






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	(     _UpperCamelCase					):





				"""simple docstring"""







				UpperCamelCase__					: Optional[int]     					=["""image_processor""", """tokenizer"""]
				UpperCamelCase__					: Union[str, Any]     					="""LayoutLMv2ImageProcessor"""
				UpperCamelCase__					: str     					=("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


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




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=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__				,    )
														__UpperCamelCase :   Union[str, Any]									=kwargs.pop('feature_extractor'       )

									__UpperCamelCase :   str									=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				,    lowerCamelCase__				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = True				,    lowerCamelCase__ = False				,    lowerCamelCase__ = False				,    lowerCamelCase__ = None				,    lowerCamelCase__ = 0				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = False				,    lowerCamelCase__ = False				,    lowerCamelCase__ = False				,    lowerCamelCase__ = False				,    lowerCamelCase__ = True				,    lowerCamelCase__ = None				,    **lowerCamelCase__				,    ):




									"""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 :   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__       ):
																			__UpperCamelCase :   Tuple									=[text]  # add batch dimension (as the image processor always adds a batch dimension)
														__UpperCamelCase :   int									=features['words']

									__UpperCamelCase :   List[str]									=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
									__UpperCamelCase :   List[Any]									=features.pop('pixel_values'       )
									if return_overflowing_tokens is True:
														__UpperCamelCase :   Union[str, Any]									=self.get_overflowing_images(a__				,    encoded_inputs['overflow_to_sample_mapping']       )
									__UpperCamelCase :   List[Any]									=images

									return encoded_inputs


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=[]
									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				,    *lowerCamelCase__				,    **lowerCamelCase__       ):




									"""simple docstring"""

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


				def  __lowercase				(					self				,    *lowerCamelCase__				,    **lowerCamelCase__       ):




									"""simple docstring"""

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


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

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


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									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       ):




									"""simple docstring"""

									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,    a__				,    )
									return self.image_processor





"""simple docstring"""


from .hash_table import HashTable
class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Any     , *a__   :							Any     , **a__   :							Tuple		)							->    Tuple:





						'''simple docstring'''







						super().__init__(*a__     , **a__		)







		def 			a_			(       self   :							Tuple     , a__   :							int     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      1
						_A       	=      self.hash_function(key + i * i		)

						while self.values[new_key] is not None and self.values[new_key] != key:
										i += 1
										_A       	=      (
										    self.hash_function(key + i * i		)
										    if not self.balanced_factor() >= self.lim_charge
										    else None
										)

										if new_key is None:
														break

						return new_key