




"""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       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[Any]       =      ["image_processor", "tokenizer"]
				__UpperCAmelCase :     Tuple       =      "LayoutLMv2ImageProcessor"
				__UpperCAmelCase :     int       =      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



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

						__lowercase				:		int												=				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					:	int    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   lowercase__					:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,   lowercase__					:	Union[List[List[int]], List[List[List[int]]]] = None    ,   lowercase__					:	Optional[Union[List[int], List[List[int]]]] = None    ,   lowercase__					:	bool = True    ,   lowercase__					:	Union[bool, str, PaddingStrategy] = False    ,   lowercase__					:	Union[bool, str, TruncationStrategy] = None    ,   lowercase__					:	Optional[int] = None    ,   lowercase__					:	int = 0    ,   lowercase__					:	Optional[int] = None    ,   lowercase__					:	Optional[bool] = None    ,   lowercase__					:	Optional[bool] = None    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = False    ,   lowercase__					:	bool = True    ,   lowercase__					:	Optional[Union[str, TensorType]] = None    ,   **lowercase__					:	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
						__lowercase				:		Any												=				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__						):
										__lowercase				:		List[str]												=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
								__lowercase				:		List[str]												=				features["words"]

						__lowercase				:		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=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
						__lowercase				:		Optional[Any]												=				features.pop("pixel_values"						)
						if return_overflowing_tokens is True:
								__lowercase				:		Any												=				self.get_overflowing_images(lowercase__    ,   encoded_inputs["overflow_to_sample_mapping"]						)
						__lowercase				:		Union[str, Any]												=				images

						return encoded_inputs



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	str    ,   lowercase__					:	Dict						):
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						__lowercase				:		Any												=				[]
						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       snake_case    (     self					:	Optional[Any]    ,   *lowercase__					:	Dict    ,   **lowercase__					:	str						):
						return self.tokenizer.batch_decode(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Tuple    ,   *lowercase__					:	Tuple    ,   **lowercase__					:	Dict						):
						return self.tokenizer.decode(*lowercase__    ,   **lowercase__						)



				@property
				def       snake_case    (     self					:	str						):
						return ["input_ids", "bbox", "attention_mask", "image"]



				@property
				def       snake_case    (     self					:	Any						):
						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       snake_case    (     self					:	int						):
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,   lowercase__    ,   )
						return self.image_processor






"""simple docstring"""







from collections import deque

from .hash_table import HashTable




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	Optional[int]    ,   *lowercase__					:	Any    ,   **lowercase__					:	Union[str, Any]						):
						super().__init__(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	Dict    ,   lowercase__					:	Dict						):
						__lowercase				:		Dict												=				deque([]						) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.values[key]



				def       snake_case    (     self					:	str						):
						return (
						    sum(self.charge_factor - len(lowercase__						) for slot in self.values						)
						    / self.size_table
						    * self.charge_factor
						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str=None						):
						if not (
						    len(self.values[key]						) == self.charge_factor and self.values.count(lowercase__						) == 0
						):
								return key
						return super()._collision_resolution(lowercase__    ,   lowercase__						)
