

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      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''image_processor''', '''tokenizer''']
 _lowerCAmelCase									=	'''LayoutLMv2ImageProcessor'''
 _lowerCAmelCase									=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')




 def __init__(			self  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  **_UpperCAmelCase							):
   __UpperCAmelCase	:  List[str]		=     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  ,  )
     __UpperCAmelCase	:  Optional[int]		=     kwargs.pop('feature_extractor'							)

   __UpperCAmelCase	:  Dict		=     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 = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = 0  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   # 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.'							)

   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
   __UpperCAmelCase	:  List[Any]		=     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							):
       __UpperCAmelCase	:  Dict		=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
     __UpperCAmelCase	:  Optional[int]		=     features['words']

   __UpperCAmelCase	:  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=_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
   __UpperCAmelCase	:  int		=     features.pop('pixel_values'							)
   if return_overflowing_tokens is True:
     __UpperCAmelCase	:  str		=     self.get_overflowing_images(_UpperCAmelCase  ,  encoded_inputs['overflow_to_sample_mapping']							)
   __UpperCAmelCase	:  Dict		=     images

   return encoded_inputs




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   __UpperCAmelCase	:  Optional[Any]		=     []
   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     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.batch_decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 @property
 def     a       (			self							):
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




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










'''simple docstring'''






import qiskit
from qiskit.providers import Backend




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						tuple[qiskit.QuantumCircuit, str, str]:





       '''simple docstring'''


       _UpperCAmelCase	,_UpperCAmelCase    :		Optional[Any]     	=					bin(lowerCAmelCase_						)[2:], bin(lowerCAmelCase_						)[2:]  # Remove leading '0b'

       # Ensure that both strings are of the same length
       if len(lowerCAmelCase_						) > len(lowerCAmelCase_						):
              _UpperCAmelCase    :		str     	=					y.zfill(len(lowerCAmelCase_						)						)
       else:
              _UpperCAmelCase    :		int     	=					x.zfill(len(lowerCAmelCase_						)						)

       # We need (3 * number of bits in the larger number)+1 qBits
       # The second parameter is the number of classical registers, to measure the result
       _UpperCAmelCase    :		Any     	=					qiskit.QuantumCircuit((len(lowerCAmelCase_						) * 3) + 1			,  len(lowerCAmelCase_						) + 1						)

       # We are essentially "not-ing" the bits that are 1
       # Reversed because it's easier to perform ops on more significant bits
       for i in range(len(lowerCAmelCase_						)						):
              if x[::-1][i] == "1":
                     circuit.x(lowerCAmelCase_						)
       for j in range(len(lowerCAmelCase_						)						):
              if y[::-1][j] == "1":
                     circuit.x(len(lowerCAmelCase_						) + j						)

       return circuit, x, y




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )->						Dict:





       '''simple docstring'''


       circuit.ccx(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.cx(lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.ccx(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.cx(lowerCAmelCase_			,  lowerCAmelCase_						)
       circuit.cx(lowerCAmelCase_			,  lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = qiskit.Aer.get_backend("""aer_simulator"""						)			,  )->						int:





       '''simple docstring'''


       if vala < 0 or vala < 0:
              raise ValueError("""Both Integers must be positive!"""						)

       # Store the Integers
       _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase    :		Union[str, Any]     	=					store_two_classics(lowerCAmelCase_			,  lowerCAmelCase_						)
       for i in range(len(lowerCAmelCase_						)						):
              full_adder(lowerCAmelCase_			,  lowerCAmelCase_			,  len(lowerCAmelCase_						) + i			,  len(lowerCAmelCase_						) + len(lowerCAmelCase_						) + i			,  len(lowerCAmelCase_						) + len(lowerCAmelCase_						) + i + 1						)
              circuit.barrier()  # Optional, just for aesthetics

       # Measure the resultant qBits
       for i in range(len(lowerCAmelCase_						) + 1						):
              circuit.measure([(len(lowerCAmelCase_						) * 2) + i]			,  [i]						)

       _UpperCAmelCase    :		Optional[int]     	=					qiskit.execute(lowerCAmelCase_			,  lowerCAmelCase_			,  shots=1						).result()

       # The result is in binary. Convert it back to int
       return int(list(res.get_counts()						)[0]			,  2						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



