




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__       (					UpperCamelCase__      ):
     a		:			Tuple							=	["""image_processor""", """tokenizer"""]
     a		:			Dict							=	"""LayoutLMv2ImageProcessor"""
     a		:			str							=	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







     def __init__(	self  ,		A=None  ,		A=None  ,		**A )  ->    List[Any]:



          '''simple docstring'''



          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."  ,		A  ,		)
               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__(A  ,		A )







     def __call__(	self  ,		A  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = True  ,		A = False  ,		A = False  ,		A = None  ,		A = 0  ,		A = None  ,		A = None  ,		A = None  ,		A = False  ,		A = False  ,		A = False  ,		A = False  ,		A = True  ,		A = None  ,		**A  ,		)  ->    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
          a      =      self.image_processor(images=A  ,		return_tensors=A )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(A  ,		A ):
                    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=A  ,		add_special_tokens=A  ,		padding=A  ,		truncation=A  ,		max_length=A  ,		stride=A  ,		pad_to_multiple_of=A  ,		return_token_type_ids=A  ,		return_attention_mask=A  ,		return_overflowing_tokens=A  ,		return_special_tokens_mask=A  ,		return_offsets_mapping=A  ,		return_length=A  ,		verbose=A  ,		return_tensors=A  ,		**A  ,		)

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

          return encoded_inputs







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    List[str]:



          '''simple docstring'''



          a      =      []
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx] )

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

          return images_with_overflow







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    Union[str, Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		*A  ,		**A )  ->    Optional[int]:



          '''simple docstring'''



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







     @property
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



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







     @property
     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



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







     @property
     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



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







import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

