"""simple docstring"""



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Any					     =				['image_processor', 'tokenizer']
			_UpperCamelCase  :						List[Any]					     =				'BridgeTowerImageProcessor'
			_UpperCamelCase  :						Union[str, Any]					     =				('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										super().__init__(snake_case_   ,      snake_case_							)





			def __call__(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = True   ,      snake_case_ = False   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = 0   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = False   ,      snake_case_ = False   ,      snake_case_ = False   ,      snake_case_ = False   ,      snake_case_ = True   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.tokenizer(
										    text=snake_case_   ,      add_special_tokens=snake_case_   ,      padding=snake_case_   ,      truncation=snake_case_   ,      max_length=snake_case_   ,      stride=snake_case_   ,      pad_to_multiple_of=snake_case_   ,      return_token_type_ids=snake_case_   ,      return_attention_mask=snake_case_   ,      return_overflowing_tokens=snake_case_   ,      return_special_tokens_mask=snake_case_   ,      return_offsets_mapping=snake_case_   ,      return_length=snake_case_   ,      verbose=snake_case_   ,      return_tensors=snake_case_   ,      **snake_case_   ,      )
										# add pixel_values + pixel_mask
										A__       :     str								=			self.image_processor(
										    snake_case_   ,      return_tensors=snake_case_   ,      do_normalize=snake_case_   ,      do_center_crop=snake_case_   ,      **snake_case_							)
										encoding.update(snake_case_							)

										return encoding





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







										'''simple docstring'''


										return self.tokenizer.batch_decode(*snake_case_   ,      **snake_case_							)





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







										'''simple docstring'''


										return self.tokenizer.decode(*snake_case_   ,      **snake_case_							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.tokenizer.model_input_names
										A__       :     Tuple								=			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)






"""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]





