



'''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 	_lowerCAmelCase     (    lowercase_       ):






		'''simple docstring'''
		snake_case_							= ['image_processor', 'tokenizer']
		snake_case_							= 'LayoutLMv2ImageProcessor'
		snake_case_							= ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
		def __init__(						self							:       Any      ,		UpperCamelCase_							:       Any=None      ,		UpperCamelCase_							:       int=None      ,		**UpperCamelCase_							:       Tuple		)      ->			str:







									'''simple docstring'''





									_lowercase							:		Dict				=   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_      ,		)
																_lowercase							:		int				=   kwargs.pop('''feature_extractor'''		)

									_lowercase							:		Union[str, Any]				=   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							:       str      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       str = None      ,		UpperCamelCase_							:       Optional[int] = None      ,		UpperCamelCase_							:       List[Any] = None      ,		UpperCamelCase_							:       int = None      ,		UpperCamelCase_							:       Any = True      ,		UpperCamelCase_							:       List[Any] = False      ,		UpperCamelCase_							:       Tuple = False      ,		UpperCamelCase_							:       Optional[Any] = None      ,		UpperCamelCase_							:       Union[str, Any] = 0      ,		UpperCamelCase_							:       str = None      ,		UpperCamelCase_							:       List[str] = None      ,		UpperCamelCase_							:       Tuple = None      ,		UpperCamelCase_							:       Any = False      ,		UpperCamelCase_							:       Optional[int] = False      ,		UpperCamelCase_							:       Tuple = False      ,		UpperCamelCase_							:       int = False      ,		UpperCamelCase_							:       int = True      ,		UpperCamelCase_							:       Union[str, Any] = None      ,		**UpperCamelCase_							:       List[str]      ,		)      ->			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
									_lowercase							:		Optional[int]				=   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_		):
																							_lowercase							:		Dict				=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
																_lowercase							:		int				=   features["words"]

									_lowercase							:		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
									_lowercase							:		List[str]				=   features.pop('''pixel_values'''		)
									if return_overflowing_tokens is True:
																_lowercase							:		Dict				=   self.get_overflowing_images(UpperCAmelCase_      ,		encoded_inputs['''overflow_to_sample_mapping''']		)
									_lowercase							:		Optional[Any]				=   images

									return encoded_inputs
		def 						__lowercase      (						self							:       Dict      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       Union[str, Any]		)      ->			Dict:







									'''simple docstring'''





									_lowercase							:		str				=   []
									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 						__lowercase      (						self							:       Dict      ,		*UpperCamelCase_							:       Union[str, Any]      ,		**UpperCamelCase_							:       Any		)      ->			str:







									'''simple docstring'''





									return self.tokenizer.batch_decode(*UpperCAmelCase_      ,		**UpperCAmelCase_		)
		def 						__lowercase      (						self							:       Union[str, Any]      ,		*UpperCamelCase_							:       List[str]      ,		**UpperCamelCase_							:       Union[str, Any]		)      ->			int:







									'''simple docstring'''





									return self.tokenizer.decode(*UpperCAmelCase_      ,		**UpperCAmelCase_		)
		@property
		def 						__lowercase      (						self							:       str		)      ->			Optional[Any]:







									'''simple docstring'''





									return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
		@property
		def 						__lowercase      (						self							:       Any		)      ->			str:







									'''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 						__lowercase      (						self							:       Optional[Any]		)      ->			List[Any]:







									'''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 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     quantum_engine.allocate_qubit()
       H | qubit
       Measure | qubit
       return int(snake_case_						)


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

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

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

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

  print("""Random numbers""", random_numbers_list)






