

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort

class __UpperCamelCase     (				_a					,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=0						):
						UpperCAmelCase__:	int									= np.random.RandomState(lowerCamelCase__						)
						UpperCAmelCase__:	List[Any]									= {
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 7.5,
						    "output_type": "numpy",
						}
						return inputs





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	int									= self.get_dummy_inputs()
						UpperCAmelCase__:	Optional[int]									= pipe(**lowerCamelCase__						).images
						UpperCAmelCase__:	Any									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						UpperCAmelCase__:	List[Any]									= np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						UpperCAmelCase__:	str									= PNDMScheduler.from_config(pipe.scheduler.config				,      skip_prk_steps=lowerCamelCase__						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	List[str]									= self.get_dummy_inputs()
						UpperCAmelCase__:	Tuple									= pipe(**lowerCamelCase__						).images
						UpperCAmelCase__:	Union[str, Any]									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						UpperCAmelCase__:	int									= np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						UpperCAmelCase__:	List[Any]									= LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	Union[str, Any]									= self.get_dummy_inputs()
						UpperCAmelCase__:	Tuple									= pipe(**lowerCamelCase__						).images
						UpperCAmelCase__:	str									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						UpperCAmelCase__:	Any									= np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						UpperCAmelCase__:	List[str]									= EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	Optional[Any]									= self.get_dummy_inputs()
						UpperCAmelCase__:	int									= pipe(**lowerCamelCase__						).images
						UpperCAmelCase__:	Optional[Any]									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						UpperCAmelCase__:	Optional[Any]									= np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						UpperCAmelCase__:	Optional[Any]									= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	Dict									= self.get_dummy_inputs()
						UpperCAmelCase__:	List[str]									= pipe(**lowerCamelCase__						).images
						UpperCAmelCase__:	Tuple									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						UpperCAmelCase__:	Dict									= np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						UpperCAmelCase__:	Tuple									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	int									= self.get_dummy_inputs()
						UpperCAmelCase__:	List[Any]									= pipe(**lowerCamelCase__						).images
						UpperCAmelCase__:	int									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						UpperCAmelCase__:	List[Any]									= np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	List[Any]									= self.get_dummy_inputs()
						UpperCAmelCase__:	Dict									= 3 * [inputs["prompt"]]

						# forward
						UpperCAmelCase__:	Tuple									= pipe(**lowerCamelCase__						)
						UpperCAmelCase__:	str									= output.images[0, -3:, -3:, -1]

						UpperCAmelCase__:	int									= self.get_dummy_inputs()
						UpperCAmelCase__:	Union[str, Any]									= 3 * [inputs.pop("prompt"						)]

						UpperCAmelCase__:	int									= pipe.tokenizer(
						    lowerCamelCase__				,      padding="max_length"				,      max_length=pipe.tokenizer.model_max_length				,      truncation=lowerCamelCase__				,      return_tensors="np"				,      )
						UpperCAmelCase__:	Optional[Any]									= text_inputs["input_ids"]

						UpperCAmelCase__:	List[Any]									= pipe.text_encoder(input_ids=text_inputs.astype(np.intaa						)						)[0]

						UpperCAmelCase__:	Dict									= prompt_embeds

						# forward
						UpperCAmelCase__:	int									= pipe(**lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= output.images[0, -3:, -3:, -1]

						assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1e-4





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,      provider="CPUExecutionProvider"						)
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	str									= self.get_dummy_inputs()
						UpperCAmelCase__:	Dict									= 3 * ["this is a negative prompt"]
						UpperCAmelCase__:	Union[str, Any]									= negative_prompt
						UpperCAmelCase__:	List[str]									= 3 * [inputs["prompt"]]

						# forward
						UpperCAmelCase__:	int									= pipe(**lowerCamelCase__						)
						UpperCAmelCase__:	int									= output.images[0, -3:, -3:, -1]

						UpperCAmelCase__:	List[str]									= self.get_dummy_inputs()
						UpperCAmelCase__:	str									= 3 * [inputs.pop("prompt"						)]

						UpperCAmelCase__:	Tuple									= []
						for p in [prompt, negative_prompt]:
							UpperCAmelCase__:	Optional[Any]									= pipe.tokenizer(
							    lowerCamelCase__				,      padding="max_length"				,      max_length=pipe.tokenizer.model_max_length				,      truncation=lowerCamelCase__				,      return_tensors="np"				,      )
							UpperCAmelCase__:	Any									= text_inputs["input_ids"]

							embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa						)						)[0]						)

						UpperCAmelCase__							,						UpperCAmelCase__:	Any									= embeds

						# forward
						UpperCAmelCase__:	Any									= pipe(**lowerCamelCase__						)
						UpperCAmelCase__:	str									= output.images[0, -3:, -3:, -1]

						assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()						).max() < 1e-4

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






					'''simple docstring'''



