


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 	A_ (	snake_case__ ,				unittest.TestCase   ):




						'''simple docstring'''


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




						def 					_snake_case     (    self:	Union[str, Any]						,				a:	List[Any]=0    ):
							__lowerCamelCase		:							Optional[Any]		 =						np.random.RandomState(_A    )
							__lowerCamelCase		:							int		 =						{
							    '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 					_snake_case     (    self:	Dict    ):
							__lowerCamelCase		:							Union[str, Any]		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Dict		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							Optional[Any]		 =						pipe(**_A    ).images
							__lowerCamelCase		:							str		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							__lowerCamelCase		:							Union[str, Any]		 =						np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]    )

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




						def 					_snake_case     (    self:	Union[str, Any]    ):
							__lowerCamelCase		:							Union[str, Any]		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							__lowerCamelCase		:							int		 =						PNDMScheduler.from_config(pipe.scheduler.config						,				skip_prk_steps=_A    )
							pipe.set_progress_bar_config(disable=_A    )

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

							assert image.shape == (1, 128, 128, 3)
							__lowerCamelCase		:							Union[str, Any]		 =						np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]    )

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




						def 					_snake_case     (    self:	List[str]    ):
							__lowerCamelCase		:							List[str]		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							__lowerCamelCase		:							Optional[Any]		 =						LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							List[str]		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							List[Any]		 =						pipe(**_A    ).images
							__lowerCamelCase		:							List[str]		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							__lowerCamelCase		:							Dict		 =						np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

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




						def 					_snake_case     (    self:	Tuple    ):
							__lowerCamelCase		:							str		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							__lowerCamelCase		:							Optional[int]		 =						EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=_A    )

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

							assert image.shape == (1, 128, 128, 3)
							__lowerCamelCase		:							List[Any]		 =						np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

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




						def 					_snake_case     (    self:	Optional[Any]    ):
							__lowerCamelCase		:							List[str]		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							__lowerCamelCase		:							List[Any]		 =						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Any		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							Any		 =						pipe(**_A    ).images
							__lowerCamelCase		:							List[str]		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							__lowerCamelCase		:							List[Any]		 =						np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]    )

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




						def 					_snake_case     (    self:	Dict    ):
							__lowerCamelCase		:							Any		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							__lowerCamelCase		:							Optional[Any]		 =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Tuple		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							Dict		 =						pipe(**_A    ).images
							__lowerCamelCase		:							Optional[Any]		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							__lowerCamelCase		:							List[str]		 =						np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]    )

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




						def 					_snake_case     (    self:	List[Any]    ):
							__lowerCamelCase		:							Optional[Any]		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Dict		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							List[str]		 =						3 * [inputs['prompt']]

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

							__lowerCamelCase		:							int		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							Optional[Any]		 =						3 * [inputs.pop('prompt'    )]

							__lowerCamelCase		:							List[str]		 =						pipe.tokenizer(
							    _A						,				padding='max_length'						,				max_length=pipe.tokenizer.model_max_length						,				truncation=_A						,				return_tensors='np'						,				)
							__lowerCamelCase		:							Union[str, Any]		 =						text_inputs['input_ids']

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

							__lowerCamelCase		:							Tuple		 =						prompt_embeds

							# forward
							__lowerCamelCase		:							Optional[int]		 =						pipe(**_A    )
							__lowerCamelCase		:							Union[str, Any]		 =						output.images[0, -3:, -3:, -1]

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




						def 					_snake_case     (    self:	Dict    ):
							__lowerCamelCase		:							str		 =						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,				provider='CPUExecutionProvider'    )
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							List[str]		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							List[str]		 =						3 * ['this is a negative prompt']
							__lowerCamelCase		:							Optional[Any]		 =						negative_prompt
							__lowerCamelCase		:							Any		 =						3 * [inputs['prompt']]

							# forward
							__lowerCamelCase		:							Optional[int]		 =						pipe(**_A    )
							__lowerCamelCase		:							List[str]		 =						output.images[0, -3:, -3:, -1]

							__lowerCamelCase		:							List[str]		 =						self.get_dummy_inputs()
							__lowerCamelCase		:							Any		 =						3 * [inputs.pop('prompt'    )]

							__lowerCamelCase		:							Union[str, Any]		 =						[]
							for p in [prompt, negative_prompt]:
								__lowerCamelCase		:							Any		 =						pipe.tokenizer(
								    _A						,				padding='max_length'						,				max_length=pipe.tokenizer.model_max_length						,				truncation=_A						,				return_tensors='np'						,				)
								__lowerCamelCase		:							List[str]		 =						text_inputs['input_ids']

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

							__lowerCamelCase ,					__lowerCamelCase		:							Tuple		 =						embeds

							# forward
							__lowerCamelCase		:							Optional[Any]		 =						pipe(**_A    )
							__lowerCamelCase		:							Union[str, Any]		 =						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 	A_ (	unittest.TestCase   ):




						'''simple docstring'''


						@property
						def 					_snake_case     (    self:	Optional[Any]    ):
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)




