



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    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    ["""image_processor""", """tokenizer"""]
				lowercase__     =    """LayoutLMv2ImageProcessor"""
				lowercase__     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")

				def __init__(			self  ,			snake_case__=None  ,			snake_case__=None  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= None
									if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,			snake_case__  ,			)
														UpperCamelCase_			= kwargs.pop("feature_extractor"     )

									UpperCamelCase_			= 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__(snake_case__  ,			snake_case__     )

				def __call__(			self  ,			snake_case__  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = True  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = None  ,			snake_case__ = 0  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = None  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = False  ,			snake_case__ = True  ,			snake_case__ = None  ,			**snake_case__  ,			):

									'''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
									UpperCamelCase_			= self.image_processor(images=snake_case__  ,			return_tensors=snake_case__     )

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
														if isinstance(snake_case__  ,			snake_case__     ):
																			UpperCamelCase_			= [text]  # add batch dimension (as the image processor always adds a batch dimension)
														UpperCamelCase_			= features["words"]

									UpperCamelCase_			= 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=snake_case__  ,			add_special_tokens=snake_case__  ,			padding=snake_case__  ,			truncation=snake_case__  ,			max_length=snake_case__  ,			stride=snake_case__  ,			pad_to_multiple_of=snake_case__  ,			return_token_type_ids=snake_case__  ,			return_attention_mask=snake_case__  ,			return_overflowing_tokens=snake_case__  ,			return_special_tokens_mask=snake_case__  ,			return_offsets_mapping=snake_case__  ,			return_length=snake_case__  ,			verbose=snake_case__  ,			return_tensors=snake_case__  ,			**snake_case__  ,			)

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

									return encoded_inputs

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


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

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

									return images_with_overflow

				def    _lowerCamelCase       (			self  ,			*snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


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

				def    _lowerCamelCase       (			self  ,			*snake_case__  ,			**snake_case__     ):

									'''simple docstring'''


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

				@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."  ,			snake_case__  ,			)
									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."  ,			snake_case__  ,			)
									return self.image_processor



from math import pi



def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()