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





					@property
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= ort.SessionOptions()
						UpperCAmelCase__:	Union[str, Any]									= False
						return options





					def    _UpperCAmelCase    (				self						):
						# using the PNDM scheduler by default
						UpperCAmelCase__:	List[str]									= OnnxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"				,      revision="onnx"				,      safety_checker=lowerCamelCase__				,      feature_extractor=lowerCamelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	int									= "A painting of a squirrel eating a burger"
						np.random.seed(0						)
						UpperCAmelCase__:	Any									= sd_pipe([prompt]				,      guidance_scale=6.0				,      num_inference_steps=1_0				,      output_type="np"						)
						UpperCAmelCase__:	List[str]									= output.images

						UpperCAmelCase__:	str									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						UpperCAmelCase__:	Tuple									= np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= DDIMScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"				,      subfolder="scheduler"				,      revision="onnx"						)
						UpperCAmelCase__:	str									= OnnxStableDiffusionPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"				,      revision="onnx"				,      scheduler=lowerCamelCase__				,      safety_checker=lowerCamelCase__				,      feature_extractor=lowerCamelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	List[Any]									= "open neural network exchange"
						UpperCAmelCase__:	Any									= np.random.RandomState(0						)
						UpperCAmelCase__:	Union[str, Any]									= sd_pipe([prompt]				,      guidance_scale=7.5				,      num_inference_steps=1_0				,      generator=lowerCamelCase__				,      output_type="np"						)
						UpperCAmelCase__:	Tuple									= output.images
						UpperCAmelCase__:	List[Any]									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						UpperCAmelCase__:	List[Any]									= np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= LMSDiscreteScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"				,      subfolder="scheduler"				,      revision="onnx"						)
						UpperCAmelCase__:	str									= OnnxStableDiffusionPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"				,      revision="onnx"				,      scheduler=lowerCamelCase__				,      safety_checker=lowerCamelCase__				,      feature_extractor=lowerCamelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
						sd_pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	List[Any]									= "open neural network exchange"
						UpperCAmelCase__:	str									= np.random.RandomState(0						)
						UpperCAmelCase__:	Any									= sd_pipe([prompt]				,      guidance_scale=7.5				,      num_inference_steps=1_0				,      generator=lowerCamelCase__				,      output_type="np"						)
						UpperCAmelCase__:	Any									= output.images
						UpperCAmelCase__:	Optional[Any]									= image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						UpperCAmelCase__:	int									= np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= 0

						def test_callback_fn(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						) -> None:
							UpperCAmelCase__:	Union[str, Any]									= True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 0:
								assert latents.shape == (1, 4, 6_4, 6_4)
								UpperCAmelCase__:	Optional[Any]									= latents[0, -3:, -3:, -1]
								UpperCAmelCase__:	List[str]									= np.array(
								    [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]						)

								assert np.abs(latents_slice.flatten() - expected_slice						).max() < 1e-3
							elif step == 5:
								assert latents.shape == (1, 4, 6_4, 6_4)
								UpperCAmelCase__:	List[str]									= latents[0, -3:, -3:, -1]
								UpperCAmelCase__:	List[Any]									= np.array(
								    [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]						)

								assert np.abs(latents_slice.flatten() - expected_slice						).max() < 1e-3

						UpperCAmelCase__:	List[Any]									= False

						UpperCAmelCase__:	str									= OnnxStableDiffusionPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"				,      revision="onnx"				,      safety_checker=lowerCamelCase__				,      feature_extractor=lowerCamelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
						pipe.set_progress_bar_config(disable=lowerCamelCase__						)

						UpperCAmelCase__:	str									= "Andromeda galaxy in a bottle"

						UpperCAmelCase__:	str									= np.random.RandomState(0						)
						pipe(
						    prompt=lowerCamelCase__				,      num_inference_steps=5				,      guidance_scale=7.5				,      generator=lowerCamelCase__				,      callback=lowerCamelCase__				,      callback_steps=1				,      )
						assert test_callback_fn.has_been_called
						assert number_of_steps == 6





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= OnnxStableDiffusionPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"				,      revision="onnx"				,      safety_checker=lowerCamelCase__				,      feature_extractor=lowerCamelCase__				,      provider=self.gpu_provider				,      sess_options=self.gpu_options				,      )
						assert isinstance(lowerCamelCase__				,      lowerCamelCase__						)
						assert pipe.safety_checker is None

						UpperCAmelCase__:	Optional[Any]									= pipe("example prompt"				,      num_inference_steps=2						).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(lowerCamelCase__						)
							UpperCAmelCase__:	List[Any]									= OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase__						)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						UpperCAmelCase__:	str									= pipe("example prompt"				,      num_inference_steps=2						).images[0]
						assert image is not None


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	int									= [], []
	while len(SCREAMING_SNAKE_CASE      ) > 1:
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= min(SCREAMING_SNAKE_CASE      ), max(SCREAMING_SNAKE_CASE      )
		start.append(SCREAMING_SNAKE_CASE      )
		end.append(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
	end.reverse()
	return start + collection + end


if __name__ == "__main__":
					_lowerCAmelCase  :      Union[str, Any]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Dict			 =[int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")