
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 	__lowercase				(  _lowerCAmelCase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Union[str, Any]	      =			'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'

			def        UpperCAmelCase__ (self			,   A=0      ):
								lowerCamelCase_					:      str     =		np.random.RandomState(_lowerCAmelCase      )
								lowerCamelCase_					:      Dict     =		{
								    '''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      ):
								lowerCamelCase_					:      Any     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

								assert image.shape == (1, 1_2_8, 1_2_8, 3)
								lowerCamelCase_					:      Dict     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								lowerCamelCase_					:      Optional[Any]     =		PNDMScheduler.from_config(pipe.scheduler.config			,   skip_prk_steps=_lowerCAmelCase      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

								assert image.shape == (1, 1_2_8, 1_2_8, 3)
								lowerCamelCase_					:      Optional[int]     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								lowerCamelCase_					:      Dict     =		LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

								assert image.shape == (1, 1_2_8, 1_2_8, 3)
								lowerCamelCase_					:      Optional[int]     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								lowerCamelCase_					:      int     =		EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

								assert image.shape == (1, 1_2_8, 1_2_8, 3)
								lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								lowerCamelCase_					:      List[str]     =		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

								assert image.shape == (1, 1_2_8, 1_2_8, 3)
								lowerCamelCase_					:      Dict     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								lowerCamelCase_					:      Tuple     =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

								assert image.shape == (1, 1_2_8, 1_2_8, 3)
								lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

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

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

								lowerCamelCase_					:      Union[str, Any]     =		pipe.tokenizer(
								    _lowerCAmelCase			,   padding='''max_length'''			,   max_length=pipe.tokenizer.model_max_length			,   truncation=_lowerCAmelCase			,   return_tensors='''np'''			,   )
								lowerCamelCase_					:      Dict     =		text_inputs['''input_ids''']

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

								lowerCamelCase_					:      Union[str, Any]     =		prompt_embeds

								# forward
								lowerCamelCase_					:      Any     =		pipe(**_lowerCAmelCase      )
								lowerCamelCase_					:      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      ):
								lowerCamelCase_					:      Union[str, Any]     =		OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''      )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

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

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

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

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

								lowerCamelCase_,      lowerCamelCase_					:      Dict     =		embeds

								# forward
								lowerCamelCase_					:      Optional[int]     =		pipe(**_lowerCAmelCase      )
								lowerCamelCase_					:      List[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 	__lowercase				(  unittest.TestCase    ):

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

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		ort.SessionOptions()
								lowerCamelCase_					:      Optional[int]     =		False
								return options

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		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      )

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

								lowerCamelCase_					:      Dict     =		image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								lowerCamelCase_					:      Union[str, Any]     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		DDIMScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''			,   subfolder='''scheduler'''			,   revision='''onnx'''      )
								lowerCamelCase_					:      int     =		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      )

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5'''			,   subfolder='''scheduler'''			,   revision='''onnx'''      )
								lowerCamelCase_					:      Optional[Any]     =		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      )

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								lowerCamelCase_					:      List[str]     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		0

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

								lowerCamelCase_					:      List[Any]     =		False

								lowerCamelCase_					:      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      )

								lowerCamelCase_					:      Optional[Any]     =		'''Andromeda galaxy in a bottle'''

								lowerCamelCase_					:      List[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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      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

								lowerCamelCase_					:      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      )
													lowerCamelCase_					:      Dict     =		OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase      )

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




'''simple docstring'''


import qiskit




def lowercase_	(   _lowercase			,       _lowercase				)     ->							qiskit.result.counts.Counts:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		qiskit.Aer.get_backend('''aer_simulator'''				)

					# Create a Quantum Circuit acting on the q register
					lowerCamelCase_					:      Optional[int]     =		qiskit.QuantumCircuit(_lowercase			,       _lowercase				)

					# Apply X (NOT) Gate to Qubits 0 & 1
					circuit.x(0				)
					circuit.x(1				)

					# Map the quantum measurement to the classical bits
					circuit.measure([0, 1]			,       [0, 1]				)

					# Execute the circuit on the qasm simulator
					lowerCamelCase_					:      Optional[int]     =		qiskit.execute(_lowercase			,       _lowercase			,       shots=1_000				)

					# Return the histogram data of the results of the experiment.
					return job.result().get_counts(_lowercase				)


if __name__ == "__main__":
					__lowercase							:   List[Any]					=     single_qubit_measure(2, 2)
					print(f'Total count for various states are: {counts}')


