





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 			__A       (      _UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      StableDiffusionInstructPixaPixPipeline
      UpperCAmelCase__						=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
      UpperCAmelCase__						=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase__						=      IMAGE_TO_IMAGE_IMAGE_PARAMS
      UpperCAmelCase__						=      IMAGE_TO_IMAGE_IMAGE_PARAMS


      def 				__snake_case			(		self):



             """simple docstring"""





             torch.manual_seed(0)
             _lowerCamelCase							:     Optional[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      ,							)
             _lowerCamelCase							:     List[str]	=		PNDMScheduler(skip_prk_steps=_UpperCAmelCase)
             torch.manual_seed(0)
             _lowerCamelCase							:     int	=		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)
             _lowerCamelCase							:     str	=		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      ,							)
             _lowerCamelCase							:     Optional[int]	=		CLIPTextModel(_UpperCAmelCase)
             _lowerCamelCase							:     str	=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

             _lowerCamelCase							:     Union[str, Any]	=		{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components


      def 				__snake_case			(		self      ,							a__      ,							a__=0):



             """simple docstring"""





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



             """simple docstring"""





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

             _lowerCamelCase							:     Union[str, Any]	=		self.get_dummy_inputs(_UpperCAmelCase)
             _lowerCamelCase							:     str	=		sd_pipe(**_UpperCAmelCase).images
             _lowerCamelCase							:     List[Any]	=		image[0, -3:, -3:, -1]
             assert image.shape == (1, 32, 32, 3)
             _lowerCamelCase							:     Optional[int]	=		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 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Dict	=		self.get_dummy_inputs(_UpperCAmelCase)
             _lowerCamelCase							:     Union[str, Any]	=		'''french fries'''
             _lowerCamelCase							:     str	=		sd_pipe(**_UpperCAmelCase      ,							negative_prompt=_UpperCAmelCase)
             _lowerCamelCase							:     Dict	=		output.images
             _lowerCamelCase							:     Optional[int]	=		image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     Union[str, Any]	=		self.get_dummy_inputs(_UpperCAmelCase)
             _lowerCamelCase							:     List[str]	=		[inputs['''prompt''']] * 2

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

             _lowerCamelCase							:     int	=		sd_pipe(**_UpperCAmelCase).images
             _lowerCamelCase							:     Optional[Any]	=		image[-1, -3:, -3:, -1]

             assert image.shape == (2, 32, 32, 3)
             _lowerCamelCase							:     List[str]	=		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 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     Optional[int]	=		[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)
             _lowerCamelCase							:     int	=		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 				__snake_case			(		self):



             """simple docstring"""





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



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		self.get_dummy_components()
             _lowerCamelCase							:     Optional[Any]	=		StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase)
             _lowerCamelCase							:     Any	=		VaeImageProcessor(do_resize=_UpperCAmelCase      ,							do_normalize=_UpperCAmelCase)
             _lowerCamelCase							:     Dict	=		pipe.to(_UpperCAmelCase)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase)

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

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

             _lowerCamelCase							:     str	=		pipe(**_UpperCAmelCase)[0]

             _lowerCamelCase							:     Union[str, Any]	=		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 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							a__=0):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		torch.manual_seed(_UpperCAmelCase)
             _lowerCamelCase							:     str	=		load_image(
                 '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''')
             _lowerCamelCase							:     List[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 				__snake_case			(		self):



             """simple docstring"""





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

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

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase							:     Tuple	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 '''timbrooks/instruct-pix2pix'''      ,							safety_checker=_UpperCAmelCase)
             _lowerCamelCase							:     Optional[int]	=		LMSDiscreteScheduler.from_config(pipe.scheduler.config)
             pipe.to(_UpperCAmelCase)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase)
             pipe.enable_attention_slicing()

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

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase							:     Tuple	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		StableDiffusionInstructPixaPixPipeline.from_pretrained(
                 '''timbrooks/instruct-pix2pix'''      ,							safety_checker=_UpperCAmelCase)
             _lowerCamelCase							:     Optional[int]	=		DDIMScheduler.from_config(pipe.scheduler.config)
             pipe.to(_UpperCAmelCase)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase)
             pipe.enable_attention_slicing()

             _lowerCamelCase							:     List[str]	=		self.get_inputs()
             _lowerCamelCase							:     str	=		pipe(**_UpperCAmelCase).images
             _lowerCamelCase							:     Optional[int]	=		image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		0

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

             _lowerCamelCase							:     Union[str, Any]	=		False

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     List[Any]	=		self.get_inputs()
             _lowerCamelCase							:     Tuple	=		pipe(**_UpperCAmelCase)

             _lowerCamelCase							:     List[str]	=		torch.cuda.max_memory_allocated()
             # make sure that less than 2.2 GB is allocated
             assert mem_bytes < 2.2 * 10**9



      def 				__snake_case			(		self):



             """simple docstring"""





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

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

             _lowerCamelCase							:     List[Any]	=		pipe(**_UpperCAmelCase)
             _lowerCamelCase							:     int	=		output.images[0]

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

             assert image.shape == (504, 504, 3)
             _lowerCamelCase							:     Union[str, Any]	=		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    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float(moles / volume      ) * nfactor      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (volume)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((pressure * volume) / (0.0_8_2_1 * moles)      )      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

