
'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class   a_ (      lowerCamelCase__		,  lowerCamelCase__		,  lowerCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Union[str, Any]       =    StableDiffusionInstructPixaPixPipeline
			lowerCamelCase__	:			Tuple       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
			lowerCamelCase__	:			Optional[Any]       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase__	:			Any       =    IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase__	:			int       =    IMAGE_TO_IMAGE_IMAGE_PARAMS




			def 					lowerCAmelCase__					(       self	):
				torch.manual_seed(0	)
				a_      				=						UNetaDConditionModel(
				    block_out_channels=(32, 64)					,   layers_per_block=2					,   sample_size=32					,   in_channels=8					,   out_channels=4					,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,   cross_attention_dim=32					,   )
				a_      				=						PNDMScheduler(skip_prk_steps=snake_case__	)
				torch.manual_seed(0	)
				a_      				=						AutoencoderKL(
				    block_out_channels=[32, 64]					,   in_channels=3					,   out_channels=3					,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,   latent_channels=4					,   )
				torch.manual_seed(0	)
				a_      				=						CLIPTextConfig(
				    bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1e-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=10_00					,   )
				a_      				=						CLIPTextModel(snake_case__	)
				a_      				=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

				a_      				=						{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """vae""": vae,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """safety_checker""": None,
				    """feature_extractor""": None,
				}
				return components




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=0	):
				a_      				=						floats_tensor((1, 3, 32, 32)					,   rng=random.Random(snake_case__	)	).to(snake_case__	)
				a_      				=						image.cpu().permute(0					,   2					,   3					,   1	)[0]
				a_      				=						Image.fromarray(np.uinta(snake_case__	)	).convert("""RGB"""	)
				if str(snake_case__	).startswith("""mps"""	):
					a_      				=						torch.manual_seed(snake_case__	)
				else:
					a_      				=						torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
				a_      				=						{
				    """prompt""": """A painting of a squirrel eating a burger""",
				    """image""": image,
				    """generator""": generator,
				    """num_inference_steps""": 2,
				    """guidance_scale""": 6.0,
				    """image_guidance_scale""": 1,
				    """output_type""": """numpy""",
				}
				return inputs




			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
				a_      				=						self.get_dummy_components()
				a_      				=						StableDiffusionInstructPixaPixPipeline(**snake_case__	)
				a_      				=						sd_pipe.to(snake_case__	)
				sd_pipe.set_progress_bar_config(disable=snake_case__	)

				a_      				=						self.get_dummy_inputs(snake_case__	)
				a_      				=						sd_pipe(**snake_case__	).images
				a_      				=						image[0, -3:, -3:, -1]
				assert image.shape == (1, 32, 32, 3)
				a_      				=						np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]	)

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
				a_      				=						self.get_dummy_components()
				a_      				=						StableDiffusionInstructPixaPixPipeline(**snake_case__	)
				a_      				=						sd_pipe.to(snake_case__	)
				sd_pipe.set_progress_bar_config(disable=snake_case__	)

				a_      				=						self.get_dummy_inputs(snake_case__	)
				a_      				=						"""french fries"""
				a_      				=						sd_pipe(**snake_case__					,   negative_prompt=snake_case__	)
				a_      				=						output.images
				a_      				=						image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				a_      				=						np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]	)

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
				a_      				=						self.get_dummy_components()
				a_      				=						StableDiffusionInstructPixaPixPipeline(**snake_case__	)
				a_      				=						sd_pipe.to(snake_case__	)
				sd_pipe.set_progress_bar_config(disable=snake_case__	)

				a_      				=						self.get_dummy_inputs(snake_case__	)
				a_      				=						[inputs["""prompt"""]] * 2

				a_      				=						np.array(inputs["""image"""]	).astype(np.floataa	) / 2_55.0
				a_      				=						torch.from_numpy(snake_case__	).unsqueeze(0	).to(snake_case__	)
				a_      				=						image / 2 + 0.5
				a_      				=						image.permute(0					,   3					,   1					,   2	)
				a_      				=						image.repeat(2					,   1					,   1					,   1	)

				a_      				=						sd_pipe(**snake_case__	).images
				a_      				=						image[-1, -3:, -3:, -1]

				assert image.shape == (2, 32, 32, 3)
				a_      				=						np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]	)

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
				a_      				=						self.get_dummy_components()
				a_      				=						EulerAncestralDiscreteScheduler(
				    beta_start=0.0_00_85					,   beta_end=0.0_12					,   beta_schedule="""scaled_linear"""	)
				a_      				=						StableDiffusionInstructPixaPixPipeline(**snake_case__	)
				a_      				=						sd_pipe.to(snake_case__	)
				sd_pipe.set_progress_bar_config(disable=snake_case__	)

				a_      				=						self.get_dummy_inputs(snake_case__	)
				a_      				=						sd_pipe(**snake_case__	).images
				a_      				=						image[0, -3:, -3:, -1]

				a_      				=						[round(snake_case__					,   4	) for x in image_slice.flatten().tolist()]
				print(""",""".join([str(snake_case__	) for x in slice]	)	)

				assert image.shape == (1, 32, 32, 3)
				a_      				=						np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]	)

