
import numpy as np




class   SCREAMING_SNAKE_CASE__	:





	'''simple docstring'''



	def __init__(    self      :       Any     ,    lowercase      :       List[Any]=None     ,    lowercase      :       Optional[int]=None     ,    lowercase      :       Tuple=None     ,    lowercase      :       List[str]=None     ,    lowercase      :       int=None      ):


				'''simple docstring'''


				self.set_matricies(red=a_     ,    green=a_     ,    blue=a_     ,    red_edge=a_     ,    nir=a_      )



	def 					A				(    self      :       List[Any]     ,    lowercase      :       Tuple=None     ,    lowercase      :       Optional[int]=None     ,    lowercase      :       Any=None     ,    lowercase      :       Dict=None     ,    lowercase      :       Any=None      ):


				'''simple docstring'''


				if red is not None:
							_snake_case            =     red
				if green is not None:
							_snake_case            =     green
				if blue is not None:
							_snake_case            =     blue
				if red_edge is not None:
							_snake_case            =     red_edge
				if nir is not None:
							_snake_case            =     nir
				return True



	def 					A				(    self      :       Tuple     ,    lowercase      :       Tuple=""     ,    lowercase      :       Dict=None     ,    lowercase      :       Optional[Any]=None     ,    lowercase      :       Optional[int]=None     ,    lowercase      :       Tuple=None     ,    lowercase      :       str=None      ):


				'''simple docstring'''


				self.set_matricies(red=a_     ,    green=a_     ,    blue=a_     ,    red_edge=a_     ,    nir=a_      )
				_snake_case            =     {
				    "ARVI2": self.arvaa,
				    "CCCI": self.ccci,
				    "CVI": self.cvi,
				    "GLI": self.gli,
				    "NDVI": self.ndvi,
				    "BNDVI": self.bndvi,
				    "redEdgeNDVI": self.red_edge_ndvi,
				    "GNDVI": self.gndvi,
				    "GBNDVI": self.gbndvi,
				    "GRNDVI": self.grndvi,
				    "RBNDVI": self.rbndvi,
				    "PNDVI": self.pndvi,
				    "ATSAVI": self.atsavi,
				    "BWDRVI": self.bwdrvi,
				    "CIgreen": self.ci_green,
				    "CIrededge": self.ci_rededge,
				    "CI": self.ci,
				    "CTVI": self.ctvi,
				    "GDVI": self.gdvi,
				    "EVI": self.evi,
				    "GEMI": self.gemi,
				    "GOSAVI": self.gosavi,
				    "GSAVI": self.gsavi,
				    "Hue": self.hue,
				    "IVI": self.ivi,
				    "IPVI": self.ipvi,
				    "I": self.i,
				    "RVI": self.rvi,
				    "MRVI": self.mrvi,
				    "MSAVI": self.m_savi,
				    "NormG": self.norm_g,
				    "NormNIR": self.norm_nir,
				    "NormR": self.norm_r,
				    "NGRDI": self.ngrdi,
				    "RI": self.ri,
				    "S": self.s,
				    "IF": self._if,
				    "DVI": self.dvi,
				    "TVI": self.tvi,
				    "NDRE": self.ndre,
				}

				try:
							return funcs[index]()
				except KeyError:
							print('Index not in the list!'      )
							return False



	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


				return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
				    (self.nir - self.red) / (self.nir + self.red)
				)



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


				return self.nir * (self.red / (self.green**2))



	def 					A				(    self      :       Dict      ):


				'''simple docstring'''


				return (2 * self.green - self.red - self.blue) / (
				    2 * self.green + self.red + self.blue
				)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.nir - self.red) / (self.nir + self.red)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.nir - self.blue) / (self.nir + self.blue)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.redEdge - self.red) / (self.redEdge + self.red)



	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				return (self.nir - self.green) / (self.nir + self.green)



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				return (self.nir - (self.green + self.blue)) / (
				    self.nir + (self.green + self.blue)
				)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.nir - (self.green + self.red)) / (
				    self.nir + (self.green + self.red)
				)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				return (self.nir - (self.green + self.red + self.blue)) / (
				    self.nir + (self.green + self.red + self.blue)
				)



	def 					A				(    self      :       Any     ,    lowercase      :       Union[str, Any]=0.08     ,    lowercase      :       int=1.22     ,    lowercase      :       Tuple=0.03      ):


				'''simple docstring'''


				return a * (
				    (self.nir - a * self.red - b)
				    / (a * self.nir + self.red - a * b + x * (1 + a**2))
				)



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)



	def 					A				(    self      :       Optional[Any]      ):


				'''simple docstring'''


				return (self.nir / self.green) - 1



	def 					A				(    self      :       Dict      ):


				'''simple docstring'''


				return (self.nir / self.redEdge) - 1



	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				return (self.red - self.blue) / self.red



	def 					A				(    self      :       str      ):


				'''simple docstring'''


				_snake_case            =     self.ndvi()
				return ((ndvi + 0.5) / (abs(ndvi + 0.5      ))) * (abs(ndvi + 0.5      ) ** (1 / 2))



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return self.nir - self.green



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				return 2.5 * (
				    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
				)



	def 					A				(    self      :       Tuple      ):


				'''simple docstring'''


				_snake_case            =     (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
				    self.nir + self.red + 0.5
				)
				return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)



	def 					A				(    self      :       Dict     ,    lowercase      :       Any=0.16      ):


