



"""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 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """tokenizer"""]
			UpperCamelCase__			      =	"""LayoutLMv2ImageProcessor"""
			UpperCamelCase__			      =	("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



			def __init__(		self     :    Any				,   __UpperCamelCase     :    Union[str, Any]=None				,   __UpperCamelCase     :    Any=None				,   **__UpperCamelCase     :    Any   )->str:
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,   __UpperCamelCase				,   )
											_UpperCAmelCase					=    kwargs.pop('''feature_extractor'''   )

							_UpperCAmelCase					=    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__(__UpperCamelCase				,   __UpperCamelCase   )



			def __call__(		self     :    List[Any]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,   __UpperCamelCase     :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,   __UpperCamelCase     :    Union[List[List[int]], List[List[List[int]]]] = None				,   __UpperCamelCase     :    Optional[Union[List[int], List[List[int]]]] = None				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Union[bool, str, PaddingStrategy] = False				,   __UpperCamelCase     :    Union[bool, str, TruncationStrategy] = None				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Optional[Union[str, TensorType]] = None				,   **__UpperCamelCase     :    Union[str, Any]				,   )->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
							_UpperCAmelCase					=    self.image_processor(images=__UpperCamelCase				,   return_tensors=__UpperCamelCase   )

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
															_UpperCAmelCase					=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
											_UpperCAmelCase					=    features['''words''']

							_UpperCAmelCase					=    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=__UpperCamelCase				,   add_special_tokens=__UpperCamelCase				,   padding=__UpperCamelCase				,   truncation=__UpperCamelCase				,   max_length=__UpperCamelCase				,   stride=__UpperCamelCase				,   pad_to_multiple_of=__UpperCamelCase				,   return_token_type_ids=__UpperCamelCase				,   return_attention_mask=__UpperCamelCase				,   return_overflowing_tokens=__UpperCamelCase				,   return_special_tokens_mask=__UpperCamelCase				,   return_offsets_mapping=__UpperCamelCase				,   return_length=__UpperCamelCase				,   verbose=__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase				,   )

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

							return encoded_inputs



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

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

							return images_with_overflow



			def  lowercase__						(		self     :    Any				,   *__UpperCamelCase     :    Union[str, Any]				,   **__UpperCamelCase     :    List[str]   )->Union[str, Any]:
							return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple				,   *__UpperCamelCase     :    List[str]				,   **__UpperCamelCase     :    List[str]   )->Optional[Any]:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



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



			@property
			def  lowercase__						(		self     :    Any   )->List[str]:
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor_class



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








"""simple docstring"""


class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    list[int]   )->None:
							_UpperCAmelCase					=    len(__UpperCamelCase   )
							_UpperCAmelCase					=    [0] * len_array

							if len_array > 0:
											_UpperCAmelCase					=    array[0]

							for i in range(1				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    self.prefix_sum[i - 1] + array[i]



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    int   )->int:
							if start == 0:
											return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    int   )->bool:
							_UpperCAmelCase					=    {0}
							for sum_item in self.prefix_sum:
											if sum_item - target_sum in sums:
															return True

											sums.add(__UpperCamelCase   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()



