
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 			__A( a  ,   unittest.TestCase			):
				# FIXME: add fast tests
				pass







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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




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


								'''simple docstring'''




								__a										=    ort.SessionOptions()
								__a										=    False
								return options




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


								'''simple docstring'''




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

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

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




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

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

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

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

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()