


'''simple docstring'''

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					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						["image_processor", "tokenizer"]
  _lowerCamelCase						=						"LayoutLMv3ImageProcessor"
  _lowerCamelCase						=						("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						None
   if "feature_extractor" in kwargs:
    warnings.warn(
        "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
        " instead."    ,						UpperCamelCase    ,						)
    lowerCamelCase_  				=						kwargs.pop("feature_extractor"  )

   lowerCamelCase_  				=						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__(UpperCamelCase    ,						UpperCamelCase  )






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







   """simple docstring"""
   # verify input
   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."  )

   # first, apply the image processor
   lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						return_tensors=UpperCamelCase  )

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

   lowerCamelCase_  				=						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=UpperCamelCase    ,						add_special_tokens=UpperCamelCase    ,						padding=UpperCamelCase    ,						truncation=UpperCamelCase    ,						max_length=UpperCamelCase    ,						stride=UpperCamelCase    ,						pad_to_multiple_of=UpperCamelCase    ,						return_token_type_ids=UpperCamelCase    ,						return_attention_mask=UpperCamelCase    ,						return_overflowing_tokens=UpperCamelCase    ,						return_special_tokens_mask=UpperCamelCase    ,						return_offsets_mapping=UpperCamelCase    ,						return_length=UpperCamelCase    ,						verbose=UpperCamelCase    ,						return_tensors=UpperCamelCase    ,						**UpperCamelCase    ,						)

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

   return encoded_inputs






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   lowerCamelCase_  				=						[]
   for sample_idx in overflow_to_sample_mapping:
    images_with_overflow.append(images[sample_idx]  )

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

   return images_with_overflow






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.tokenizer.batch_decode(*UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.tokenizer.decode(*UpperCamelCase    ,						**UpperCamelCase  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return ["input_ids", "bbox", "attention_mask", "pixel_values"]






  @property
  def 						snake_case	(					self  ):







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







  @property
  def 						snake_case	(					self  ):







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







'''simple docstring'''

from collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

 lowerCamelCase_  				=						nums[0]
 for i in range(1      ,       len(UpperCAmelCase_	)	):
  lowerCamelCase_  				=						nums[i]
  lowerCamelCase_  				=						max(UpperCAmelCase_      ,       ans + num      ,       UpperCAmelCase_	)

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     a_						:       Any      			=				int(input("""Enter number of elements : """).strip())
     a_						:       Union[str, Any]      			=				list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
     print(max_subsequence_sum(array))



