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




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Union[str, Any]:


								'''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.'''		,     _snake_case		,     )
												__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__(_snake_case		,     _snake_case				)




				def __call__(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = True		,     _snake_case = False		,     _snake_case = None		,     _snake_case = None		,     _snake_case = 0		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = False		,     _snake_case = True		,     _snake_case = None		,     **_snake_case		,     )    ->      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=_snake_case		,     return_tensors=_snake_case				)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(_snake_case		,     _snake_case				):
																__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=_snake_case		,     add_special_tokens=_snake_case		,     padding=_snake_case		,     truncation=_snake_case		,     max_length=_snake_case		,     stride=_snake_case		,     pad_to_multiple_of=_snake_case		,     return_token_type_ids=_snake_case		,     return_attention_mask=_snake_case		,     return_overflowing_tokens=_snake_case		,     return_special_tokens_mask=_snake_case		,     return_offsets_mapping=_snake_case		,     return_length=_snake_case		,     verbose=_snake_case		,     return_tensors=_snake_case		,     **_snake_case		,     )

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

								return encoded_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]				)

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

								return images_with_overflow




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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




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


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class




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


								'''simple docstring'''




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

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				if left_sum >= right_sum and left_sum >= cross_sum:
								return left_low, left_high, left_sum
				elif right_sum >= left_sum and right_sum >= cross_sum:
								return right_low, right_high, right_sum
				return cross_left, cross_right, cross_sum


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

				__a										=    0
				for i in range(mid + 1						,    high + 1  ):
								summ += arr[i]
								if summ > right_sum:
												__a										=    summ
												__a										=    i

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()