'''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 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= StableDiffusionInstructPixaPixPipeline
      UpperCAmelCase_   :   int   				= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
      UpperCAmelCase_   :   List[str]   				= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase_   :   Optional[int]   				= IMAGE_TO_IMAGE_IMAGE_PARAMS
      UpperCAmelCase_   :   int   				= IMAGE_TO_IMAGE_IMAGE_PARAMS




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Dict 					=	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__				:					List[str] 					=	PNDMScheduler(skip_prk_steps=lowerCamelCase_				)
         torch.manual_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	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__				:					Dict 					=	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       ,)
         UpperCAmelCase__				:					str 					=	CLIPTextModel(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	floats_tensor((1, 3, 32, 32)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	image.cpu().permute(0       ,2       ,3       ,1				)[0]
         UpperCAmelCase__				:					int 					=	Image.fromarray(np.uinta(lowerCamelCase_				)				).convert('''RGB'''				)
         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					str 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Any 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	{
             '''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				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					Optional[int] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					Union[str, Any] 					=	StableDiffusionInstructPixaPixPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	sd_pipe.to(lowerCamelCase_				)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	sd_pipe(**lowerCamelCase_				).images
         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase__				:					Tuple 					=	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				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					Optional[int] 					=	StableDiffusionInstructPixaPixPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	sd_pipe.to(lowerCamelCase_				)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

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

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase__				:					Optional[Any] 					=	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				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					List[Any] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					int 					=	StableDiffusionInstructPixaPixPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	sd_pipe.to(lowerCamelCase_				)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	[inputs['''prompt''']] * 2

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

         UpperCAmelCase__				:					Optional[int] 					=	sd_pipe(**lowerCamelCase_				).images
         UpperCAmelCase__				:					Tuple 					=	image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         UpperCAmelCase__				:					Union[str, 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				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					Union[str, Any] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					int 					=	EulerAncestralDiscreteScheduler(
             beta_start=0.0_0085       ,beta_end=0.012       ,beta_schedule='''scaled_linear'''				)
         UpperCAmelCase__				:					Any 					=	StableDiffusionInstructPixaPixPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	sd_pipe.to(lowerCamelCase_				)
         sd_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	sd_pipe(**lowerCamelCase_				).images
         UpperCAmelCase__				:					str 					=	image[0, -3:, -3:, -1]

         UpperCAmelCase__				:					int 					=	[round(lowerCamelCase_       ,4				) for x in image_slice.flatten().tolist()]
         print(''','''.join([str(lowerCamelCase_				) for x in slice]				)				)

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase__				:					Optional[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				)      ->      Optional[int]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					Optional[Any] 					=	StableDiffusionInstructPixaPixPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	VaeImageProcessor(do_resize=lowerCamelCase_       ,do_normalize=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	pipe.to(lowerCamelCase_				)
         pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	pipe(**self.get_dummy_inputs_by_type(lowerCamelCase_       ,input_image_type='''pt'''				)				)[0]

         UpperCAmelCase__				:					Any 					=	components['''vae''']
         UpperCAmelCase__				:					List[Any] 					=	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():
               UpperCAmelCase__				:					Tuple 					=	vae.encode(inputs[image_param]				).latent_dist.mode()

         UpperCAmelCase__				:					Union[str, Any] 					=	pipe(**lowerCamelCase_				)[0]

         UpperCAmelCase__				:					Any 					=	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 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=0				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	torch.manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	{
             '''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[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''       ,safety_checker=lowerCamelCase_				)
         pipe.to(lowerCamelCase_				)
         pipe.set_progress_bar_config(disable=lowerCamelCase_				)
         pipe.enable_attention_slicing()

         UpperCAmelCase__				:					Dict 					=	self.get_inputs()
         UpperCAmelCase__				:					Any 					=	pipe(**lowerCamelCase_				).images
         UpperCAmelCase__				:					Union[str, Any] 					=	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase__				:					List[Any] 					=	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				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''       ,safety_checker=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
         pipe.to(lowerCamelCase_				)
         pipe.set_progress_bar_config(disable=lowerCamelCase_				)
         pipe.enable_attention_slicing()

         UpperCAmelCase__				:					Tuple 					=	self.get_inputs()
         UpperCAmelCase__				:					Tuple 					=	pipe(**lowerCamelCase_				).images
         UpperCAmelCase__				:					Optional[Any] 					=	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase__				:					List[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:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''       ,safety_checker=lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	DDIMScheduler.from_config(pipe.scheduler.config				)
         pipe.to(lowerCamelCase_				)
         pipe.set_progress_bar_config(disable=lowerCamelCase_				)
         pipe.enable_attention_slicing()

         UpperCAmelCase__				:					int 					=	self.get_inputs()
         UpperCAmelCase__				:					int 					=	pipe(**lowerCamelCase_				).images
         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase__				:					List[str] 					=	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				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	0

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

         UpperCAmelCase__				:					Union[str, Any] 					=	False

         UpperCAmelCase__				:					int 					=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''       ,safety_checker=lowerCamelCase_       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	pipe.to(lowerCamelCase_				)
         pipe.set_progress_bar_config(disable=lowerCamelCase_				)
         pipe.enable_attention_slicing()

         UpperCAmelCase__				:					List[str] 					=	self.get_inputs()
         pipe(**lowerCamelCase_       ,callback=lowerCamelCase_       ,callback_steps=1				)
         assert callback_fn.has_been_called
         assert number_of_steps == 3




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase__				:					int 					=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix'''       ,safety_checker=lowerCamelCase_       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Optional[int] 					=	pipe.to(lowerCamelCase_				)
         pipe.set_progress_bar_config(disable=lowerCamelCase_				)
         pipe.enable_attention_slicing(1				)
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase__				:					List[str] 					=	self.get_inputs()
         UpperCAmelCase__				:					Tuple 					=	pipe(**lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	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[Any]:



         '''simple docstring'''



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

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

         UpperCAmelCase__				:					str 					=	pipe(**lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	output.images[0]

         UpperCAmelCase__				:					List[Any] 					=	image[255:258, 383:386, -1]

         assert image.shape == (504, 504, 3)
         UpperCAmelCase__				:					str 					=	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'''

import baseaa







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''
   return baseaa.baaencode(string.encode('''utf-8'''     )     )







def __UpperCamelCase( _A       :					bytes     ):
   '''simple docstring'''
   return baseaa.baadecode(_A     ).decode('''utf-8'''     )


if __name__ == "__main__":
  UpperCamelCase__  :						List[str]					=      'Hello World!'
  UpperCamelCase__  :						str					=      baseaa_encode(test)
  print(encoded)

  UpperCamelCase__  :						int					=      baseaa_decode(encoded)
  print(decoded)




