"""simple docstring"""
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 		snake_case (				self    ):






          """simple docstring"""

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

    @property
    def 		snake_case (				self    ):






          """simple docstring"""

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

    def 		snake_case (				self    ):






          """simple docstring"""

          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=lowerCAmelCase		,    feature_extractor=lowerCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
          pipe.set_progress_bar_config(disable=lowerCAmelCase    )

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

          snake_case								=			np.random.RandomState(0    )
          snake_case								=			pipe(
              prompt=lowerCAmelCase		,    image=lowerCAmelCase		,    mask_image=lowerCAmelCase		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=lowerCAmelCase		,    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([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]    )

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

    def 		snake_case (				self    ):






          """simple docstring"""

          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=lowerCAmelCase		,    safety_checker=lowerCAmelCase		,    feature_extractor=lowerCAmelCase		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
          pipe.set_progress_bar_config(disable=lowerCAmelCase    )

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

          snake_case								=			np.random.RandomState(0    )
          snake_case								=			pipe(
              prompt=lowerCAmelCase		,    image=lowerCAmelCase		,    mask_image=lowerCAmelCase		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=lowerCAmelCase		,    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([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]    )

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



"""simple docstring"""
class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				pass





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[
										    [],
										    [],
										    [],
										]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										try:
																if len(self.queues[priority]    ) >= 1_00:
																						raise OverflowError('Maximum queue size is 100'    )
																self.queues[priority].append(lowerCAmelCase    )
										except IndexError:
																raise ValueError('Valid priorities are 0, 1, and 2'    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										for queue in self.queues:
																if queue:
																						return queue.pop(0    )
										raise UnderFlowError('All queues are empty'    )

				def __str__(				self    ):






										"""simple docstring"""

										return "\n".join(F"""Priority {i}: {q}""" for i, q in enumerate(self.queues    )    )





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			[]

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if len(self.queue    ) == 1_00:
																raise OverFlowError('Maximum queue size is 100'    )
										self.queue.append(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										if not self.queue:
																raise UnderFlowError('The queue is empty'    )
										else:
																snake_case								=			min(self.queue    )
																self.queue.remove(lowerCAmelCase    )
																return data

				def __str__(				self    ):






										"""simple docstring"""

										return str(self.queue    )





def    lowerCAmelCase__    (			)					->				int:





						"""simple docstring"""






						snake_case								=			FixedPriorityQueue()
						fpq.enqueue(0 ,			1_0					)
						fpq.enqueue(1 ,			7_0					)
						fpq.enqueue(0 ,			1_0_0					)
						fpq.enqueue(2 ,			1					)
						fpq.enqueue(2 ,			5					)
						fpq.enqueue(1 ,			7					)
						fpq.enqueue(2 ,			4					)
						fpq.enqueue(1 ,			6_4					)
						fpq.enqueue(0 ,			1_2_8					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(_UpperCamelCase					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)
						print(fpq.dequeue()					)





def    lowerCAmelCase__    (			)					->				List[str]:





						"""simple docstring"""






						snake_case								=			ElementPriorityQueue()
						epq.enqueue(1_0					)
						epq.enqueue(7_0					)
						epq.enqueue(1_0_0					)
						epq.enqueue(1					)
						epq.enqueue(5					)
						epq.enqueue(7					)
						epq.enqueue(4					)
						epq.enqueue(6_4					)
						epq.enqueue(1_2_8					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(_UpperCamelCase					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)
						print(epq.dequeue()					)


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()


