






'''simple docstring'''
from __future__ import annotations

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

from matplotlib import pyplot as plt




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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

   lowercase_	:    str           =     (low + high) // 2
   lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     max_subarray(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_    ,		lowercase_    ,		lowercase_	:    Union[str, Any]           =     max_subarray(_UpperCamelCase						,		mid + 1						,		_UpperCamelCase							)
   lowercase_    ,		lowercase_    ,		lowercase_	:    str           =     max_cross_sum(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     float("-inf"							), -1
   lowercase_    ,		lowercase_	:    Dict           =     float("-inf"							), -1

   lowercase_	:    int | float           =     0
   for i in range(_UpperCamelCase						,		low - 1						,		-1							):
      summ += arr[i]
      if summ > left_sum:
         lowercase_	:    int           =     summ
         lowercase_	:    Union[str, Any]           =     i

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

   return max_left, max_right, (left_sum + right_sum)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Tuple           =     [randint(1						,		_UpperCamelCase							) for _ in range(_UpperCamelCase							)]
   lowercase_	:    List[str]           =     time.time()
   max_subarray(_UpperCamelCase						,		0						,		input_size - 1							)
   lowercase_	:    Optional[int]           =     time.time()
   return end - start




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
   lowercase_	:    Tuple           =     [time_max_subarray(_UpperCamelCase							) for input_size in input_sizes]
   print("No of Inputs\t\tTime Taken"							)
   for input_size, runtime in zip(_UpperCamelCase						,		_UpperCamelCase							):
      print(_UpperCamelCase						,		"\t\t"						,		_UpperCamelCase							)
   plt.plot(_UpperCamelCase						,		_UpperCamelCase							)
   plt.xlabel("Number of Inputs"							)
   plt.ylabel("Time taken in seconds"							)
   plt.show()


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

 testmod()












'''simple docstring'''
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						(		snake_case			):
    __lowerCamelCase:   str		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   Dict		       =    'Pix2StructImageProcessor'
    __lowerCamelCase:   Union[str, Any]		       =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(			self  :       str  ,     a  :       Dict  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     False
       super().__init__(a  ,     a			)




    def __call__(			self  :       Tuple  ,     a  :       int=None  ,     a  :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     a  :       bool = True  ,     a  :       Union[bool, str, PaddingStrategy] = False  ,     a  :       Union[bool, str, TruncationStrategy] = None  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = 2_0_4_8  ,     a  :       int = 0  ,     a  :       Optional[int] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = True  ,     a  :       Optional[Union[str, TensorType]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       if images is None and text is None:
          raise ValueError("You have to specify either images or text."			)

       # Get only text
       if images is None and not self.image_processor.is_vqa:
          lowercase_	:    Dict           =     self.tokenizer
          lowercase_	:    Tuple           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )
          return text_encoding

       if not self.image_processor.is_vqa:
          # add pixel_values
          lowercase_	:    Optional[int]           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     **a			)
       else:
          # add pixel_values and bbox
          lowercase_	:    Any           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     header_text=a  ,     **a			)

       if text is not None and not self.image_processor.is_vqa:
          lowercase_	:    int           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )

          if "attention_mask" in text_encoding:
             lowercase_	:    str           =     text_encoding.pop("attention_mask"			)
          if "input_ids" in text_encoding:
             lowercase_	:    Dict           =     text_encoding.pop("input_ids"			)
       else:
          lowercase_	:    str           =     None

       if text_encoding is not None:
          encoding_image_processor.update(a			)

       return encoding_image_processor




    def 				lowerCAmelCase__				(			self  :       Any  ,     *a  :       str  ,     **a  :       Tuple			):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       str  ,     *a  :       Optional[int]  ,     **a  :       Any			):






       '''simple docstring'''






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





    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.tokenizer.model_input_names
       lowercase_	:    Dict           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




