





'''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   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : List[str]           =							["""image_processor""", """tokenizer"""]
 _lowerCamelCase  : Optional[int]           =							"""LayoutLMv3ImageProcessor"""
 _lowerCamelCase  : Union[str, Any]           =							("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")




 def __init__(					self       :    Tuple	,			snake_case_       :    Tuple=None	,			snake_case_       :    Optional[Any]=None	,			**snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   None
      if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."	,			snake_case_	,			)
           _UpperCAmelCase     							=   kwargs.pop("feature_extractor"    )

      _UpperCAmelCase     							=   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__(snake_case_	,			snake_case_    )




 def __call__(					self       :    List[str]	,			snake_case_       :    Tuple	,			snake_case_       :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,			snake_case_       :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,			snake_case_       :    Union[List[List[int]], List[List[List[int]]]] = None	,			snake_case_       :    Optional[Union[List[int], List[List[int]]]] = None	,			snake_case_       :    bool = True	,			snake_case_       :    Union[bool, str, PaddingStrategy] = False	,			snake_case_       :    Union[bool, str, TruncationStrategy] = None	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    int = 0	,			snake_case_       :    Optional[int] = None	,			snake_case_       :    Optional[bool] = None	,			snake_case_       :    Optional[bool] = None	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = False	,			snake_case_       :    bool = True	,			snake_case_       :    Optional[Union[str, TensorType]] = None	,			**snake_case_       :    Optional[Any]	,			):
      # 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
      _UpperCAmelCase     							=   self.image_processor(images=snake_case_	,			return_tensors=snake_case_    )

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

      _UpperCAmelCase     							=   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=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
      _UpperCAmelCase     							=   features.pop("pixel_values"    )
      if return_overflowing_tokens is True:
           _UpperCAmelCase     							=   self.get_overflowing_images(snake_case_	,			encoded_inputs["overflow_to_sample_mapping"]    )
      _UpperCAmelCase     							=   images

      return encoded_inputs




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    List[Any]	,			snake_case_       :    Tuple    ):
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      _UpperCAmelCase     							=   []
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]    )

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

      return images_with_overflow




 def      lowercase	(					self       :    Tuple	,			*snake_case_       :    Optional[int]	,			**snake_case_       :    Any    ):
      return self.tokenizer.batch_decode(*snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Optional[Any]	,			*snake_case_       :    List[Any]	,			**snake_case_       :    Any    ):
      return self.tokenizer.decode(*snake_case_	,			**snake_case_    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return ["input_ids", "bbox", "attention_mask", "pixel_values"]




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,			snake_case_	,			)
      return self.image_processor_class




 @property
 def      lowercase	(					self       :    Tuple    ):
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,			snake_case_	,			)
      return self.image_processor











'''simple docstring'''




import math
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     return math.sqrt(__lowercase       ) * math.sqrt(__lowercase       ) == num
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     _UpperCAmelCase     							=   0
     _UpperCAmelCase     							=   n
     while left <= right:
          _UpperCAmelCase     							=   (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               _UpperCAmelCase     							=   mid - 1
          else:
               _UpperCAmelCase     							=   mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()




