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_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE    				=			"""LayoutLMv3ImageProcessor"""
  _SCREAMING_SNAKE_CASE    				=			("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")

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

   __a       =  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__(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

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

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

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

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

   return encoded_inputs

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

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

   return images_with_overflow

  def 		_UpperCAmelCase   (		self :      Any     ,							*__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							**__SCREAMING_SNAKE_CASE :      Any       ):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							*__SCREAMING_SNAKE_CASE :      int     ,							**__SCREAMING_SNAKE_CASE :      List[str]       ):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  @property
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   return ["input_ids", "bbox", "attention_mask", "pixel_values"]

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

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



# Function to print upper half of diamond (pyramid)



def   __A		(			_A					):



 """simple docstring"""


 for i in range(0							,				_A					):
  for _ in range(0							,				n - i - 1					):  # printing spaces
   print(" "							,				end=""					)
  for _ in range(0							,				i + 1					):  # printing stars
   print("* "							,				end=""					)
  print()



def   __A		(			_A					):



 """simple docstring"""


 for i in range(_A							,				0							,				-1					):
  for _ in range(_A							,				0							,				-1					):  # printing stars
   print("* "							,				end=""					)
  print()
  for _ in range(n - i + 1							,				0							,				-1					):  # printing spaces
   print(" "							,				end=""					)



def   __A		(			_A					):



 """simple docstring"""


 if n <= 0:
  print("       ...       ....        nothing printing :("					)
  return
 floyd(_A					)  # upper half
 reverse_floyd(_A					)  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE :	Tuple							     =		1
  while K:
    SCREAMING_SNAKE_CASE :	Tuple							     =		int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE :	Optional[int]							     =		int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")