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




			def 					lowerCAmelCase__					(       self	):
				super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_components()
				a_      				=						StableDiffusionInstructPixaPixPipeline(**snake_case__	)
				a_      				=						VaeImageProcessor(do_resize=snake_case__					,   do_normalize=snake_case__	)
				a_      				=						pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)

				a_      				=						pipe(**self.get_dummy_inputs_by_type(snake_case__					,   input_image_type="""pt"""	)	)[0]

				a_      				=						components["""vae"""]
				a_      				=						self.get_dummy_inputs_by_type(snake_case__					,   input_image_type="""pt"""	)

				for image_param in self.image_latents_params:
					if image_param in inputs.keys():
						a_      				=						vae.encode(inputs[image_param]	).latent_dist.mode()

				a_      				=						pipe(**snake_case__	)[0]

				a_      				=						np.abs(out - out_latents_inputs	).max()
				self.assertLess(snake_case__					,   1e-4					,   """passing latents as image input generate different result from passing image"""	)







@slow
@require_torch_gpu
class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self	):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=0	):
				a_      				=						torch.manual_seed(snake_case__	)
				a_      				=						load_image(
				    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""	)
				a_      				=						{
				    """prompt""": """turn him into a cyborg""",
				    """image""": image,
				    """generator""": generator,
				    """num_inference_steps""": 3,
				    """guidance_scale""": 7.5,
				    """image_guidance_scale""": 1.0,
				    """output_type""": """numpy""",
				}
				return inputs




			def 					lowerCAmelCase__					(       self	):
				a_      				=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    """timbrooks/instruct-pix2pix"""					,   safety_checker=snake_case__	)
				pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)
				pipe.enable_attention_slicing()

				a_      				=						self.get_inputs()
				a_      				=						pipe(**snake_case__	).images
				a_      				=						image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				a_      				=						np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]	)

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    """timbrooks/instruct-pix2pix"""					,   safety_checker=snake_case__	)
				a_      				=						LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
				pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)
				pipe.enable_attention_slicing()

				a_      				=						self.get_inputs()
				a_      				=						pipe(**snake_case__	).images
				a_      				=						image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				a_      				=						np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]	)

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    """timbrooks/instruct-pix2pix"""					,   safety_checker=snake_case__	)
				a_      				=						DDIMScheduler.from_config(pipe.scheduler.config	)
				pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)
				pipe.enable_attention_slicing()

				a_      				=						self.get_inputs()
				a_      				=						pipe(**snake_case__	).images
				a_      				=						image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				a_      				=						np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]	)

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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						0

				def callback_fn(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	) -> None:
					a_      				=						True
					nonlocal number_of_steps
					number_of_steps += 1
					if step == 1:
						a_      				=						latents.detach().cpu().numpy()
						assert latents.shape == (1, 4, 64, 64)
						a_      				=						latents[0, -3:, -3:, -1]
						a_      				=						np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]	)

						assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
					elif step == 2:
						a_      				=						latents.detach().cpu().numpy()
						assert latents.shape == (1, 4, 64, 64)
						a_      				=						latents[0, -3:, -3:, -1]
						a_      				=						np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]	)

						assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

				a_      				=						False

				a_      				=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    """timbrooks/instruct-pix2pix"""					,   safety_checker=snake_case__					,   torch_dtype=torch.floataa	)
				a_      				=						pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)
				pipe.enable_attention_slicing()

				a_      				=						self.get_inputs()
				pipe(**snake_case__					,   callback=snake_case__					,   callback_steps=1	)
				assert callback_fn.has_been_called
				assert number_of_steps == 3




			def 					lowerCAmelCase__					(       self	):
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				a_      				=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    """timbrooks/instruct-pix2pix"""					,   safety_checker=snake_case__					,   torch_dtype=torch.floataa	)
				a_      				=						pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)
				pipe.enable_attention_slicing(1	)
				pipe.enable_sequential_cpu_offload()

				a_      				=						self.get_inputs()
				a_      				=						pipe(**snake_case__	)

				a_      				=						torch.cuda.max_memory_allocated()
				# make sure that less than 2.2 GB is allocated
				assert mem_bytes < 2.2 * 10**9






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_inputs()
				# resize to resolution that is divisible by 8 but not 16 or 32
				a_      				=						inputs["""image"""].resize((5_04, 5_04)	)

				a_      				=						"""timbrooks/instruct-pix2pix"""
				a_      				=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    snake_case__					,   safety_checker=snake_case__					,   )
				pipe.to(snake_case__	)
				pipe.set_progress_bar_config(disable=snake_case__	)
				pipe.enable_attention_slicing()

				a_      				=						pipe(**snake_case__	)
				a_      				=						output.images[0]

				a_      				=						image[2_55:2_58, 3_83:3_86, -1]

				assert image.shape == (5_04, 5_04, 3)
				a_      				=						np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3







import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

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

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

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


if __name__ == "__main__":
							print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')