


import os
from collections.abc import Iterator
def 			_a (      SCREAMING_SNAKE_CASE = "."     ):


		"""simple docstring"""




		for dir_path, dir_names, filenames in os.walk(lowerCAmelCase_     ):
				lowercase__            =      [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
				for filename in filenames:
						if filename == "__init__.py":
								continue
						if os.path.splitext(lowerCAmelCase_     )[1] in (".py", ".ipynb"):
								yield os.path.join(lowerCAmelCase_						,				lowerCAmelCase_     ).lstrip('''./'''     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return f'{i * "  "}*' if i else "\n##"
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      old_path.split(os.sep     )
		for i, new_part in enumerate(new_path.split(os.sep     )     ):
				if (i + 1 > len(lowerCAmelCase_     ) or old_parts[i] != new_part) and new_part:
						print(f'{md_prefix(lowerCAmelCase_     )} {new_part.replace("_"						,				" "     ).title()}'     )
		return new_path
def 			_a (      SCREAMING_SNAKE_CASE = "."     ):


		"""simple docstring"""




		lowercase__            =      ''''''
		for filepath in sorted(good_file_paths(lowerCAmelCase_     )     ):
				lowercase__     ,		lowercase__            =      os.path.split(lowerCAmelCase_     )
				if filepath != old_path:
						lowercase__            =      print_path(lowerCAmelCase_						,				lowerCAmelCase_     )
				lowercase__            =      (filepath.count(os.sep     ) + 1) if filepath else 0
				lowercase__            =      f'{filepath}/{filename}'.replace(''' '''						,				'''%20'''     )
				lowercase__            =      os.path.splitext(filename.replace('''_'''						,				''' '''     ).title()     )[0]
				print(f'{md_prefix(lowerCAmelCase_     )} [{filename}]({url})'     )


if __name__ == "__main__":
			print_directory_md('.')









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 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase		,    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  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 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	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					,  )
     __lowerCAmelCase								=	PNDMScheduler(skip_prk_steps=lowerCAmelCase_     )
     torch.manual_seed(0     )
     __lowerCAmelCase								=	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     )
     __lowerCAmelCase								=	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					,  )
     __lowerCAmelCase								=	CLIPTextModel(lowerCAmelCase_     )
     __lowerCAmelCase								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

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






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

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]
     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		List[str]     )							->						Dict:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

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

     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	[inputs['prompt']] * 2

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

     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[-1, -3:, -3:, -1]

     assert image.shape == (2, 3_2, 3_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		Dict     )							->						Optional[Any]:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85					,  beta_end=0.0_12					,  beta_schedule='scaled_linear'     )
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

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

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

     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	VaeImageProcessor(do_resize=lowerCAmelCase_					,  do_normalize=lowerCAmelCase_     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_					,  input_image_type='pt'     )     )[0]

     __lowerCAmelCase								=	components['vae']
     __lowerCAmelCase								=	self.get_dummy_inputs_by_type(lowerCAmelCase_					,  input_image_type='pt'     )

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

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )[0]

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





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

  """simple docstring"""


  def 				lowercase		(				self :		int     )							->						Optional[int]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






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

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

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		Tuple     )							->						List[str]:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     __lowerCAmelCase								=	LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		Optional[Any]     )							->						Dict:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     __lowerCAmelCase								=	DDIMScheduler.from_config(pipe.scheduler.config     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	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 				lowercase		(				self :		Optional[int]     )							->						int:
     __lowerCAmelCase								=	0

     def callback_fn(lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		torch.FloatTensor     ) -> None:
        __lowerCAmelCase								=	True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           __lowerCAmelCase								=	latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase								=	latents[0, -3:, -3:, -1]
           __lowerCAmelCase								=	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:
           __lowerCAmelCase								=	latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase								=	latents[0, -3:, -3:, -1]
           __lowerCAmelCase								=	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

     __lowerCAmelCase								=	False

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

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






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

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

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )

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






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

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

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )
     __lowerCAmelCase								=	output.images[0]

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

     assert image.shape == (5_0_4, 5_0_4, 3)
     __lowerCAmelCase								=	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