				'''simple docstring'''


				return (self.nir - self.green) / (self.nir + self.green + y)



	def 					A				(    self      :       List[str]     ,    lowercase      :       Dict=0.5      ):


				'''simple docstring'''


				return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				return np.arctan(
				    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)      )



	def 					A				(    self      :       Optional[int]     ,    lowercase      :       Dict=None     ,    lowercase      :       int=None      ):


				'''simple docstring'''


				return (self.nir - b) / (a * self.red)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)



	def 					A				(    self      :       Union[str, Any]      ):


				'''simple docstring'''


				return (self.red + self.green + self.blue) / 30.5



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				return self.nir / self.red



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				return (self.rvi() - 1) / (self.rvi() + 1)



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				return (
				    (2 * self.nir + 1)
				    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
				) / 2



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


				return self.green / (self.nir + self.red + self.green)



	def 					A				(    self      :       Tuple      ):


				'''simple docstring'''


				return self.nir / (self.nir + self.red + self.green)



	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				return self.red / (self.nir + self.red + self.green)



	def 					A				(    self      :       Tuple      ):


				'''simple docstring'''


				return (self.green - self.red) / (self.green + self.red)



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


				return (self.red - self.green) / (self.red + self.green)



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				_snake_case            =     np.max([np.max(self.red      ), np.max(self.green      ), np.max(self.blue      )]      )
				_snake_case            =     np.min([np.min(self.red      ), np.min(self.green      ), np.min(self.blue      )]      )
				return (max_value - min_value) / max_value



	def 					A				(    self      :       Optional[Any]      ):


				'''simple docstring'''


				return (2 * self.red - self.green - self.blue) / (self.green - self.blue)



	def 					A				(    self      :       str      ):


				'''simple docstring'''


				return self.nir / self.red



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return (self.ndvi() + 0.5) ** (1 / 2)





	def 					A				(    self      :       Optional[Any]      ):


				'''simple docstring'''


				return (self.nir - self.redEdge) / (self.nir + self.redEdge)






"""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 					__UpperCAmelCase							(						_UpperCamelCase						,  unittest.TestCase  ):
				__lowerCamelCase				:	List[Any]				     =				"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[Any]=0    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			np.random.RandomState(a_    )
							a__  :	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    :			Dict    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	Union[str, Any]		 =			pipe(**a_    ).images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	List[str]		 =			PNDMScheduler.from_config(pipe.scheduler.config	, skip_prk_steps=a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	List[Any]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Tuple		 =			LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

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

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Dict		 =			np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	int		 =			pipe(**a_    ).images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	str		 =			np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

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

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Any:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	str		 =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

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

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			str    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	Any		 =			3 * [inputs["prompt"]]

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

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

							a__  :	Optional[Any]		 =			pipe.tokenizer(
							    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[str]		 =			text_inputs["input_ids"]

							a__  :	int		 =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

							a__  :	List[Any]		 =			prompt_embeds

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

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






				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs()
							a__  :	Dict		 =			3 * ["this is a negative prompt"]
							a__  :	Optional[Any]		 =			negative_prompt
							a__  :	Any		 =			3 * [inputs["prompt"]]

							# forward
							a__  :	str		 =			pipe(**a_    )
							a__  :	List[str]		 =			output.images[0, -3:, -3:, -1]

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

							a__  :	List[Any]		 =			[]
							for p in [prompt, negative_prompt]:
										a__  :	int		 =			pipe.tokenizer(
										    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
										a__  :	Any		 =			text_inputs["input_ids"]

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

							a__ ,	a__  :	Union[str, Any]		 =			embeds

							# forward
							a__  :	Dict		 =			pipe(**a_    )
							a__  :	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 					__UpperCAmelCase							(						unittest.TestCase  ):
				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Optional[int]:



							'''simple docstring'''



							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Tuple:



							'''simple docstring'''



							a__  :	List[str]		 =			ort.SessionOptions()
							a__  :	List[str]		 =			False
							return options
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Dict		 =			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_    )

							a__  :	Optional[int]		 =			"A painting of a squirrel eating a burger"
							np.random.seed(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=6.0	, num_inference_steps=10	, output_type="np"    )
							a__  :	Any		 =			output.images

							a__  :	Optional[int]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	str		 =			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			DDIMScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	str		 =			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_    )

							a__  :	str		 =			"open neural network exchange"
							a__  :	Tuple		 =			np.random.RandomState(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	Dict		 =			output.images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[Any]		 =			LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	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_    )

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

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			0

							def test_callback_fn(a_    :			int	, a_    :			int	, a_    :			np.ndarray    ) -> None:
										a__  :	Optional[int]		 =			True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Any		 =			latents[0, -3:, -3:, -1]
													a__  :	Union[str, Any]		 =			np.array(
													    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]    )

													assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
										elif step == 5:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Union[str, Any]		 =			latents[0, -3:, -3:, -1]
													a__  :	Optional[int]		 =			np.array(
													    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]    )

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

							a__  :	Tuple		 =			False

							a__  :	Optional[int]		 =			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_    )

							a__  :	List[Any]		 =			"Andromeda galaxy in a bottle"

							a__  :	str		 =			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       UpperCAmelCase      (		self    :			Tuple    )	->	List[str]:



							'''simple docstring'''



							a__  :	Optional[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

							a__  :	Tuple		 =			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_    )
										a__  :	Optional[int]		 =			OnnxStableDiffusionPipeline.from_pretrained(a_    )

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