'''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       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   # FIXME: add fast tests
   pass



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






   """simple docstring"""
   @property
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					ort.SessionOptions()
        lowercase_					:       Tuple     				=					False
        return options
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       Tuple     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
        lowercase_					:       Optional[int]     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
        lowercase_					:       int     				=					OnnxStableDiffusionInpaintPipeline.from_pretrained(
            'runwayml/stable-diffusion-inpainting'       ,				revision='onnx'       ,				safety_checker=_a       ,				feature_extractor=_a       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       int     				=					'A red cat sitting on a park bench'

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

        assert images.shape == (1, 512, 512, 3)
        lowercase_					:       List[Any]     				=					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 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       int     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
        lowercase_					:       str     				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
        lowercase_					:       str     				=					LMSDiscreteScheduler.from_pretrained(
            'runwayml/stable-diffusion-inpainting'       ,				subfolder='scheduler'       ,				revision='onnx'				)
        lowercase_					:       Optional[int]     				=					OnnxStableDiffusionInpaintPipeline.from_pretrained(
            'runwayml/stable-diffusion-inpainting'       ,				revision='onnx'       ,				scheduler=_a       ,				safety_checker=_a       ,				feature_extractor=_a       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
        pipe.set_progress_bar_config(disable=_a				)

        lowercase_					:       List[str]     				=					'A red cat sitting on a park bench'

        lowercase_					:       Dict     				=					np.random.RandomState(0				)
        lowercase_					:       Tuple     				=					pipe(
            prompt=_a       ,				image=_a       ,				mask_image=_a       ,				guidance_scale=7.5       ,				num_inference_steps=20       ,				generator=_a       ,				output_type='np'       ,				)
        lowercase_					:       Optional[Any]     				=					output.images
        lowercase_					:       Union[str, Any]     				=					images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowercase_					:       List[str]     				=					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       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					[
            [],
            [],
            [],
        ]
   def 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     None:
        try:
             if len(self.queues[priority]				) >= 100:
                  raise OverflowError('Maximum queue size is 100'				)
             self.queues[priority].append(_a				)
        except IndexError:
             raise ValueError('Valid priorities are 0, 1, and 2'				)
   def 		_lowerCamelCase (self				)    ->     int:
        for queue in self.queues:
             if queue:
                  return queue.pop(0				)
        raise UnderFlowError('All queues are empty'				)





   def __str__(self				)    ->     str:
        return "\n".join(f'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     str:
        lowercase_					:       List[Any]     				=					[]
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        if len(self.queue				) == 100:
             raise OverFlowError('Maximum queue size is 100'				)
        self.queue.append(_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        if not self.queue:
             raise UnderFlowError('The queue is empty'				)
        else:
             lowercase_					:       Tuple     				=					min(self.queue				)
             self.queue.remove(_a				)
             return data





   def __str__(self				)    ->     str:
        return str(self.queue				)



def _UpperCamelCase   (       ):
     lowercase_					:       Any     				=					FixedPriorityQueue()
     fpq.enqueue(0  ,       10							)
     fpq.enqueue(1  ,       70							)
     fpq.enqueue(0  ,       100							)
     fpq.enqueue(2  ,       1							)
     fpq.enqueue(2  ,       5							)
     fpq.enqueue(1  ,       7							)
     fpq.enqueue(2  ,       4							)
     fpq.enqueue(1  ,       64							)
     fpq.enqueue(0  ,       128							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)

def _UpperCamelCase   (       ):
     lowercase_					:       Optional[int]     				=					ElementPriorityQueue()
     epq.enqueue(10							)
     epq.enqueue(70							)
     epq.enqueue(100							)
     epq.enqueue(1							)
     epq.enqueue(5							)
     epq.enqueue(7							)
     epq.enqueue(4							)
     epq.enqueue(64							)
     epq.enqueue(128							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)


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




