


'''simple docstring'''





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__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Optional[int]					      =			'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Any=0	)      ->		List[str]:
						__A=	np.random.RandomState(lowerCAmelCase_	)
						__A=	{
						    '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 					lowerCAmelCase     (  self							:			List[str]	)      ->		Tuple:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 128, 128, 3)
						__A=	np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						__A=	PNDMScheduler.from_config(pipe.scheduler.config			,    skip_prk_steps=lowerCAmelCase_	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 128, 128, 3)
						__A=	np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		str:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						__A=	LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 128, 128, 3)
						__A=	np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			int	)      ->		Tuple:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						__A=	EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 128, 128, 3)
						__A=	np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[str]:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						__A=	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 128, 128, 3)
						__A=	np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Dict:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						__A=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	pipe(**lowerCAmelCase_	).images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 128, 128, 3)
						__A=	np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	3 * [inputs['prompt']]

						# forward
						__A=	pipe(**lowerCAmelCase_	)
						__A=	output.images[0, -3:, -3:, -1]

						__A=	self.get_dummy_inputs()
						__A=	3 * [inputs.pop('prompt'	)]

						__A=	pipe.tokenizer(
						    lowerCAmelCase_			,    padding='max_length'			,    max_length=pipe.tokenizer.model_max_length			,    truncation=lowerCAmelCase_			,    return_tensors='np'			,    )
						__A=	text_inputs['input_ids']

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

						__A=	prompt_embeds

						# forward
						__A=	pipe(**lowerCAmelCase_	)
						__A=	output.images[0, -3:, -3:, -1]

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

			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Any:
						__A=	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,    provider='CPUExecutionProvider'	)
						pipe.set_progress_bar_config(disable=lowerCAmelCase_	)

						__A=	self.get_dummy_inputs()
						__A=	3 * ['this is a negative prompt']
						__A=	negative_prompt
						__A=	3 * [inputs['prompt']]

						# forward
						__A=	pipe(**lowerCAmelCase_	)
						__A=	output.images[0, -3:, -3:, -1]

						__A=	self.get_dummy_inputs()
						__A=	3 * [inputs.pop('prompt'	)]

						__A=	[]
						for p in [prompt, negative_prompt]:
									__A=	pipe.tokenizer(
									    lowerCAmelCase_			,    padding='max_length'			,    max_length=pipe.tokenizer.model_max_length			,    truncation=lowerCAmelCase_			,    return_tensors='np'			,    )
									__A=	text_inputs['input_ids']

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

						__A,	__A=	embeds

						# forward
						__A=	pipe(**lowerCAmelCase_	)
						__A=	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 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Union[str, Any]:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)
			@property
			def 					lowerCAmelCase     (  self							:			str	)      ->		Union[str, Any]:
						__A=	ort.SessionOptions()
						__A=	False
						return options
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Tuple:
						# using the PNDM scheduler by default
						__A=	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_	)

						__A=	'A painting of a squirrel eating a burger'
						np.random.seed(0	)
						__A=	sd_pipe([prompt]			,    guidance_scale=6.0			,    num_inference_steps=10			,    output_type='np'	)
						__A=	output.images

						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						__A=	np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		List[str]:
						__A=	DDIMScheduler.from_pretrained(
						    'runwayml/stable-diffusion-v1-5'			,    subfolder='scheduler'			,    revision='onnx'	)
						__A=	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_	)

						__A=	'open neural network exchange'
						__A=	np.random.RandomState(0	)
						__A=	sd_pipe([prompt]			,    guidance_scale=7.5			,    num_inference_steps=10			,    generator=lowerCAmelCase_			,    output_type='np'	)
						__A=	output.images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						__A=	np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[str]:
						__A=	LMSDiscreteScheduler.from_pretrained(
						    'runwayml/stable-diffusion-v1-5'			,    subfolder='scheduler'			,    revision='onnx'	)
						__A=	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_	)

						__A=	'open neural network exchange'
						__A=	np.random.RandomState(0	)
						__A=	sd_pipe([prompt]			,    guidance_scale=7.5			,    num_inference_steps=10			,    generator=lowerCAmelCase_			,    output_type='np'	)
						__A=	output.images
						__A=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						__A=	np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3
			def 					lowerCAmelCase     (  self							:			str	)      ->		Dict:
						__A=	0

						def test_callback_fn(lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			np.ndarray	) -> None:
									__A=	True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 0:
												assert latents.shape == (1, 4, 64, 64)
												__A=	latents[0, -3:, -3:, -1]
												__A=	np.array(
												    [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]	)

												assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1E-3
									elif step == 5:
												assert latents.shape == (1, 4, 64, 64)
												__A=	latents[0, -3:, -3:, -1]
												__A=	np.array(
												    [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]	)

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

						__A=	False

						__A=	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_	)

						__A=	'Andromeda galaxy in a bottle'

						__A=	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 					lowerCAmelCase     (  self							:			Any	)      ->		int:
						__A=	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

						__A=	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_	)
									__A=	OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_	)

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







'''simple docstring'''





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  			=				re.compile(r'''([A-Z]+)([A-Z][a-z])''')
UpperCAmelCase__  			=				re.compile(r'''([a-z\d])([A-Z])''')

UpperCAmelCase__  			=				re.compile(r'''(?<!_)_(?!_)''')
UpperCAmelCase__  			=				re.compile(r'''(_{2,})''')

UpperCAmelCase__  			=				r'''^\w+(\.\w+)*$'''

UpperCAmelCase__  			=				r'''<>:/\|?*'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	_uppercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			__A=	_lowercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			return name.lower()






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	_single_underscore_re.split(_SCREAMING_SNAKE_CASE )
			__A=	[_multiple_underscores_re.split(_SCREAMING_SNAKE_CASE ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) if n != '' )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			return camelcase_to_snakecase(_SCREAMING_SNAKE_CASE )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			if not re.match(_split_re,_SCREAMING_SNAKE_CASE ):
						raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
			return f"""{filename_prefix_for_name(_SCREAMING_SNAKE_CASE )}-{split}"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Union[str, Any]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			if filetype_suffix:
						prefix += f""".{filetype_suffix}"""
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			return f"""{filepath}*"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[Any]=None,_SCREAMING_SNAKE_CASE					: List[str]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			if shard_lengths:
						__A=	len(_SCREAMING_SNAKE_CASE )
						__A=	[f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(_SCREAMING_SNAKE_CASE )]
						if filetype_suffix:
									__A=	[filename + f""".{filetype_suffix}""" for filename in filenames]
						return filenames
			else:
						__A=	prefix
						if filetype_suffix:
									filename += f""".{filetype_suffix}"""
						return [filename]



