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       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     StableDiffusionInstructPixaPixPipeline
  lowercase_			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
  lowercase_			=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowercase_			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowercase_			=     IMAGE_TO_IMAGE_IMAGE_PARAMS




  def 	__lowercase(      self     :	str				)->       Dict:




       """simple docstring"""
       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				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     , )
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		PNDMScheduler(skip_prk_steps=a_				)
       torch.manual_seed(0				)
       SCREAMING_SNAKE_CASE__				:				List[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				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		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     , )
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		CLIPTextModel(a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Union[str, Any]     , a_     :	List[str]=0				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		floats_tensor((1, 3, 32, 32)     , rng=random.Random(a_				)				).to(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image.cpu().permute(0     , 2     , 3     , 1				)[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		Image.fromarray(np.uinta(a_				)				).convert('RGB'				)
       if str(a_				).startswith('mps'				):
            SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.manual_seed(a_				)
       else:
            SCREAMING_SNAKE_CASE__				:				str			=		torch.Generator(device=a_				).manual_seed(a_				)
       SCREAMING_SNAKE_CASE__				:				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 	__lowercase(      self     :	Any				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		StableDiffusionInstructPixaPixPipeline(**a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		sd_pipe.to(a_				)
       sd_pipe.set_progress_bar_config(disable=a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.get_dummy_inputs(a_				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		sd_pipe(**a_				).images
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__				:				Tuple			=		StableDiffusionInstructPixaPixPipeline(**a_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		sd_pipe.to(a_				)
       sd_pipe.set_progress_bar_config(disable=a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_dummy_inputs(a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'french fries'
       SCREAMING_SNAKE_CASE__				:				Tuple			=		sd_pipe(**a_     , negative_prompt=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		output.images
       SCREAMING_SNAKE_CASE__				:				Dict			=		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__				:				Dict			=		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 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		StableDiffusionInstructPixaPixPipeline(**a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		sd_pipe.to(a_				)
       sd_pipe.set_progress_bar_config(disable=a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_dummy_inputs(a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		[inputs['prompt']] * 2

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		sd_pipe(**a_				).images
       SCREAMING_SNAKE_CASE__				:				List[str]			=		image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       SCREAMING_SNAKE_CASE__				:				Optional[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 	__lowercase(      self     :	str				)->       List[str]:




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

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

       SCREAMING_SNAKE_CASE__				:				List[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)
       SCREAMING_SNAKE_CASE__				:				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 	__lowercase(      self     :	int				)->       str:




       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		StableDiffusionInstructPixaPixPipeline(**a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		VaeImageProcessor(do_resize=a_     , do_normalize=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		pipe.to(a_				)
       pipe.set_progress_bar_config(disable=a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		pipe(**self.get_dummy_inputs_by_type(a_     , input_image_type='pt'				)				)[0]

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		components['vae']
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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():
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		vae.encode(inputs[image_param]				).latent_dist.mode()

       SCREAMING_SNAKE_CASE__				:				Any			=		pipe(**a_				)[0]

       SCREAMING_SNAKE_CASE__				:				List[str]			=		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       snake_case					(      unittest.TestCase						):




  def 	__lowercase(      self     :	Optional[int]				)->       Any:




       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




  def 	__lowercase(      self     :	int     , a_     :	Union[str, Any]=0				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.manual_seed(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'				)
       SCREAMING_SNAKE_CASE__				:				str			=		{
           '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 	__lowercase(      self     :	Tuple				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'     , safety_checker=a_				)
       pipe.to(a_				)
       pipe.set_progress_bar_config(disable=a_				)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.get_inputs()
       SCREAMING_SNAKE_CASE__				:				Any			=		pipe(**a_				).images
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__				:				str			=		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 	__lowercase(      self     :	int				)->       Optional[int]:




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

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'     , safety_checker=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		DDIMScheduler.from_config(pipe.scheduler.config				)
       pipe.to(a_				)
       pipe.set_progress_bar_config(disable=a_				)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_inputs()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pipe(**a_				).images
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__				:				Dict			=		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 	__lowercase(      self     :	List[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		0

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

       SCREAMING_SNAKE_CASE__				:				Any			=		False

       SCREAMING_SNAKE_CASE__				:				int			=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'     , safety_checker=a_     , torch_dtype=torch.floataa				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		pipe.to(a_				)
       pipe.set_progress_bar_config(disable=a_				)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_inputs()
       pipe(**a_     , callback=a_     , callback_steps=1				)
       assert callback_fn.has_been_called
       assert number_of_steps == 3




  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[Any]:




       """simple docstring"""
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       SCREAMING_SNAKE_CASE__				:				int			=		self.get_inputs()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		pipe(**a_				)

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




  def 	__lowercase(      self     :	int				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       SCREAMING_SNAKE_CASE__				:				int			=		inputs['image'].resize((504, 504)				)

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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pipe(**a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		output.images[0]

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       SCREAMING_SNAKE_CASE__				:				int			=		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




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		angle_in_radians
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		3
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		-1

     for _ in range(lowercase__ ):
          result += (b * (angle_in_radians**a)) / factorial(lowercase__ )

          SCREAMING_SNAKE_CASE__				:				Any			=		-b  # One positive term and the next will be negative and so on...
          a += 2  # Increased by 2 for every term.

     return round(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      __import__("doctest").testmod()



