from random import randint, random






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = False      ,      SCREAMING_SNAKE_CASE = False      ,      SCREAMING_SNAKE_CASE = 5      ,      )->						Any:




      """simple docstring"""

      snake_case_ =   [[-1] * number_of_cells]  # Create a highway without any car
      snake_case_ =   0
      snake_case_ =   max(snake_case__      ,      0      )
      while i < number_of_cells:
            snake_case_ =   (
                randint(0      ,      snake_case__      ) if random_speed else initial_speed
            )  # Place the cars
            i += (
                randint(1      ,      max_speed * 2      ) if random_frequency else frequency
            )  # Arbitrary number, may need tuning
      return highway






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   highway_now[car_index + 1 :]
      for cell in range(len(snake_case__      )      ):  # May need a better name for this
            if cells[cell] != -1:  # If the cell is not empty then
                  return distance  # we have the distance we wanted
            distance += 1
      # Here if the car is near the end of the highway
      return distance + get_distance(snake_case__      ,      -1      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      snake_case_ =   len(snake_case__      )
      # Beforce calculations, the highway is empty
      snake_case_ =   [-1] * number_of_cells

      for car_index in range(snake_case__      ):
            if highway_now[car_index] != -1:
                  # Add 1 to the current speed of the car and cap the speed
                  snake_case_ =   min(highway_now[car_index] + 1      ,      snake_case__      )
                  # Number of empty cell before the next car
                  snake_case_ =   get_distance(snake_case__      ,      snake_case__      ) - 1
                  # We can't have the car causing an accident
                  snake_case_ =   min(next_highway[car_index]      ,      snake_case__      )
                  if random() < probability:
                        # Randomly, a driver will slow down
                        snake_case_ =   max(next_highway[car_index] - 1      ,      0      )
      return next_highway






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   len(highway[0]      )

      for i in range(snake_case__      ):
            snake_case_ =   update(highway[i]      ,      snake_case__      ,      snake_case__      )
            snake_case_ =   [-1] * number_of_cells

            for car_index in range(snake_case__      ):
                  snake_case_ =   next_speeds_calculated[car_index]
                  if speed != -1:
                        # Change the position based on the speed (with % to create the loop)
                        snake_case_ =   (car_index + speed) % number_of_cells
                        # Commit the change of position
                        snake_case_ =   speed
            highway.append(snake_case__      )

      return highway


if __name__ == "__main__":
   import doctest

   doctest.testmod()



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort







class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__  (			self      ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ort.SessionOptions()
       snake_case_ =   False
       return options



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=10	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   subfolder='''scheduler'''	,   revision='''onnx'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   scheduler=_UpperCAmelCase	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=20	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3