





from __future__ import annotations

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

from matplotlib import pyplot as plt



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   tuple[int | None, int | None, float]:


      '''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(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						max_subarray(_SCREAMING_SNAKE_CASE ,						mid + 1 ,						_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						max_cross_sum(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
      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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   tuple[int, int, float]:


      '''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(_SCREAMING_SNAKE_CASE ,						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    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[randint(1 ,						_SCREAMING_SNAKE_CASE	) for _ in range(_SCREAMING_SNAKE_CASE	)]
      SCREAMING_SNAKE_CASE		=						time.time()
      max_subarray(_SCREAMING_SNAKE_CASE ,						0 ,						input_size - 1	)
      SCREAMING_SNAKE_CASE		=						time.time()
      return end - start



def    __lowercase   (      ) ->   None:


      '''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(_SCREAMING_SNAKE_CASE	) for input_size in input_sizes]
      print("""No of Inputs\t\tTime Taken"""	)
      for input_size, runtime in zip(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
            print(_SCREAMING_SNAKE_CASE ,						"""\t\t""" ,						_SCREAMING_SNAKE_CASE	)
      plt.plot(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
      plt.xlabel("""Number of Inputs"""	)
      plt.ylabel("""Time taken in seconds"""	)
      plt.show()


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

      testmod()









import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=3						,out_channels=3						,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,)
           return model






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""						,return_dict=lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE		=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

@slow
@require_torch
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=20						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


