



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 ):
  __lowerCamelCase		=      StableDiffusionInstructPixaPixPipeline
  __lowerCamelCase		=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
  __lowerCamelCase		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __lowerCamelCase		=      IMAGE_TO_IMAGE_IMAGE_PARAMS
  __lowerCamelCase		=      IMAGE_TO_IMAGE_IMAGE_PARAMS







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     torch.manual_seed(0 )
     lowercase__			       =  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   ,							)
     lowercase__			       =  PNDMScheduler(skip_prk_steps=_lowercase )
     torch.manual_seed(0 )
     lowercase__			       =  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 )
     lowercase__			       =  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=10_00   ,							)
     lowercase__			       =  CLIPTextModel(_lowercase )
     lowercase__			       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

     lowercase__			       =  {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :List[str]   ,							_lowercase  :str=0 ):






     '''simple docstring'''





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






     '''simple docstring'''





     lowercase__			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowercase__			       =  self.get_dummy_components()
     lowercase__			       =  StableDiffusionInstructPixaPixPipeline(**_lowercase )
     lowercase__			       =  sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

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






     '''simple docstring'''





     lowercase__			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowercase__			       =  self.get_dummy_components()
     lowercase__			       =  StableDiffusionInstructPixaPixPipeline(**_lowercase )
     lowercase__			       =  sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     lowercase__			       =  self.get_dummy_inputs(_lowercase )
     lowercase__			       =  "french fries"
     lowercase__			       =  sd_pipe(**_lowercase   ,							negative_prompt=_lowercase )
     lowercase__			       =  output.images
     lowercase__			       =  image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     lowercase__			       =  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   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowercase__			       =  self.get_dummy_components()
     lowercase__			       =  StableDiffusionInstructPixaPixPipeline(**_lowercase )
     lowercase__			       =  sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     lowercase__			       =  self.get_dummy_inputs(_lowercase )
     lowercase__			       =  [inputs["prompt"]] * 2

     lowercase__			       =  np.array(inputs["image"] ).astype(np.floataa ) / 255.0
     lowercase__			       =  torch.from_numpy(_lowercase ).unsqueeze(0 ).to(_lowercase )
     lowercase__			       =  image / 2 + 0.5
     lowercase__			       =  image.permute(0   ,							3   ,							1   ,							2 )
     lowercase__			       =  image.repeat(2   ,							1   ,							1   ,							1 )

     lowercase__			       =  sd_pipe(**_lowercase ).images
     lowercase__			       =  image[-1, -3:, -3:, -1]

     assert image.shape == (2, 32, 32, 3)
     lowercase__			       =  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   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowercase__			       =  self.get_dummy_components()
     lowercase__			       =  EulerAncestralDiscreteScheduler(
         beta_start=0.00085   ,							beta_end=0.012   ,							beta_schedule="scaled_linear" )
     lowercase__			       =  StableDiffusionInstructPixaPixPipeline(**_lowercase )
     lowercase__			       =  sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

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

     lowercase__			       =  [round(_lowercase   ,							4 ) for x in image_slice.flatten().tolist()]
     print(",".join([str(_lowercase ) for x in slice] ) )

     assert image.shape == (1, 32, 32, 3)
     lowercase__			       =  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   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





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







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






     '''simple docstring'''





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

     lowercase__			       =  pipe(**self.get_dummy_inputs_by_type(_lowercase   ,							input_image_type="pt" ) )[0]

     lowercase__			       =  components["vae"]
     lowercase__			       =  self.get_dummy_inputs_by_type(_lowercase   ,							input_image_type="pt" )

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

     lowercase__			       =  pipe(**_lowercase )[0]

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





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







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






     '''simple docstring'''





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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Optional[int]=0 ):






     '''simple docstring'''





     lowercase__			       =  torch.manual_seed(_lowercase )
     lowercase__			       =  load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
     lowercase__			       =  {
         "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   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"   ,							safety_checker=_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_12, 5_12, 3)
     lowercase__			       =  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   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"   ,							safety_checker=_lowercase )
     lowercase__			       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_12, 5_12, 3)
     lowercase__			       =  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   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"   ,							safety_checker=_lowercase )
     lowercase__			       =  DDIMScheduler.from_config(pipe.scheduler.config )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_12, 5_12, 3)
     lowercase__			       =  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   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  0

     def callback_fn(_lowercase  :int   ,							_lowercase  :int   ,							_lowercase  :torch.FloatTensor ) -> None:
        lowercase__			       =  True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           lowercase__			       =  latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           lowercase__			       =  latents[0, -3:, -3:, -1]
           lowercase__			       =  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:
           lowercase__			       =  latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           lowercase__			       =  latents[0, -3:, -3:, -1]
           lowercase__			       =  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

     lowercase__			       =  False

     lowercase__			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"   ,							safety_checker=_lowercase   ,							torch_dtype=torch.floataa )
     lowercase__			       =  pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

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







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





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

     lowercase__			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix"   ,							safety_checker=_lowercase   ,							torch_dtype=torch.floataa )
     lowercase__			       =  pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing(1 )
     pipe.enable_sequential_cpu_offload()

     lowercase__			       =  self.get_inputs()
     lowercase__			       =  pipe(**_lowercase )

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







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     lowercase__			       =  inputs["image"].resize((5_04, 5_04) )

     lowercase__			       =  "timbrooks/instruct-pix2pix"
     lowercase__			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
         _lowercase   ,							safety_checker=_lowercase   ,							)
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

     lowercase__			       =  pipe(**_lowercase )
     lowercase__			       =  output.images[0]

     lowercase__			       =  image[2_55:2_58, 3_83:3_86, -1]

     assert image.shape == (5_04, 5_04, 3)
     lowercase__			       =  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













def _A       (   __magic_name__	):
   try:
      lowercase__			       =  float(__magic_name__	)
   except ValueError:
      raise ValueError("Please enter a valid number"	)
   lowercase__			       =  decimal - int(__magic_name__	)
   if fractional_part == 0:
      return int(__magic_name__	), 1
   else:
      lowercase__			       =  len(str(__magic_name__	).split("."	)[1]	)
      lowercase__			       =  int(decimal * (10**number_of_frac_digits)	)
      lowercase__			       =  10**number_of_frac_digits
      lowercase__       ,							lowercase__			       =  denominator, numerator
      while True:
         lowercase__			       =  dividend % divisor
         if remainder == 0:
            break
         lowercase__       ,							lowercase__			       =  divisor, remainder
      lowercase__       ,							lowercase__			       =  numerator / divisor, denominator / divisor
      return int(__magic_name__	), int(__magic_name__	)


if __name__ == "__main__":
  print(F"""{decimal_to_fraction(2) = }""")
  print(F"""{decimal_to_fraction(89.0) = }""")
  print(F"""{decimal_to_fraction("67") = }""")
  print(F"""{decimal_to_fraction("45.0") = }""")
  print(F"""{decimal_to_fraction(1.5) = }""")
  print(F"""{decimal_to_fraction("6.25") = }""")
  print(F"""{decimal_to_fraction("78td") = }""")