						@property
						def 					_snake_case     (    self:	Any    ):
							__lowerCamelCase		:							Optional[Any]		 =						ort.SessionOptions()
							__lowerCamelCase		:							str		 =						False
							return options




						def 					_snake_case     (    self:	int    ):
							__lowerCamelCase		:							int		 =						OnnxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=_A						,				feature_extractor=_A						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
							sd_pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Tuple		 =						'A painting of a squirrel eating a burger'
							np.random.seed(0    )
							__lowerCamelCase		:							Union[str, Any]		 =						sd_pipe([prompt]						,				guidance_scale=6.0						,				num_inference_steps=10						,				output_type='np'    )
							__lowerCamelCase		:							Optional[int]		 =						output.images

							__lowerCamelCase		:							List[Any]		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							__lowerCamelCase		:							Dict		 =						np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]    )

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




						def 					_snake_case     (    self:	Optional[Any]    ):
							__lowerCamelCase		:							str		 =						DDIMScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'						,				subfolder='scheduler'						,				revision='onnx'    )
							__lowerCamelCase		:							List[Any]		 =						OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'						,				revision='onnx'						,				scheduler=_A						,				safety_checker=_A						,				feature_extractor=_A						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
							sd_pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Optional[Any]		 =						'open neural network exchange'
							__lowerCamelCase		:							Optional[int]		 =						np.random.RandomState(0    )
							__lowerCamelCase		:							Tuple		 =						sd_pipe([prompt]						,				guidance_scale=7.5						,				num_inference_steps=10						,				generator=_A						,				output_type='np'    )
							__lowerCamelCase		:							int		 =						output.images
							__lowerCamelCase		:							List[Any]		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							__lowerCamelCase		:							Any		 =						np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]    )

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




						def 					_snake_case     (    self:	Tuple    ):
							__lowerCamelCase		:							Union[str, Any]		 =						LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'						,				subfolder='scheduler'						,				revision='onnx'    )
							__lowerCamelCase		:							Union[str, Any]		 =						OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'						,				revision='onnx'						,				scheduler=_A						,				safety_checker=_A						,				feature_extractor=_A						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
							sd_pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Union[str, Any]		 =						'open neural network exchange'
							__lowerCamelCase		:							Tuple		 =						np.random.RandomState(0    )
							__lowerCamelCase		:							Optional[Any]		 =						sd_pipe([prompt]						,				guidance_scale=7.5						,				num_inference_steps=10						,				generator=_A						,				output_type='np'    )
							__lowerCamelCase		:							int		 =						output.images
							__lowerCamelCase		:							List[Any]		 =						image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							__lowerCamelCase		:							List[Any]		 =						np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]    )

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




						def 					_snake_case     (    self:	Optional[int]    ):
							__lowerCamelCase		:							str		 =						0

							def test_callback_fn(a:	Union[str, Any]						,				a:	List[str]						,				a:	Optional[int]    ) -> None:
								__lowerCamelCase		:							str		 =						True
								nonlocal number_of_steps
								number_of_steps += 1
								if step == 0:
									assert latents.shape == (1, 4, 64, 64)
									__lowerCamelCase		:							Any		 =						latents[0, -3:, -3:, -1]
									__lowerCamelCase		:							Any		 =						np.array(
									    [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]    )

									assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1e-3
								elif step == 5:
									assert latents.shape == (1, 4, 64, 64)
									__lowerCamelCase		:							Optional[int]		 =						latents[0, -3:, -3:, -1]
									__lowerCamelCase		:							Tuple		 =						np.array(
									    [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]    )

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

							__lowerCamelCase		:							str		 =						False

							__lowerCamelCase		:							Dict		 =						OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'						,				revision='onnx'						,				safety_checker=_A						,				feature_extractor=_A						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
							pipe.set_progress_bar_config(disable=_A    )

							__lowerCamelCase		:							Union[str, Any]		 =						'Andromeda galaxy in a bottle'

							__lowerCamelCase		:							List[Any]		 =						np.random.RandomState(0    )
							pipe(
							    prompt=_A						,				num_inference_steps=5						,				guidance_scale=7.5						,				generator=_A						,				callback=_A						,				callback_steps=1						,				)
							assert test_callback_fn.has_been_called
							assert number_of_steps == 6




						def 					_snake_case     (    self:	str    ):
							__lowerCamelCase		:							Any		 =						OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'						,				revision='onnx'						,				safety_checker=_A						,				feature_extractor=_A						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
							assert isinstance(_A						,				_A    )
							assert pipe.safety_checker is None

							__lowerCamelCase		:							Optional[int]		 =						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(_A    )
								__lowerCamelCase		:							Dict		 =						OnnxStableDiffusionPipeline.from_pretrained(_A    )

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





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						prime_factors(SCREAMING_SNAKE_CASE__						)
 if is_square_free(SCREAMING_SNAKE_CASE__						):
  return -1 if len(SCREAMING_SNAKE_CASE__						) % 2 else 1
 return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

