






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__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       str 					= ['image_processor', 'tokenizer']
		UpperCAmelCase_							:       List[str] 					= 'LayoutLMv2ImageProcessor'
		UpperCAmelCase_							:       List[str] 					= ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')






		def __init__(      self							, lowercase__=None							, lowercase__=None							, **lowercase__						)				->				Optional[int]:
				__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."							, lowercase__							, )
						__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__(lowercase__							, lowercase__						)






		def __call__(      self							, lowercase__							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = True							, lowercase__ = False							, lowercase__ = False							, lowercase__ = None							, lowercase__ = 0							, lowercase__ = None							, lowercase__ = None							, lowercase__ = None							, lowercase__ = False							, lowercase__ = False							, lowercase__ = False							, lowercase__ = False							, lowercase__ = True							, lowercase__ = None							, **lowercase__							, )				->				BatchEncoding:
				# verify input
				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=lowercase__							, return_tensors=lowercase__						)

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

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

				return encoded_inputs






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				List[Any]:
				# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
				__A    =							[]
				for sample_idx in overflow_to_sample_mapping:
						images_with_overflow.append(images[sample_idx]						)

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

				return images_with_overflow






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				Optional[Any]:
				return self.tokenizer.batch_decode(*lowercase__							, **lowercase__						)






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				List[str]:
				return self.tokenizer.decode(*lowercase__							, **lowercase__						)






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






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase__							, )
				return self.image_processor_class






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase__							, )
				return self.image_processor







"""simple docstring"""





from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
_lowercase						   =     TypeVar("""T""", bound="""Enum""")

@unique
class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      =80
				_lowerCAmelCase						      =70
				_lowerCAmelCase						      =60
				_lowerCAmelCase						      =50
				_lowerCAmelCase						      =40
				_lowerCAmelCase						      =30
				_lowerCAmelCase						      =20
				_lowerCAmelCase						      =10







@unique
class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      =24
				_lowerCAmelCase						      =21
				_lowerCAmelCase						      =18
				_lowerCAmelCase						      =15
				_lowerCAmelCase						      =12
				_lowerCAmelCase						      =9
				_lowerCAmelCase						      =6
				_lowerCAmelCase						      =3
				_lowerCAmelCase						      =2
				_lowerCAmelCase						      =1
				_lowerCAmelCase						      =-1
				_lowerCAmelCase						      =-2
				_lowerCAmelCase						      =-3
				_lowerCAmelCase						      =-6
				_lowerCAmelCase						      =-9
				_lowerCAmelCase						      =-12
				_lowerCAmelCase						      =-15
				_lowerCAmelCase						      =-18
				_lowerCAmelCase						      =-21
				_lowerCAmelCase						      =-24

				@classmethod
				def    _lowerCamelCase       ( cls :  type[T]   ):
										return {unit.name: unit.value for unit in cls if unit.value > 0}





				@classmethod
				def    _lowerCamelCase       ( cls :  type[T]   ):
										return {unit.name: unit.value for unit in cls if unit.value < 0}







def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       List[Any]				      =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												snake_case__							:       Optional[int]				      =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f'''{numerical_part!s} {name_prefix}'''
						return str(lowerCamelCase		)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						for prefix in BinaryUnit:
												snake_case__							:       int				      =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f'''{numerical_part!s} {prefix.name}'''
						return str(lowerCamelCase		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()


