


'''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	(						lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase							):



  '''simple docstring'''







  A_     :      Any				   =    StableDiffusionInstructPixaPixPipeline
  A_     :      Dict				   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
  A_     :      Dict				   =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  A_     :      Dict				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
  A_     :      Tuple				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS







  def        __lowerCAmelCase		( self		:						Any      )  ->			str:
   torch.manual_seed(0      )
   __magic_name__	:   Any					     =					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  ,					)
   __magic_name__	:   Dict					     =					PNDMScheduler(skip_prk_steps=_A      )
   torch.manual_seed(0      )
   __magic_name__	:   str					     =					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      )
   __magic_name__	:   Optional[Any]					     =					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=1000  ,					)
   __magic_name__	:   str					     =					CLIPTextModel(_A      )
   __magic_name__	:   int					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

   __magic_name__	:   List[Any]					     =					{
       'unet': unet,
       'scheduler': scheduler,
       'vae': vae,
       'text_encoder': text_encoder,
       'tokenizer': tokenizer,
       'safety_checker': None,
       'feature_extractor': None,
   }
   return components







  def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						Optional[int]  ,					_A		:						Union[str, Any]=0      )  ->			List[Any]:
   __magic_name__	:   List[str]					     =					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(_A      )      ).to(_A      )
   __magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      )[0]
   __magic_name__	:   Union[str, Any]					     =					Image.fromarray(np.uinta(_A      )      ).convert('RGB'      )
   if str(_A      ).startswith('mps'      ):
    __magic_name__	:   Optional[Any]					     =					torch.manual_seed(_A      )
   else:
    __magic_name__	:   Tuple					     =					torch.Generator(device=_A      ).manual_seed(_A      )
   __magic_name__	:   str					     =					{
       '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		:						Dict      )  ->			Union[str, Any]:
   __magic_name__	:   Dict					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
   __magic_name__	:   List[str]					     =					self.get_dummy_components()
   __magic_name__	:   Any					     =					StableDiffusionInstructPixaPixPipeline(**_A      )
   __magic_name__	:   Tuple					     =					sd_pipe.to(_A      )
   sd_pipe.set_progress_bar_config(disable=_A      )

   __magic_name__	:   int					     =					self.get_dummy_inputs(_A      )
   __magic_name__	:   Tuple					     =					sd_pipe(**_A      ).images
   __magic_name__	:   str					     =					image[0, -3:, -3:, -1]
   assert image.shape == (1, 32, 32, 3)
   __magic_name__	:   Dict					     =					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        __lowerCAmelCase		( self		:						Any      )  ->			Dict:
   __magic_name__	:   Tuple					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
   __magic_name__	:   int					     =					self.get_dummy_components()
   __magic_name__	:   Any					     =					StableDiffusionInstructPixaPixPipeline(**_A      )
   __magic_name__	:   Tuple					     =					sd_pipe.to(_A      )
   sd_pipe.set_progress_bar_config(disable=_A      )

   __magic_name__	:   List[str]					     =					self.get_dummy_inputs(_A      )
   __magic_name__	:   Optional[int]					     =					'french fries'
   __magic_name__	:   str					     =					sd_pipe(**_A  ,					negative_prompt=_A      )
   __magic_name__	:   List[Any]					     =					output.images
   __magic_name__	:   List[Any]					     =					image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   __magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						str      )  ->			Any:
   __magic_name__	:   Any					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
   __magic_name__	:   Union[str, Any]					     =					self.get_dummy_components()
   __magic_name__	:   str					     =					StableDiffusionInstructPixaPixPipeline(**_A      )
   __magic_name__	:   Any					     =					sd_pipe.to(_A      )
   sd_pipe.set_progress_bar_config(disable=_A      )

   __magic_name__	:   Union[str, Any]					     =					self.get_dummy_inputs(_A      )
   __magic_name__	:   str					     =					[inputs['prompt']] * 2

   __magic_name__	:   List[str]					     =					np.array(inputs['image']      ).astype(np.floataa      ) / 255.0
   __magic_name__	:   Tuple					     =					torch.from_numpy(_A      ).unsqueeze(0      ).to(_A      )
   __magic_name__	:   Optional[Any]					     =					image / 2 + 0.5
   __magic_name__	:   Optional[int]					     =					image.permute(0  ,					3  ,					1  ,					2      )
   __magic_name__	:   Optional[Any]					     =					image.repeat(2  ,					1  ,					1  ,					1      )

   __magic_name__	:   str					     =					sd_pipe(**_A      ).images
   __magic_name__	:   Any					     =					image[-1, -3:, -3:, -1]

   assert image.shape == (2, 32, 32, 3)
   __magic_name__	:   Any					     =					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        __lowerCAmelCase		( self		:						str      )  ->			List[str]:
   __magic_name__	:   Union[str, Any]					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
   __magic_name__	:   str					     =					self.get_dummy_components()
   __magic_name__	:   Tuple					     =					EulerAncestralDiscreteScheduler(
       beta_start=0.0_0085  ,					beta_end=0.012  ,					beta_schedule='scaled_linear'      )
   __magic_name__	:   List[str]					     =					StableDiffusionInstructPixaPixPipeline(**_A      )
   __magic_name__	:   Dict					     =					sd_pipe.to(_A      )
   sd_pipe.set_progress_bar_config(disable=_A      )

   __magic_name__	:   List[str]					     =					self.get_dummy_inputs(_A      )
   __magic_name__	:   Optional[int]					     =					sd_pipe(**_A      ).images
   __magic_name__	:   Union[str, Any]					     =					image[0, -3:, -3:, -1]

