

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 	UpperCAmelCase__		(	__A	):






  """simple docstring"""


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






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

       SCREAMING_SNAKE_CASE__   =   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						:		Optional[int]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		List[Any] = None     ,       __lowerCamelCase						:		List[str] = None     ,       __lowerCamelCase						:		List[Any] = None     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		List[str] = True     ,       __lowerCamelCase						:		List[str] = False     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		Optional[Any] = None     ,       __lowerCamelCase						:		List[str] = 0     ,       __lowerCamelCase						:		List[str] = None     ,       __lowerCamelCase						:		Any = None     ,       __lowerCamelCase						:		Tuple = None     ,       __lowerCamelCase						:		Optional[int] = False     ,       __lowerCamelCase						:		Any = False     ,       __lowerCamelCase						:		List[str] = False     ,       __lowerCamelCase						:		List[str] = False     ,       __lowerCamelCase						:		Any = True     ,       __lowerCamelCase						:		Dict = None     ,       **__lowerCamelCase						:		Optional[int]     ,       )		->							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
       SCREAMING_SNAKE_CASE__   =   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 ):
                 SCREAMING_SNAKE_CASE__   =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__   =   features['''words''']

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

       return encoded_inputs






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






  def 				lowercase_		(							self						:		Dict     ,       *__lowerCamelCase						:		List[Any]     ,       **__lowerCamelCase						:		List[Any] )		->							str:
       return self.tokenizer.decode(*__lowercase     ,       **__lowercase )






  @property
  def 				lowercase_		(							self						:		int )		->							Optional[Any]:
       return ["input_ids", "bbox", "attention_mask", "image"]






  @property
  def 				lowercase_		(							self						:		List[str] )		->							Optional[int]:
       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 				lowercase_		(							self						:		Any )		->							Tuple:
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,       __lowercase     ,       )
       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     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     if not arr:
          return None, None, 0
     if low == high:
          return low, high, arr[low]

     SCREAMING_SNAKE_CASE__   =   (low + high) // 2
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   max_subarray(_A				,		_A				,		_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   max_subarray(_A				,		mid + 1				,		_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   float('''-inf'''    ), -1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   float('''-inf'''    ), -1

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

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

     return max_left, max_right, (left_sum + right_sum)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [randint(1				,		_A    ) for _ in range(_A    )]
     SCREAMING_SNAKE_CASE__   =   time.time()
     max_subarray(_A				,		0				,		input_size - 1    )
     SCREAMING_SNAKE_CASE__   =   time.time()
     return end - start
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
     SCREAMING_SNAKE_CASE__   =   [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()


