'''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   (   UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      StableDiffusionInstructPixaPixPipeline
							a_        =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
							a_        =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							a_        =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							a_        =      IMAGE_TO_IMAGE_IMAGE_PARAMS





							def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=8     ,		out_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,		cross_attention_dim=3_2     ,		)
								__UpperCAmelCase							=			PNDMScheduler(skip_prk_steps=__a	)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			AutoencoderKL(
								    block_out_channels=[3_2, 6_4]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPTextConfig(
								    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
								__UpperCAmelCase							=			CLIPTextModel(__a	)
								__UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								__UpperCAmelCase							=			{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]=0	)							->	int:
								__UpperCAmelCase							=			floats_tensor((1, 3, 3_2, 3_2)     ,		rng=random.Random(__a	)	).to(__a	)
								__UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	)[0]
								__UpperCAmelCase							=			Image.fromarray(np.uinta(__a	)	).convert('''RGB'''	)
								if str(__a	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(__a	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			{
								    '''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       snake_case__						(      self				:							List[str]	)							->	Optional[Any]:
								__UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
								__UpperCAmelCase							=			sd_pipe.to(__a	)
								sd_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)
								__UpperCAmelCase							=			sd_pipe(**__a	).images
								__UpperCAmelCase							=			image[0, -3:, -3:, -1]
								assert image.shape == (1, 3_2, 3_2, 3)
								__UpperCAmelCase							=			np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]	)

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





							def       snake_case__						(      self				:							List[str]	)							->	Optional[Any]:
								__UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
								__UpperCAmelCase							=			sd_pipe.to(__a	)
								sd_pipe.set_progress_bar_config(disable=__a	)

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

								assert image.shape == (1, 3_2, 3_2, 3)
								__UpperCAmelCase							=			np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]	)

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





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

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

								__UpperCAmelCase							=			np.array(inputs['''image''']	).astype(np.floataa	) / 2_5_5.0
								__UpperCAmelCase							=			torch.from_numpy(__a	).unsqueeze(0	).to(__a	)
								__UpperCAmelCase							=			image / 2 + 0.5
								__UpperCAmelCase							=			image.permute(0     ,		3     ,		1     ,		2	)
								__UpperCAmelCase							=			image.repeat(2     ,		1     ,		1     ,		1	)

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

								assert image.shape == (2, 3_2, 3_2, 3)
								__UpperCAmelCase							=			np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]	)

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





							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[Any]:
								__UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			EulerAncestralDiscreteScheduler(
								    beta_start=0.0_0_0_8_5     ,		beta_end=0.0_1_2     ,		beta_schedule='''scaled_linear'''	)
								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
								__UpperCAmelCase							=			sd_pipe.to(__a	)
								sd_pipe.set_progress_bar_config(disable=__a	)

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

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

								assert image.shape == (1, 3_2, 3_2, 3)
								__UpperCAmelCase							=			np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]	)

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





							def       snake_case__						(      self				:							str	)							->	Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






							def       snake_case__						(      self				:							List[Any]	)							->	Tuple:
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
								__UpperCAmelCase							=			VaeImageProcessor(do_resize=__a     ,		do_normalize=__a	)
								__UpperCAmelCase							=			pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)

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

								__UpperCAmelCase							=			components['''vae''']
								__UpperCAmelCase							=			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():
										__UpperCAmelCase							=			vae.encode(inputs[image_param]	).latent_dist.mode()

								__UpperCAmelCase							=			pipe(**__a	)[0]

								__UpperCAmelCase							=			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 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Dict	)							->	Dict:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def       snake_case__						(      self				:							List[str]     ,		__a				:							Dict=0	)							->	List[str]:
								__UpperCAmelCase							=			torch.manual_seed(__a	)
								__UpperCAmelCase							=			load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''	)
								__UpperCAmelCase							=			{
								    '''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       snake_case__						(      self				:							Optional[Any]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a	)
								pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__UpperCAmelCase							=			np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]	)

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





							def       snake_case__						(      self				:							Any	)							->	List[str]:
								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a	)
								__UpperCAmelCase							=			LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
								pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__UpperCAmelCase							=			np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]	)

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





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

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

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__UpperCAmelCase							=			np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]	)

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





							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[Any]:
								__UpperCAmelCase							=			0

								def callback_fn(__a				:							int     ,		__a				:							int     ,		__a				:							torch.FloatTensor	) -> None:
									__UpperCAmelCase							=			True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
										__UpperCAmelCase							=			latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										__UpperCAmelCase							=			latents[0, -3:, -3:, -1]
										__UpperCAmelCase							=			np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]	)

										assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
									elif step == 2:
										__UpperCAmelCase							=			latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										__UpperCAmelCase							=			latents[0, -3:, -3:, -1]
										__UpperCAmelCase							=			np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]	)

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

								__UpperCAmelCase							=			False

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

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





							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								__UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a     ,		torch_dtype=torch.floataa	)
								__UpperCAmelCase							=			pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)
								pipe.enable_attention_slicing(1	)
								pipe.enable_sequential_cpu_offload()

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

								__UpperCAmelCase							=			torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 1_0**9






							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								__UpperCAmelCase							=			inputs['''image'''].resize((5_0_4, 5_0_4)	)

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

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

								__UpperCAmelCase							=			image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

								assert image.shape == (5_0_4, 5_0_4, 3)
								__UpperCAmelCase							=			np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]	)

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

'''simple docstring'''


from pathlib import Path

import fire





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	dest_dir.mkdir(exist_ok=UpperCamelCase__       )
	for path in src_dir.iterdir():
		__UpperCAmelCase							=			[x.rstrip() for x in list(path.open().readlines()       )][:n]
		__UpperCAmelCase							=			dest_dir.joinpath(path.name       )
		print(UpperCamelCase__       )
		dest_path.open('''w'''       ).write('''\n'''.join(UpperCamelCase__       )       )


if __name__ == "__main__":
					fire.Fire(minify)
