(	self  :					List[Any]					,				UpperCamelCase_  :					Optional[int]=None					,				UpperCamelCase_  :					Optional[Any]=None					,				UpperCamelCase_  :					List[str]=None					,				UpperCamelCase_  :					Union[str, Any]="replace"					,				UpperCamelCase_  :					List[Any]="<s>"					,				UpperCamelCase_  :					str="</s>"					,				UpperCamelCase_  :					List[str]="</s>"					,				UpperCamelCase_  :					str="<s>"					,				UpperCamelCase_  :					Tuple="<unk>"					,				UpperCamelCase_  :					List[str]="<pad>"					,				UpperCamelCase_  :					Optional[Any]="<mask>"					,				UpperCamelCase_  :					Optional[Any]=False					,				UpperCamelCase_  :					List[str]=True					,				**UpperCamelCase_  :					List[str]					,				):
           super().__init__(
               UpperCamelCase_					,				UpperCamelCase_					,				tokenizer_file=UpperCamelCase_					,				errors=UpperCamelCase_					,				bos_token=UpperCamelCase_					,				eos_token=UpperCamelCase_					,				sep_token=UpperCamelCase_					,				cls_token=UpperCamelCase_					,				unk_token=UpperCamelCase_					,				pad_token=UpperCamelCase_					,				mask_token=UpperCamelCase_					,				add_prefix_space=UpperCamelCase_					,				trim_offsets=UpperCamelCase_					,				**UpperCamelCase_					,				)

           lowerCAmelCase					:	int       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
           if pre_tok_state.get('''add_prefix_space'''					,				UpperCamelCase_				) != add_prefix_space:
                 lowerCAmelCase					:	List[Any]       =   getattr(UpperCamelCase_					,				pre_tok_state.pop('''type'''				)				)
                 lowerCAmelCase					:	Any       =   add_prefix_space
                 lowerCAmelCase					:	int       =   pre_tok_class(**UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   add_prefix_space

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

                 lowerCAmelCase					:	str       =   False

                 if state.get('''add_prefix_space'''					,				UpperCamelCase_				) != add_prefix_space:
                       lowerCAmelCase					:	Optional[int]       =   add_prefix_space
                       lowerCAmelCase					:	Tuple       =   True

                 if state.get('''trim_offsets'''					,				UpperCamelCase_				) != trim_offsets:
                       lowerCAmelCase					:	List[str]       =   trim_offsets
                       lowerCAmelCase					:	Optional[int]       =   True

                 if changes_to_apply:
                       lowerCAmelCase					:	Optional[int]       =   getattr(UpperCamelCase_					,				state.pop('''type'''				)				)
                       lowerCAmelCase					:	Optional[int]       =   component_class(**UpperCamelCase_				)
                       setattr(self.backend_tokenizer					,				UpperCamelCase_					,				UpperCamelCase_				)






     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def      lowerCamelCase__     (	self  :					Optional[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      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					int				):
           lowerCAmelCase					:	List[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else value
           lowerCAmelCase					:	Optional[int]       =   value






     def      lowerCamelCase__     (	self  :					Optional[int]					,				*UpperCamelCase_  :					List[Any]					,				**UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Optional[Any]       =   kwargs.get('''is_split_into_words'''					,				UpperCamelCase_				)

           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(*UpperCamelCase_					,				**UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple					,				*UpperCamelCase_  :					Optional[int]					,				**UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Union[str, Any]       =   kwargs.get('''is_split_into_words'''					,				UpperCamelCase_				)

           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(*UpperCamelCase_					,				**UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           lowerCAmelCase					:	str       =   self._tokenizer.model.save(UpperCamelCase_					,				name=UpperCamelCase_				)
           return tuple(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Tuple=None				):
           lowerCAmelCase					:	Optional[int]       =   [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  :					Optional[int]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	int       =   [self.sep_token_id]
           lowerCAmelCase					:	str       =   [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  :					Optional[Any]					,				UpperCamelCase_  :					Union[Dict[str, EncodedInput], BatchEncoding]					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					Optional[bool] = None					,				):
           lowerCAmelCase					:	str       =   super()._pad(
               encoded_inputs=UpperCamelCase_					,				max_length=UpperCamelCase_					,				padding_strategy=UpperCamelCase_					,				pad_to_multiple_of=UpperCamelCase_					,				return_attention_mask=UpperCamelCase_					,				)

           # Load from model defaults
           if return_attention_mask is None:
                 lowerCAmelCase					:	List[Any]       =   '''attention_mask''' in self.model_input_names

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

                 if needs_to_be_padded:
                       lowerCAmelCase					:	Tuple       =   len(UpperCamelCase_				) - 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`
                             lowerCAmelCase					:	Optional[Any]       =   (
                                 encoded_inputs['''global_attention_mask'''] + [-1] * difference
                             )
                       elif self.padding_side == "left":
                             lowerCAmelCase					:	List[Any]       =   [-1] * difference + encoded_inputs[
                                 '''global_attention_mask'''
                             ]
                       else:
                             raise ValueError('''Invalid padding strategy:''' + str(self.padding_side				)				)

           return encoded_inputs






"""simple docstring"""





from __future__ import annotations

from typing import Any



class 		snake_case_(       a__			):
     pass



class 		snake_case_:






     def __init__(	self  :					Any					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Any       =   data
           lowerCAmelCase					:	Node | None       =   None






     def __iter__(	self  :					int				):
           lowerCAmelCase					:	Any       =   self
           lowerCAmelCase					:	Union[str, Any]       =   []
           while node:
                 if node in visited:
                       raise ContainsLoopError
                 visited.append(UpperCamelCase_				)
                 yield node.data
                 lowerCAmelCase					:	Optional[int]       =   node.next_node






     @property
     def      lowerCamelCase__     (	self  :					str				):
           try:
                 list(self				)
                 return False
           except ContainsLoopError:
                 return True


if __name__ == "__main__":
 snake_case__		:     Dict    				=				Node(1)
 snake_case__		:     Any    				=				Node(2)
 snake_case__		:     int    				=				Node(3)
 snake_case__		:     Any    				=				Node(4)
 print(root_node.has_loop)  # False
 snake_case__		:     Tuple    				=				root_node.next_node
 print(root_node.has_loop)  # True

 snake_case__		:     List[Any]    				=				Node(5)
 snake_case__		:     int    				=				Node(6)
 snake_case__		:     List[Any]    				=				Node(5)
 snake_case__		:     Dict    				=				Node(6)
 print(root_node.has_loop)  # False

 snake_case__		:     Any    				=				Node(1)
 print(root_node.has_loop)  # False


