


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	(   __snake_case       ):
				lowerCamelCase    :					str 					=['image_processor', 'tokenizer']
				lowerCamelCase    :					List[str] 					='LayoutLMv2ImageProcessor'
				lowerCamelCase    :					Union[str, Any] 					=('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')







				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 = False			,  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'''






					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
					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     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					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     lowerCamelCase_    (   self			,  *UpperCAmelCase			,  **UpperCAmelCase      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self			,  *UpperCAmelCase			,  **UpperCAmelCase      ):

					'''simple docstring'''






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







				@property
				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				@property
				def     lowerCamelCase_    (   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     lowerCamelCase_    (   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"""



import doctest

import projectq
from projectq.ops import H, Measure
def     UpperCAmelCase    (					_lowercase   :      projectq.cengines._main.MainEngine  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(_lowercase  )


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

		# initialises a new quantum backend
		lowercase_							 =       projectq.MainEngine()

		# Generate a list of 10 random numbers
		lowercase_							 =       [get_random_number(quantum_engine) for _ in range(10)]

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

		print('Random numbers', random_numbers_list)