




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     ):
		__snake_case									=   ['image_processor', 'tokenizer']
		__snake_case									=   'LayoutLMv2ImageProcessor'
		__snake_case									=   ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')



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







								"""simple docstring"""



								if "feature_extractor" in kwargs:
														warnings.warn(
														    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
														    ''' instead.''',	UpperCamelCase__,	)
														lowerCAmelCase_      			=  kwargs.pop('''feature_extractor'''   )

								lowerCAmelCase_      			=  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,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = True,	UpperCamelCase__ = False,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = 0,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = False,	UpperCamelCase__ = True,	UpperCamelCase__ = None,	**UpperCamelCase__,	):







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

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

								return encoded_inputs



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  []
								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        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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










def __UpperCamelCase			(       _A  ):
						if not numbers:
												return 0

						if not isinstance(_A ,  (list, tuple)  ) or not all(
						    isinstance(_A ,  _A  ) for number in numbers  ):
												raise ValueError('''numbers must be an iterable of integers'''  )

						lowerCAmelCase_      			=  lowerCAmelCase_      			=  lowerCAmelCase_      			=  numbers[0]

						for i in range(1 ,  len(_A  )  ):
												# update the maximum and minimum subarray products
												lowerCAmelCase_      			=  numbers[i]
												if number < 0:
																		lowerCAmelCase_			,					lowerCAmelCase_      			=  min_till_now, max_till_now
												lowerCAmelCase_      			=  max(_A ,  max_till_now * number  )
												lowerCAmelCase_      			=  min(_A ,  min_till_now * number  )

												# update the maximum product found till now
												lowerCAmelCase_      			=  max(_A ,  _A  )

						return max_prod




