


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_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		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 		UpperCamelCase__  (			self      ):
       torch.manual_seed(0      )
       snake_case_ =   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	,   )
       snake_case_ =   PNDMScheduler(skip_prk_steps=_UpperCAmelCase      )
       torch.manual_seed(0      )
       snake_case_ =   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      )
       snake_case_ =   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	,   )
       snake_case_ =   CLIPTextModel(_UpperCAmelCase      )
       snake_case_ =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=0      ):
       snake_case_ =   floats_tensor((1, 3, 32, 32)	,   rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )
       snake_case_ =   image.cpu().permute(0	,   2	,   3	,   1      )[0]
       snake_case_ =   Image.fromarray(np.uinta(_UpperCAmelCase      )      ).convert('''RGB'''      )
       if str(_UpperCAmelCase      ).startswith('''mps'''      ):
             snake_case_ =   torch.manual_seed(_UpperCAmelCase      )
       else:
             snake_case_ =   torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )
       snake_case_ =   {
           '''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      ):
       snake_case_ =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_ =   self.get_dummy_components()
       snake_case_ =   StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase      )
       snake_case_ =   sd_pipe.to(_UpperCAmelCase      )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   self.get_dummy_inputs(_UpperCAmelCase      )
       snake_case_ =   sd_pipe(**_UpperCAmelCase      ).images
       snake_case_ =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       snake_case_ =   np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_ =   self.get_dummy_components()
       snake_case_ =   StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase      )
       snake_case_ =   sd_pipe.to(_UpperCAmelCase      )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

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

       assert image.shape == (1, 32, 32, 3)
       snake_case_ =   np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_ =   self.get_dummy_components()
       snake_case_ =   StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase      )
       snake_case_ =   sd_pipe.to(_UpperCAmelCase      )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   self.get_dummy_inputs(_UpperCAmelCase      )
       snake_case_ =   [inputs['''prompt''']] * 2

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

       snake_case_ =   sd_pipe(**_UpperCAmelCase      ).images
       snake_case_ =   image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       snake_case_ =   np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_ =   self.get_dummy_components()
       snake_case_ =   EulerAncestralDiscreteScheduler(
           beta_start=0.00_085	,   beta_end=0.012	,   beta_schedule='''scaled_linear'''      )
       snake_case_ =   StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase      )
       snake_case_ =   sd_pipe.to(_UpperCAmelCase      )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

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

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

       assert image.shape == (1, 32, 32, 3)
       snake_case_ =   np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.get_dummy_components()
       snake_case_ =   StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase      )
       snake_case_ =   VaeImageProcessor(do_resize=_UpperCAmelCase	,   do_normalize=_UpperCAmelCase      )
       snake_case_ =   pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase	,   input_image_type='''pt'''      )      )[0]

       snake_case_ =   components['''vae''']
       snake_case_ =   self.get_dummy_inputs_by_type(_UpperCAmelCase	,   input_image_type='''pt'''      )

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

       snake_case_ =   pipe(**_UpperCAmelCase      )[0]

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







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

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase=0      ):
       snake_case_ =   torch.manual_seed(_UpperCAmelCase      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''      )
       snake_case_ =   {
           '''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      ):
       snake_case_ =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''	,   safety_checker=_UpperCAmelCase      )
       pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       pipe.enable_attention_slicing()

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

       assert image.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''	,   safety_checker=_UpperCAmelCase      )
       snake_case_ =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       pipe.enable_attention_slicing()

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

       assert image.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''	,   safety_checker=_UpperCAmelCase      )
       snake_case_ =   DDIMScheduler.from_config(pipe.scheduler.config      )
       pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       pipe.enable_attention_slicing()

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

       assert image.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   0

       def callback_fn(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ) -> None:
             snake_case_ =   True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                   snake_case_ =   latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   snake_case_ =   latents[0, -3:, -3:, -1]
                   snake_case_ =   np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

                   assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
             elif step == 2:
                   snake_case_ =   latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   snake_case_ =   latents[0, -3:, -3:, -1]
                   snake_case_ =   np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

                   assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

       snake_case_ =   False

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

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



 def 		UpperCamelCase__  (			self      ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       snake_case_ =   self.get_inputs()
       snake_case_ =   pipe(**_UpperCAmelCase      )

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

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

       snake_case_ =   pipe(**_UpperCAmelCase      )
       snake_case_ =   output.images[0]

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

       assert image.shape == (5_04, 5_04, 3)
       snake_case_ =   np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-3



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 6008_5147_5143      )->						int:




      """simple docstring"""

      try:
            snake_case_ =   int(SCREAMING_SNAKE_CASE      )
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''      )
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''      )
      snake_case_ =   1
      snake_case_ =   2
      while i * i <= n:
            while n % i == 0:
                  snake_case_ =   i
                  n //= i
            i += 1
      if n > 1:
            snake_case_ =   n
      return int(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   print(f'''{solution() = }''')