


"""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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				StableDiffusionInstructPixaPixPipeline
		__UpperCAmelCase		:			Any										=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
		__UpperCAmelCase		:			Optional[int]										=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__UpperCAmelCase		:			Any										=				IMAGE_TO_IMAGE_IMAGE_PARAMS
		__UpperCAmelCase		:			Any										=				IMAGE_TO_IMAGE_IMAGE_PARAMS

		def 			__UpperCAmelCase       (  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=_a      )
				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=1_000							, )
				__a           =			CLIPTextModel(_a      )
				__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 			__UpperCAmelCase       (  self							, _a							, _a=0      ):
				__a           =			floats_tensor((1, 3, 32, 32)							, rng=random.Random(_a      )      ).to(_a      )
				__a           =			image.cpu().permute(0							, 2							, 3							, 1      )[0]
				__a           =			Image.fromarray(np.uinta(_a      )      ).convert('''RGB'''      )
				if str(_a      ).startswith('''mps'''      ):
						__a           =			torch.manual_seed(_a      )
				else:
						__a           =			torch.Generator(device=_a      ).manual_seed(_a      )
				__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 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__a           =			self.get_dummy_components()
				__a           =			StableDiffusionInstructPixaPixPipeline(**_a      )
				__a           =			sd_pipe.to(_a      )
				sd_pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			sd_pipe(**_a      ).images
				__a           =			image[0, -3:, -3:, -1]
				assert image.shape == (1, 32, 32, 3)
				__a           =			np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__a           =			self.get_dummy_components()
				__a           =			StableDiffusionInstructPixaPixPipeline(**_a      )
				__a           =			sd_pipe.to(_a      )
				sd_pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			'''french fries'''
				__a           =			sd_pipe(**_a							, negative_prompt=_a      )
				__a           =			output.images
				__a           =			image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				__a           =			np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__a           =			self.get_dummy_components()
				__a           =			StableDiffusionInstructPixaPixPipeline(**_a      )
				__a           =			sd_pipe.to(_a      )
				sd_pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			[inputs['''prompt''']] * 2

				__a           =			np.array(inputs['''image''']      ).astype(np.floataa      ) / 255.0
				__a           =			torch.from_numpy(_a      ).unsqueeze(0      ).to(_a      )
				__a           =			image / 2 + 0.5
				__a           =			image.permute(0							, 3							, 1							, 2      )
				__a           =			image.repeat(2							, 1							, 1							, 1      )

				__a           =			sd_pipe(**_a      ).images
				__a           =			image[-1, -3:, -3:, -1]

				assert image.shape == (2, 32, 32, 3)
				__a           =			np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__a           =			self.get_dummy_components()
				__a           =			EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0085							, beta_end=0.012							, beta_schedule='''scaled_linear'''      )
				__a           =			StableDiffusionInstructPixaPixPipeline(**_a      )
				__a           =			sd_pipe.to(_a      )
				sd_pipe.set_progress_bar_config(disable=_a      )

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

				__a           =			[round(_a							, 4      ) for x in image_slice.flatten().tolist()]
				print(''','''.join([str(_a      ) for x in slice]      )      )

				assert image.shape == (1, 32, 32, 3)
				__a           =			np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]      )

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

		def 			__UpperCAmelCase       (  self      ):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_dummy_components()
				__a           =			StableDiffusionInstructPixaPixPipeline(**_a      )
				__a           =			VaeImageProcessor(do_resize=_a							, do_normalize=_a      )
				__a           =			pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			pipe(**self.get_dummy_inputs_by_type(_a							, input_image_type='''pt'''      )      )[0]

				__a           =			components['''vae''']
				__a           =			self.get_dummy_inputs_by_type(_a							, 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(**_a      )[0]

				__a           =			np.abs(out - out_latents_inputs      ).max()
				self.assertLess(_a							, 1E-4							, '''passing latents as image input generate different result from passing image'''      )







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


		'''simple docstring'''



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

		def 			__UpperCAmelCase       (  self							, _a=0      ):
				__a           =			torch.manual_seed(_a      )
				__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 			__UpperCAmelCase       (  self      ):
				__a           =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    '''timbrooks/instruct-pix2pix'''							, safety_checker=_a      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)
				__a           =			np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    '''timbrooks/instruct-pix2pix'''							, safety_checker=_a      )
				__a           =			LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)
				__a           =			np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    '''timbrooks/instruct-pix2pix'''							, safety_checker=_a      )
				__a           =			DDIMScheduler.from_config(pipe.scheduler.config      )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)
				__a           =			np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			0

				def callback_fn(_a							, _a							, _a      ) -> 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([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]      )

								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([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]      )

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

				__a           =			False

				__a           =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    '''timbrooks/instruct-pix2pix'''							, safety_checker=_a							, torch_dtype=torch.floataa      )
				__a           =			pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )
				pipe.enable_attention_slicing()

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

		def 			__UpperCAmelCase       (  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=_a							, torch_dtype=torch.floataa      )
				__a           =			pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )
				pipe.enable_attention_slicing(1      )
				pipe.enable_sequential_cpu_offload()

				__a           =			self.get_inputs()
				__a           =			pipe(**_a      )

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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_inputs()
				# resize to resolution that is divisible by 8 but not 16 or 32
				__a           =			inputs['''image'''].resize((504, 504)      )

				__a           =			'''timbrooks/instruct-pix2pix'''
				__a           =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    _a							, safety_checker=_a							, )
				pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )
				pipe.enable_attention_slicing()

				__a           =			pipe(**_a      )
				__a           =			output.images[0]

				__a           =			image[255:258, 383:386, -1]

				assert image.shape == (504, 504, 3)
				__a           =			np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]      )

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












from random import randint
from tempfile import TemporaryFile

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       0
					if start < end:
										__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       a[end]
										__lowerCAmelCase				   =       a[pivot]
										__lowerCAmelCase				   =       temp

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       _in_place_partition(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      p - 1     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      p + 1      ,      SCREAMING_SNAKE_CASE_     )
					return count







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       a[pivot]
					__lowerCAmelCase				   =       temp
					__lowerCAmelCase				   =       start - 1
					for index in range(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															__lowerCAmelCase				   =       new_pivot_index + 1
															__lowerCAmelCase				   =       a[new_pivot_index]
															__lowerCAmelCase				   =       a[index]
															__lowerCAmelCase				   =       temp

					__lowerCAmelCase				   =       a[new_pivot_index + 1]
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       temp
					return new_pivot_index + 1, count


UpperCamelCase__    			=			TemporaryFile()
UpperCamelCase__    			=			100  # 1000 elements are to be sorted


UpperCamelCase__			,	UpperCamelCase__    			=			0, 1  # mean and standard deviation
UpperCamelCase__    			=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
UpperCamelCase__    			=			np.load(outfile)
UpperCamelCase__    			=			len(M) - 1
UpperCamelCase__    			=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)


print(z)




