"""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


_UpperCamelCase								=		logging.get_logger(__name__)


_UpperCamelCase								=		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_UpperCamelCase								=		{
    "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								=		{
    "allenai/led-base-16384": 1_63_84,
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Union[str, Any]					     =				VOCAB_FILES_NAMES
			_UpperCamelCase  :						str					     =				PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase  :						Optional[Any]					     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase  :						str					     =				LEDTokenizer
			_UpperCamelCase  :						str					     =				['input_ids', 'attention_mask']





			def __init__(			self   ,      snake_case_=None   ,      snake_case_=None   ,      snake_case_=None   ,      snake_case_="replace"   ,      snake_case_="<s>"   ,      snake_case_="</s>"   ,      snake_case_="</s>"   ,      snake_case_="<s>"   ,      snake_case_="<unk>"   ,      snake_case_="<pad>"   ,      snake_case_="<mask>"   ,      snake_case_=False   ,      snake_case_=True   ,      **snake_case_   ,      ):







										'''simple docstring'''


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

										A__       :     int								=			add_prefix_space

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

																	A__       :     Optional[Any]								=			False

																	if state.get("""add_prefix_space"""   ,      snake_case_							) != add_prefix_space:
																								A__       :     Dict								=			add_prefix_space
																								A__       :     List[str]								=			True

																	if state.get("""trim_offsets"""   ,      snake_case_							) != trim_offsets:
																								A__       :     List[Any]								=			trim_offsets
																								A__       :     str								=			True

																	if changes_to_apply:
																								A__       :     str								=			getattr(snake_case_   ,      state.pop("""type"""							)							)
																								A__       :     Union[str, Any]								=			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							):







										'''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   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			AddedToken(snake_case_   ,      lstrip=snake_case_   ,      rstrip=snake_case_							) if isinstance(snake_case_   ,      snake_case_							) else value
										A__       :     int								=			value





			def 		lowerCamelCase					(			self   ,      *snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			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   ,      *snake_case_   ,      **snake_case_							):







										'''simple docstring'''


										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()._encode_plus(*snake_case_   ,      **snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Dict								=			self._tokenizer.model.save(snake_case_   ,      name=snake_case_							)
										return tuple(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=None							):







										'''simple docstring'''


										A__       :     Tuple								=			[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   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Any								=			[self.sep_token_id]
										A__       :     Dict								=			[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   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = PaddingStrategy.DO_NOT_PAD   ,      snake_case_ = None   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			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__       :     Optional[Any]								=			"""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[Any]								=			len(encoded_inputs["""global_attention_mask"""]							) != len(snake_case_							)

																	if needs_to_be_padded:
																								A__       :     List[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__       :     int								=			(
																															    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																															)
																								elif self.padding_side == "left":
																															A__       :     Union[str, 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

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) == 0:
														return []
							A__				,							A__       :     Dict								=			min(lowerCAmelCase							), max(lowerCAmelCase							)
							A__       :     List[Any]								=			int(max_value - min_value							) + 1
							A__       :     list[list]								=			[[] for _ in range(lowerCAmelCase							)]

							for i in my_list:
														buckets[int(i - min_value							)].append(lowerCAmelCase							)

							return [v for bucket in buckets for v in sorted(lowerCAmelCase							)]


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





