






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


__A      =				logging.get_logger(__name__)


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

__A      =				{
    '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',
    },
}

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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					VOCAB_FILES_NAMES
							A_						  =					PRETRAINED_VOCAB_FILES_MAP
							A_						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_						  =					LEDTokenizer
							A_						  =					["input_ids", "attention_mask"]

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

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

												_A								= add_prefix_space

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

																	_A								= False

																	if state.get('''add_prefix_space'''		,     __A   ) != add_prefix_space:
																						_A								= add_prefix_space
																						_A								= True

																	if state.get('''trim_offsets'''		,     __A   ) != trim_offsets:
																						_A								= trim_offsets
																						_A								= True

																	if changes_to_apply:
																						_A								= getattr(__A		,     state.pop('''type'''   )   )
																						_A								= 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       __A (  self:   List[str]   )    ->				str:
												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       __A (  self:   List[Any]		,     __A:   Optional[int]   )    ->				str:
												_A								= AddedToken(__A		,     lstrip=__A		,     rstrip=__A   ) if isinstance(__A		,     __A   ) else value
												_A								= value

							def       __A (  self:   List[str]		,     *__A:   str		,     **__A:   str   )    ->				BatchEncoding:
												_A								= 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       __A (  self:   List[str]		,     *__A:   int		,     **__A:   int   )    ->				BatchEncoding:
												_A								= 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       __A (  self:   List[str]		,     __A:   str		,     __A:   Optional[str] = None   )    ->				Tuple[str]:
												_A								= self._tokenizer.model.save(__A		,     name=__A   )
												return tuple(__A   )

							def       __A (  self:   Optional[int]		,     __A:   int		,     __A:   Union[str, Any]=None   )    ->				int:
												_A								= [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       __A (  self:   int		,     __A:   List[int]		,     __A:   Optional[List[int]] = None   )    ->				List[int]:
												_A								= [self.sep_token_id]
												_A								= [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       __A (  self:   List[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		,     )    ->				dict:
												_A								= 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:
																	_A								= '''attention_mask''' in self.model_input_names

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

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

												return encoded_inputs











from __future__ import annotations




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):  # noqa: E741

					'''simple docstring'''







					while r - l > 1:
										_A								= (l + r) // 2
										if v[m] >= key:
															_A								= m
										else:
															_A								= m  # noqa: E741
					return r




def      __A					(     _lowercase ):

					'''simple docstring'''







					if len(_lowercase ) == 0:
										return 0

					_A								= [0] * len(_lowercase )
					_A								= 1

					_A								= v[0]

					for i in range(1					,			len(_lowercase ) ):
										if v[i] < tail[0]:
															_A								= v[i]
										elif v[i] > tail[length - 1]:
															_A								= v[i]
															length += 1
										else:
															_A								= v[i]

					return length


if __name__ == "__main__":
				import doctest

				doctest.testmod()



