





from __future__ import annotations

lowerCAmelCase_        =							1_0






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		list[int]:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    1
				snake_case_		:       int           =    max(_UpperCamelCase					)
				while placement <= max_digit:
								# declare and initialize empty buckets
								snake_case_		:       list[list]           =    [[] for _ in range(_UpperCamelCase					)]
								# split list_of_ints between the buckets
								for i in list_of_ints:
												snake_case_		:       Tuple           =    int((i / placement) % RADIX					)
												buckets[tmp].append(_UpperCamelCase					)
								# put each buckets' contents into list_of_ints
								snake_case_		:       Optional[Any]           =    0
								for b in range(_UpperCamelCase					):
												for i in buckets[b]:
																snake_case_		:       Optional[int]           =    i
																a += 1
        # move to next
								placement *= RADIX
				return list_of_ints


if __name__ == "__main__":
	import doctest

	doctest.testmod()








from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""			)

     # Get only text
     if images is None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
        return text_encoding

     # add pixel_values
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCamelCase__			)

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



