


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_      : Dict  					=       logging.get_logger(__name__)


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

UpperCAmelCase_      : 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_      : Union[str, Any]  					=       {
    '''allenai/led-base-16384''': 1_6384,
}
class  SCREAMING_SNAKE_CASE__  (				__UpperCAmelCase			):
      snake_case__				:     int        =						VOCAB_FILES_NAMES
      snake_case__				:     Union[str, Any]        =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__				:     Optional[int]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__				:     str        =						LEDTokenizer
      snake_case__				:     List[Any]        =						['input_ids', 'attention_mask']
      def __init__(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Any=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]="replace"     ,							SCREAMING_SNAKE_CASE__				:       Tuple="<s>"     ,							SCREAMING_SNAKE_CASE__				:       int="</s>"     ,							SCREAMING_SNAKE_CASE__				:       List[Any]="</s>"     ,							SCREAMING_SNAKE_CASE__				:       Dict="<s>"     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]="<unk>"     ,							SCREAMING_SNAKE_CASE__				:       List[Any]="<pad>"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]="<mask>"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Tuple=True     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			Tuple:
             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__     ,							)

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

             a_					:		Dict							=  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     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             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_					:		Tuple							=  tuple(state['sep']     )
                    if "cls" in state:
                           a_					:		List[str]							=  tuple(state['cls']     )

                    a_					:		Optional[int]							=  False

                    if state.get('add_prefix_space'     ,							SCREAMING_SNAKE_CASE__     ) != add_prefix_space:
                           a_					:		List[str]							=  add_prefix_space
                           a_					:		Tuple							=  True

                    if state.get('trim_offsets'     ,							SCREAMING_SNAKE_CASE__     ) != trim_offsets:
                           a_					:		Union[str, Any]							=  trim_offsets
                           a_					:		Union[str, Any]							=  True

                    if changes_to_apply:
                           a_					:		str							=  getattr(SCREAMING_SNAKE_CASE__     ,							state.pop('type'     )     )
                           a_					:		List[Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             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				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			str:
             a_					:		List[Any]							=  AddedToken(SCREAMING_SNAKE_CASE__     ,							lstrip=SCREAMING_SNAKE_CASE__     ,							rstrip=SCREAMING_SNAKE_CASE__     ) if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) else value
             a_					:		List[str]							=  value
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							*SCREAMING_SNAKE_CASE__				:       Dict     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Tuple:
             a_					:		Optional[int]							=  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        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							*SCREAMING_SNAKE_CASE__				:       Tuple     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Tuple:
             a_					:		Optional[Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int] = None     )		->			List[str]:
             a_					:		int							=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE__     ,							name=SCREAMING_SNAKE_CASE__     )
             return tuple(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Any=None     )		->			List[str]:
             a_					:		Optional[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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any = None     )		->			Optional[int]:
             a_					:		Optional[int]							=  [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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any] = PaddingStrategy.DO_NOT_PAD     ,							SCREAMING_SNAKE_CASE__				:       int = None     ,							SCREAMING_SNAKE_CASE__				:       Tuple = None     ,							)		->			int:
             a_					:		Any							=  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:
                    a_					:		List[Any]							=  """attention_mask""" in self.model_input_names

             if return_attention_mask and "global_attention_mask" in encoded_inputs:
                    a_					:		Any							=  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(SCREAMING_SNAKE_CASE__     )

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

             return encoded_inputs







'''simple docstring'''




import re



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]"""						,      str_    )]



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     split_input(str_    )
			return "".join(
			    ["""""".join([char.capitalize() for char in sub_str]    ) for sub_str in string_split]    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						List[Any]	=     split_input(lowerCamelCase__    )
						if upper:
									A_	:						Tuple	=     """""".join(
									    [
									        separator.join([char.upper() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						else:
									A_	:						Optional[int]	=     """""".join(
									    [
									        separator.join([char.lower() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						return res_str
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return to_simple_case(lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						Tuple	=     to_simple_case(lowerCamelCase__    )
						return res_str[0].lower() + res_str[1:]
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """_"""    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """-"""    )


if __name__ == "__main__":
						__import__('''doctest''').testmod()