
"""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"""



				__UpperCamelCase				:						Optional[int] 	=       ['''image_processor''', '''tokenizer''']
				__UpperCamelCase				:						List[Any] 	=       '''LayoutLMv2ImageProcessor'''
				__UpperCamelCase				:						Optional[int] 	=       ('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



				def __init__(self       ,	SCREAMING_SNAKE_CASE__=None       ,	SCREAMING_SNAKE_CASE__=None       ,	**SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	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       ,	)
																		SCREAMING_SNAKE_CASE__						:	Dict									=       kwargs.pop("""feature_extractor"""   )

											SCREAMING_SNAKE_CASE__						:	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       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = True       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = 0       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = False       ,	SCREAMING_SNAKE_CASE__ = True       ,	SCREAMING_SNAKE_CASE__ = None       ,	**SCREAMING_SNAKE_CASE__       ,	) ->		str:



											"""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
											SCREAMING_SNAKE_CASE__						:	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   ):
																									SCREAMING_SNAKE_CASE__						:	str									=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       features["""words"""]

											SCREAMING_SNAKE_CASE__						:	Tuple									=       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
											SCREAMING_SNAKE_CASE__						:	str									=       features.pop("""pixel_values"""   )
											if return_overflowing_tokens is True:
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       self.get_overflowing_images(__UpperCamelCase       ,	encoded_inputs["""overflow_to_sample_mapping"""]   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       images

											return encoded_inputs



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[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      __magic_name__   (self       ,	*SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	*SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




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



				@property
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




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



				@property
				def      __magic_name__   (self   ) ->		int:



											"""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      __magic_name__   (self   ) ->		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"""




def lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       len(_snake_case						)
							for i in range(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       []
														for j in range(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       0
																					for k in range(_snake_case						):
																												SCREAMING_SNAKE_CASE__						:	Optional[int]									=       val + matrix_a[i][k] * matrix_b[k][j]
																					list_a.append(_snake_case						)
														matrix_c.append(_snake_case						)
							return matrix_c

def lowercase_  (       _snake_case						):
							return [[int(row == column						) for column in range(_snake_case						)] for row in range(_snake_case						)]

def lowercase_  (       _snake_case						):
							if n <= 1:
														return n
							SCREAMING_SNAKE_CASE__						:	Tuple									=       identity(2						)
							SCREAMING_SNAKE_CASE__						:	str									=       [[1, 1], [1, 0]]
							SCREAMING_SNAKE_CASE__						:	str									=       n - 1
							while n > 0:
														if n % 2 == 1:
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       multiply(_snake_case       ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:	int									=       multiply(_snake_case       ,_snake_case						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       int(n / 2						)
							return res_matrix[0][0]

def lowercase_  (       _snake_case						):
							if n <= 1:
														return n
							SCREAMING_SNAKE_CASE__						:	int									=       0
							SCREAMING_SNAKE_CASE__						:	str									=       1
							for _ in range(2       ,n + 1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fiba, fiba + fiba
							return fiba

def lowercase_  (       ):
							for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
														print(
														    f'''{ordinal} fibonacci number using matrix exponentiation is '''
														    f'''{nth_fibonacci_matrix(_snake_case						)} and using bruteforce is '''
														    f'''{nth_fibonacci_bruteforce(_snake_case						)}\n'''						)
							# from timeit import timeit
							# print(timeit("nth_fibonacci_matrix(1000000)",
							#              "from main import nth_fibonacci_matrix", number=5))
							# print(timeit("nth_fibonacci_bruteforce(1000000)",
							#              "from main import nth_fibonacci_bruteforce", number=5))
							# 2.3342058970001744
							# 57.256506615000035


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




