


"""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 snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      ['image_processor', 'tokenizer']
		__UpperCamelCase							     =      'LayoutLMv2ImageProcessor'
		__UpperCamelCase							     =      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')







		def __init__(       self   :							Dict     , a__   :							Optional[int]=None     , a__   :							Union[str, Any]=None     , **a__   :							str		)							->    List[str]:





						'''simple docstring'''







						_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."     , a__     , )
										_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__(a__     , a__		)







		def __call__(       self   :							List[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__   :							Optional[Any]     , )							->    BatchEncoding:





						'''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
						_A       	=      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       	=      [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=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       	=      features.pop("pixel_values"		)
						if return_overflowing_tokens is True:
										_A       	=      self.get_overflowing_images(a__     , encoded_inputs["overflow_to_sample_mapping"]		)
						_A       	=      images

						return encoded_inputs







		def 			a_			(       self   :							List[str]     , a__   :							Tuple     , a__   :							List[Any]		)							->    int:





						'''simple docstring'''







						_A       	=      []
						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 			a_			(       self   :							List[str]     , *a__   :							Dict     , **a__   :							Union[str, Any]		)							->    Tuple:





						'''simple docstring'''







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







		def 			a_			(       self   :							str     , *a__   :							List[Any]     , **a__   :							Optional[int]		)							->    Tuple:





						'''simple docstring'''







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







		@property
		def 			a_			(       self   :							Any		)							->    Optional[Any]:





						'''simple docstring'''







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







		@property
		def 			a_			(       self   :							Union[str, Any]		)							->    Union[str, Any]:





						'''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 			a_			(       self   :							Dict		)							->    List[str]:





						'''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