

import warnings
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 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	str       =						["""image_processor""", """tokenizer"""]
   snake_case_   :	List[str]       =						"""LayoutLMv2ImageProcessor"""
   snake_case_   :	str       =						("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")

   def __init__(   self							:     Optional[int]			,						lowerCAmelCase							:     Optional[Any]=None			,						lowerCAmelCase							:     Optional[Any]=None			,						**lowerCAmelCase							:     Tuple)			->			str:

    """simple docstring"""







    _snake_case      :					Tuple					=   None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""			,						lowerCAmelCase			,						)
     _snake_case      :					Union[str, Any]					=   kwargs.pop("""feature_extractor""")

    _snake_case      :					List[str]					=   image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`.""")
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`.""")

    super().__init__(lowerCAmelCase			,						lowerCAmelCase)

   def __call__(   self							:     Tuple			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,						lowerCAmelCase							:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,						lowerCAmelCase							:     Union[List[List[int]], List[List[List[int]]]] = None			,						lowerCAmelCase							:     Optional[Union[List[int], List[List[int]]]] = None			,						lowerCAmelCase							:     bool = True			,						lowerCAmelCase							:     Union[bool, str, PaddingStrategy] = False			,						lowerCAmelCase							:     Union[bool, str, TruncationStrategy] = False			,						lowerCAmelCase							:     Optional[int] = None			,						lowerCAmelCase							:     int = 0			,						lowerCAmelCase							:     Optional[int] = None			,						lowerCAmelCase							:     Optional[bool] = None			,						lowerCAmelCase							:     Optional[bool] = None			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     bool = True			,						lowerCAmelCase							:     Optional[Union[str, TensorType]] = None			,						**lowerCAmelCase							:     Optional[Any]			,						)			->			BatchEncoding:

    """simple docstring"""







    if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.""")

    if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""")

    if return_overflowing_tokens is True and return_offsets_mapping is False:
     raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""")

    # first, apply the image processor
    _snake_case      :					Dict					=   self.image_processor(images=lowerCAmelCase			,						return_tensors=lowerCAmelCase)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCAmelCase			,						lowerCAmelCase):
      _snake_case      :					Tuple					=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
     _snake_case      :					Optional[int]					=   features["""words"""]

    _snake_case      :					Union[str, Any]					=   self.tokenizer(
        text=text if text is not None else features["""words"""]			,						text_pair=text_pair if text_pair is not None else None			,						boxes=boxes if boxes is not None else features["""boxes"""]			,						word_labels=lowerCAmelCase			,						add_special_tokens=lowerCAmelCase			,						padding=lowerCAmelCase			,						truncation=lowerCAmelCase			,						max_length=lowerCAmelCase			,						stride=lowerCAmelCase			,						pad_to_multiple_of=lowerCAmelCase			,						return_token_type_ids=lowerCAmelCase			,						return_attention_mask=lowerCAmelCase			,						return_overflowing_tokens=lowerCAmelCase			,						return_special_tokens_mask=lowerCAmelCase			,						return_offsets_mapping=lowerCAmelCase			,						return_length=lowerCAmelCase			,						verbose=lowerCAmelCase			,						return_tensors=lowerCAmelCase			,						**lowerCAmelCase			,						)

    # add pixel values
    _snake_case      :					int					=   features.pop("""pixel_values""")
    if return_overflowing_tokens is True:
     _snake_case      :					Optional[int]					=   self.get_overflowing_images(lowerCAmelCase			,						encoded_inputs["""overflow_to_sample_mapping"""])
    _snake_case      :					Optional[int]					=   images

    return encoded_inputs

   def        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     Any			,						lowerCAmelCase							:     str)			->			List[Any]:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx])

    if len(lowerCAmelCase) != len(lowerCAmelCase):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         F''' {len(lowerCAmelCase)} and {len(lowerCAmelCase)}''')

    return images_with_overflow

   def        UpperCamelCase_					(   self							:     Dict			,						*lowerCAmelCase							:     Any			,						**lowerCAmelCase							:     Dict)			->			Optional[int]:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     Dict			,						*lowerCAmelCase							:     str			,						**lowerCAmelCase							:     Dict)			->			Optional[Any]:

    """simple docstring"""







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

   @property
   def        UpperCamelCase_					(   self							:     str)			->			int:

    """simple docstring"""







    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

   @property
   def        UpperCamelCase_					(   self							:     str)			->			Any:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,						lowerCAmelCase			,						)
    return self.image_processor_class


   @property
   def        UpperCamelCase_					(   self							:     Optional[int])			->			Any:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,						lowerCAmelCase			,						)
    return self.image_processor





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       Optional[int]:
 _snake_case      :					Any					=   [701, 301, 132, 57, 23, 10, 4, 1]
 for gap in gaps:
  for i in range(SCREAMING_SNAKE_CASE__			,    len(SCREAMING_SNAKE_CASE__			)			):
   _snake_case      :					List[Any]					=   collection[i]
   _snake_case      :					List[str]					=   i
   while j >= gap and collection[j - gap] > insert_value:
    _snake_case      :					int					=   collection[j - gap]
    j -= gap
   if j != i:
    _snake_case      :					Tuple					=   insert_value
 return collection


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

  testmod()
  a__      =		input("""Enter numbers separated by a comma:\n""").strip()
  a__      =		[int(item) for item in user_input.split(""",""")]
  print(shell_sort(unsorted))
