






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__     (     __snake_case   ,    __snake_case   ,    __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					StableDiffusionInstructPixaPixPipeline
     snake_case__					=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
     snake_case__					=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     snake_case__					=					IMAGE_TO_IMAGE_IMAGE_PARAMS
     snake_case__					=					IMAGE_TO_IMAGE_IMAGE_PARAMS







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           torch.manual_seed(0 )
           UpperCamelCase					       =  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   ,						)
           UpperCamelCase					       =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
           torch.manual_seed(0 )
           UpperCamelCase					       =  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 )
           UpperCamelCase					       =  CLIPTextConfig(
               bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1000   ,						)
           UpperCamelCase					       =  CLIPTextModel(_SCREAMING_SNAKE_CASE )
           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   _SCREAMING_SNAKE_CASE     ( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    str   ,						_SCREAMING_SNAKE_CASE      :    Tuple=0 ):






           """simple docstring"""




           UpperCamelCase					       =  floats_tensor((1, 3, 32, 32)   ,						rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  image.cpu().permute(0   ,						2   ,						3   ,						1 )[0]
           UpperCamelCase					       =  Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE ) ).convert('RGB' )
           if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
                 UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           else:
                 UpperCamelCase					       =  torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
           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   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe(**_SCREAMING_SNAKE_CASE ).images
           UpperCamelCase					       =  image[0, -3:, -3:, -1]
           assert image.shape == (1, 32, 32, 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   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

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

           assert image.shape == (1, 32, 32, 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   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  'cpu'  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  [inputs['prompt']] * 2

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

           UpperCamelCase					       =  sd_pipe(**_SCREAMING_SNAKE_CASE ).images
           UpperCamelCase					       =  image[-1, -3:, -3:, -1]

           assert image.shape == (2, 32, 32, 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   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           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(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  sd_pipe.to(_SCREAMING_SNAKE_CASE )
           sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

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

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

           assert image.shape == (1, 32, 32, 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   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_dummy_components()
           UpperCamelCase					       =  StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE   ,						do_normalize=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  pipe(**self.get_dummy_inputs_by_type(_SCREAMING_SNAKE_CASE   ,						input_image_type='pt' ) )[0]

           UpperCamelCase					       =  components['vae']
           UpperCamelCase					       =  self.get_dummy_inputs_by_type(_SCREAMING_SNAKE_CASE   ,						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(**_SCREAMING_SNAKE_CASE )[0]

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





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

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







     def   _SCREAMING_SNAKE_CASE     ( self      :    int   ,						_SCREAMING_SNAKE_CASE      :    Dict=0 ):






           """simple docstring"""




           UpperCamelCase					       =  torch.manual_seed(_SCREAMING_SNAKE_CASE )
           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   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




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

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

           assert image.shape == (1, 512, 512, 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   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
               'timbrooks/instruct-pix2pix'   ,						safety_checker=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config )
           pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
           pipe.enable_attention_slicing()

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

           assert image.shape == (1, 512, 512, 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   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
               'timbrooks/instruct-pix2pix'   ,						safety_checker=_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  DDIMScheduler.from_config(pipe.scheduler.config )
           pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
           pipe.enable_attention_slicing()

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

           assert image.shape == (1, 512, 512, 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   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  0

           def callback_fn(_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    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, 64, 64)
                       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, 64, 64)
                       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=_SCREAMING_SNAKE_CASE   ,						torch_dtype=torch.floataa )
           UpperCamelCase					       =  pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
           pipe.enable_attention_slicing()

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           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=_SCREAMING_SNAKE_CASE   ,						torch_dtype=torch.floataa )
           UpperCamelCase					       =  pipe.to(_SCREAMING_SNAKE_CASE )
           pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
           pipe.enable_attention_slicing(1 )
           pipe.enable_sequential_cpu_offload()

           UpperCamelCase					       =  self.get_inputs()
           UpperCamelCase					       =  pipe(**_SCREAMING_SNAKE_CASE )

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




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

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

           UpperCamelCase					       =  pipe(**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  output.images[0]

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

           assert image.shape == (504, 504, 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











from pathlib import Path

import fire
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[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)



