






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 				lowerCAmelCase_	(		a_   ,		unittest.TestCase			):
				_UpperCamelCase					:       Optional[int]							       = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


				def 						__a					(					self ,						_lowerCAmelCase=0      ):
											_lowercase							:   Optional[Any] 							=   np.random.RandomState(lowerCAmelCase_      )
											_lowercase							:   Union[str, 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 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_lowercase							:   Union[str, Any] 							=   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 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											_lowercase							:   Union[str, Any] 							=   PNDMScheduler.from_config(pipe.scheduler.config ,						skip_prk_steps=lowerCAmelCase_      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

											_lowercase							:   Dict 							=   self.get_dummy_inputs()
											_lowercase							:   List[Any] 							=   pipe(**lowerCAmelCase_      ).images
											_lowercase							:   Tuple 							=   image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_lowercase							:   Union[str, Any] 							=   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 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											_lowercase							:   int 							=   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

											_lowercase							:   List[str] 							=   self.get_dummy_inputs()
											_lowercase							:   Optional[int] 							=   pipe(**lowerCAmelCase_      ).images
											_lowercase							:   int 							=   image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_lowercase							:   Any 							=   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 						__a					(					self      ):
											_lowercase							:   str 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											_lowercase							:   str 							=   EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_lowercase							:   Optional[Any] 							=   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 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											_lowercase							:   Optional[Any] 							=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_lowercase							:   List[Any] 							=   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 						__a					(					self      ):
											_lowercase							:   Tuple 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											_lowercase							:   Tuple 							=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

											_lowercase							:   Any 							=   self.get_dummy_inputs()
											_lowercase							:   int 							=   pipe(**lowerCAmelCase_      ).images
											_lowercase							:   str 							=   image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_lowercase							:   Optional[int] 							=   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 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

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

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

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

											_lowercase							:   List[Any] 							=   pipe.tokenizer(
											    lowerCAmelCase_ ,						padding='max_length' ,						max_length=pipe.tokenizer.model_max_length ,						truncation=lowerCAmelCase_ ,						return_tensors='np' ,						)
											_lowercase							:   Optional[int] 							=   text_inputs['input_ids']

											_lowercase							:   str 							=   pipe.text_encoder(input_ids=text_inputs.astype(np.intaa      )      )[0]

											_lowercase							:   List[Any] 							=   prompt_embeds

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

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


				def 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,						provider='CPUExecutionProvider'      )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

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

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

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

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

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

											_lowercase							,							_lowercase							:   Tuple 							=   embeds

											# forward
											_lowercase							:   List[Any] 							=   pipe(**lowerCAmelCase_      )
											_lowercase							:   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 				lowerCAmelCase_	(		unittest.TestCase			):


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


				@property
				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   ort.SessionOptions()
											_lowercase							:   int 							=   False
											return options


				def 						__a					(					self      ):
											# using the PNDM scheduler by default
											_lowercase							:   Union[str, Any] 							=   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_      )

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

											_lowercase							:   int 							=   image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											_lowercase							:   int 							=   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 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   DDIMScheduler.from_pretrained(
											    'runwayml/stable-diffusion-v1-5' ,						subfolder='scheduler' ,						revision='onnx'      )
											_lowercase							:   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_      )

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

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											_lowercase							:   Optional[int] 							=   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 						__a					(					self      ):
											_lowercase							:   Dict 							=   LMSDiscreteScheduler.from_pretrained(
											    'runwayml/stable-diffusion-v1-5' ,						subfolder='scheduler' ,						revision='onnx'      )
											_lowercase							:   List[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_      )

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

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											_lowercase							:   Any 							=   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 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   0

											def test_callback_fn(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ) -> None:
																		_lowercase							:   Optional[Any] 							=   True
																		nonlocal number_of_steps
																		number_of_steps += 1
																		if step == 0:
																									assert latents.shape == (1, 4, 6_4, 6_4)
																									_lowercase							:   Union[str, Any] 							=   latents[0, -3:, -3:, -1]
																									_lowercase							:   Tuple 							=   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, 6_4, 6_4)
																									_lowercase							:   List[Any] 							=   latents[0, -3:, -3:, -1]
																									_lowercase							:   List[str] 							=   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

											_lowercase							:   int 							=   False

											_lowercase							:   int 							=   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_      )

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

											_lowercase							:   Optional[Any] 							=   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 						__a					(					self      ):
											_lowercase							:   Any 							=   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

											_lowercase							:   Dict 							=   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_      )
																		_lowercase							:   Union[str, Any] 							=   OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_      )

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








'''simple docstring'''





def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			return int((input_a, input_a).count(1 ) != 0 )






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			assert or_gate(0,0 ) == 0
			assert or_gate(0,1 ) == 1
			assert or_gate(1,0 ) == 1
			assert or_gate(1,1 ) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))