   __magic_name__	:   Any					     =					[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)
   __magic_name__	:   Any					     =					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        __lowerCAmelCase		( self		:						str      )  ->			Dict:
   super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







  def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Optional[int]:
   __magic_name__	:   Dict					     =					self.get_dummy_components()
   __magic_name__	:   int					     =					StableDiffusionInstructPixaPixPipeline(**_A      )
   __magic_name__	:   Union[str, Any]					     =					VaeImageProcessor(do_resize=_A  ,					do_normalize=_A      )
   __magic_name__	:   List[Any]					     =					pipe.to(_A      )
   pipe.set_progress_bar_config(disable=_A      )

   __magic_name__	:   str					     =					pipe(**self.get_dummy_inputs_by_type(_A  ,					input_image_type='pt'      )      )[0]

   __magic_name__	:   Tuple					     =					components['vae']
   __magic_name__	:   int					     =					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():
     __magic_name__	:   Optional[int]					     =					vae.encode(inputs[image_param]      ).latent_dist.mode()

   __magic_name__	:   Optional[Any]					     =					pipe(**_A      )[0]

   __magic_name__	:   Union[str, Any]					     =					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        __lowerCAmelCase		( self		:						Any      )  ->			Dict:
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







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

   __magic_name__	:   Optional[Any]					     =					self.get_inputs()
   __magic_name__	:   List[Any]					     =					pipe(**_A      ).images
   __magic_name__	:   Any					     =					image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   __magic_name__	:   Dict					     =					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        __lowerCAmelCase		( self		:						List[str]      )  ->			int:
   __magic_name__	:   Tuple					     =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
       'timbrooks/instruct-pix2pix'  ,					safety_checker=_A      )
   __magic_name__	:   Union[str, Any]					     =					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
   pipe.to(_A      )
   pipe.set_progress_bar_config(disable=_A      )
   pipe.enable_attention_slicing()

   __magic_name__	:   Optional[int]					     =					self.get_inputs()
   __magic_name__	:   int					     =					pipe(**_A      ).images
   __magic_name__	:   Dict					     =					image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   __magic_name__	:   str					     =					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        __lowerCAmelCase		( self		:						str      )  ->			List[Any]:
   __magic_name__	:   Union[str, Any]					     =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
       'timbrooks/instruct-pix2pix'  ,					safety_checker=_A      )
   __magic_name__	:   Any					     =					DDIMScheduler.from_config(pipe.scheduler.config      )
   pipe.to(_A      )
   pipe.set_progress_bar_config(disable=_A      )
   pipe.enable_attention_slicing()

   __magic_name__	:   List[Any]					     =					self.get_inputs()
   __magic_name__	:   int					     =					pipe(**_A      ).images
   __magic_name__	:   Optional[Any]					     =					image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 512, 3)
   __magic_name__	:   List[Any]					     =					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        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
   __magic_name__	:   Optional[Any]					     =					0

   def callback_fn(_A		:						int  ,					_A		:						int  ,					_A		:						torch.FloatTensor      ) -> None:
    __magic_name__	:   Dict					     =					True
    nonlocal number_of_steps
    number_of_steps += 1
    if step == 1:
     __magic_name__	:   Union[str, Any]					     =					latents.detach().cpu().numpy()
     assert latents.shape == (1, 4, 64, 64)
     __magic_name__	:   List[Any]					     =					latents[0, -3:, -3:, -1]
     __magic_name__	:   Optional[Any]					     =					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:
     __magic_name__	:   List[Any]					     =					latents.detach().cpu().numpy()
     assert latents.shape == (1, 4, 64, 64)
     __magic_name__	:   Tuple					     =					latents[0, -3:, -3:, -1]
     __magic_name__	:   Dict					     =					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

   __magic_name__	:   Any					     =					False

   __magic_name__	:   List[str]					     =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
       'timbrooks/instruct-pix2pix'  ,					safety_checker=_A  ,					torch_dtype=torch.floataa      )
   __magic_name__	:   str					     =					pipe.to(_A      )
   pipe.set_progress_bar_config(disable=_A      )
   pipe.enable_attention_slicing()

   __magic_name__	:   Optional[int]					     =					self.get_inputs()
   pipe(**_A  ,					callback=_A  ,					callback_steps=1      )
   assert callback_fn.has_been_called
   assert number_of_steps == 3







  def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   __magic_name__	:   Any					     =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
       'timbrooks/instruct-pix2pix'  ,					safety_checker=_A  ,					torch_dtype=torch.floataa      )
   __magic_name__	:   List[Any]					     =					pipe.to(_A      )
   pipe.set_progress_bar_config(disable=_A      )
   pipe.enable_attention_slicing(1      )
   pipe.enable_sequential_cpu_offload()

   __magic_name__	:   List[Any]					     =					self.get_inputs()
   __magic_name__	:   Union[str, Any]					     =					pipe(**_A      )

   __magic_name__	:   Optional[int]					     =					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		:						List[str]      )  ->			Any:
   __magic_name__	:   Any					     =					self.get_inputs()
   # resize to resolution that is divisible by 8 but not 16 or 32
   __magic_name__	:   Tuple					     =					inputs['image'].resize((504, 504)      )

   __magic_name__	:   Union[str, Any]					     =					'timbrooks/instruct-pix2pix'
   __magic_name__	:   Dict					     =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
       _A  ,					safety_checker=_A  ,					)
   pipe.to(_A      )
   pipe.set_progress_bar_config(disable=_A      )
   pipe.enable_attention_slicing()

   __magic_name__	:   Optional[int]					     =					pipe(**_A      )
   __magic_name__	:   int					     =					output.images[0]

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

   assert image.shape == (504, 504, 3)
   __magic_name__	:   Dict					     =					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



'''simple docstring'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}