



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__	(						UpperCamelCase    ):
					__A  :     Union[str, Any]     =    ['image_processor', 'tokenizer']
					__A  :     Any     =    'LayoutLMv2ImageProcessor'
					__A  :     Dict     =    ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')







					def __init__(							self   :  Tuple      ,		_A   :  int=None      ,		_A   :  Union[str, Any]=None      ,		**_A   :  Dict):
											A__						:				List[str]         =			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																	    " instead."      ,		_A      ,		)
																	A__						:				Dict         =			kwargs.pop("feature_extractor")

											A__						:				Optional[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__(_A      ,		_A)







					def __call__(							self   :  Union[str, Any]      ,		_A   :  Dict      ,		_A   :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A   :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A   :  Union[List[List[int]], List[List[List[int]]]] = None      ,		_A   :  Optional[Union[List[int], List[List[int]]]] = None      ,		_A   :  bool = True      ,		_A   :  Union[bool, str, PaddingStrategy] = False      ,		_A   :  Union[bool, str, TruncationStrategy] = False      ,		_A   :  Optional[int] = None      ,		_A   :  int = 0      ,		_A   :  Optional[int] = None      ,		_A   :  Optional[bool] = None      ,		_A   :  Optional[bool] = None      ,		_A   :  bool = False      ,		_A   :  bool = False      ,		_A   :  bool = False      ,		_A   :  bool = False      ,		_A   :  bool = True      ,		_A   :  Optional[Union[str, TensorType]] = None      ,		**_A   :  Union[str, Any]      ,		):
											# 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__						:				List[Any]         =			self.image_processor(images=_A      ,		return_tensors=_A)

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

											A__						:				Dict         =			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=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

											# add pixel values
											A__						:				List[Any]         =			features.pop("pixel_values")
											if return_overflowing_tokens is True:
																	A__						:				int         =			self.get_overflowing_images(_A      ,		encoded_inputs["overflow_to_sample_mapping"])
											A__						:				Dict         =			images

											return encoded_inputs







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Optional[int]      ,		_A   :  Optional[int]):
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											A__						:				int         =			[]
											for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx])

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

											return images_with_overflow







					def 			_lowercase      (							self   :  Optional[Any]      ,		*_A   :  Tuple      ,		**_A   :  Any):
											return self.tokenizer.batch_decode(*_A      ,		**_A)







					def 			_lowercase      (							self   :  Optional[int]      ,		*_A   :  str      ,		**_A   :  Union[str, Any]):
											return self.tokenizer.decode(*_A      ,		**_A)







					@property
					def 			_lowercase      (							self   :  Tuple):
											return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







					@property
					def 			_lowercase      (							self   :  str):
											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,		_A      ,		)
											return self.image_processor_class







					@property
					def 			_lowercase      (							self   :  List[str]):
											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,		_A      ,		)
											return self.image_processor




def 						snake_case__							(       __lowercase							)		->		int:







						"""simple docstring"""

						A__						:				Any         =			0
						for plain_chr in plain_text:
												A__						:				List[str]         =			(
												    ord(__lowercase							) + (hash_value << 6) + (hash_value << 1_6) - hash_value
												)
						return hash_value