


'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class _lowerCamelCase	(						lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      int				   =    StableUnCLIPImgaImgPipeline
		A_     :      Union[str, Any]				   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS
		A_     :      str				   =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		A_     :      Union[str, Any]				   =    frozenset(
		    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		A_     :      List[Any]				   =    frozenset([]							)







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			List[str]:
			__magic_name__	:   Optional[int]					     =					32
			__magic_name__	:   int					     =					embedder_hidden_size

			# image encoding components

			__magic_name__	:   Optional[Any]					     =					CLIPImageProcessor(crop_size=32  ,					size=32      )

			torch.manual_seed(0      )
			__magic_name__	:   int					     =					CLIPVisionModelWithProjection(
			    CLIPVisionConfig(
			        hidden_size=lowercase_  ,					projection_dim=lowercase_  ,					num_hidden_layers=5  ,					num_attention_heads=4  ,					image_size=32  ,					intermediate_size=37  ,					patch_size=1  ,					)      )

			# regular denoising components

			torch.manual_seed(0      )
			__magic_name__	:   List[str]					     =					StableUnCLIPImageNormalizer(embedding_dim=lowercase_      )
			__magic_name__	:   Optional[int]					     =					DDPMScheduler(beta_schedule='squaredcos_cap_v2'      )

			torch.manual_seed(0      )
			__magic_name__	:   int					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

			torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					CLIPTextModel(
			    CLIPTextConfig(
			        bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=lowercase_  ,					projection_dim=32  ,					intermediate_size=37  ,					layer_norm_eps=1E-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1000  ,					)      )

			torch.manual_seed(0      )
			__magic_name__	:   Optional[Any]					     =					UNetaDConditionModel(
			    sample_size=32  ,					in_channels=4  ,					out_channels=4  ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')  ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')  ,					block_out_channels=(32, 64)  ,					attention_head_dim=(2, 4)  ,					class_embed_type='projection'  ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,					cross_attention_dim=lowercase_  ,					layers_per_block=1  ,					upcast_attention=lowercase_  ,					use_linear_projection=lowercase_  ,					)

			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					DDIMScheduler(
			    beta_schedule='scaled_linear'  ,					beta_start=0.0_0085  ,					beta_end=0.012  ,					prediction_type='v_prediction'  ,					set_alpha_to_one=lowercase_  ,					steps_offset=1  ,					)

			torch.manual_seed(0      )
			__magic_name__	:   Optional[int]					     =					AutoencoderKL()

			__magic_name__	:   Union[str, Any]					     =					{
			    # image encoding components
			    'feature_extractor': feature_extractor,
			    'image_encoder': image_encoder.eval(),
			    # image noising components
			    'image_normalizer': image_normalizer.eval(),
			    'image_noising_scheduler': image_noising_scheduler,
			    # regular denoising components
			    'tokenizer': tokenizer,
			    'text_encoder': text_encoder.eval(),
			    'unet': unet.eval(),
			    'scheduler': scheduler,
			    'vae': vae.eval(),
			}

			return components







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Dict  ,					_A		:						int=0  ,					_A		:						Dict=True      )  ->			Tuple:







			if str(lowercase_      ).startswith('mps'      ):
				__magic_name__	:   Optional[int]					     =					torch.manual_seed(lowercase_      )
			else:
				__magic_name__	:   str					     =					torch.Generator(device=lowercase_      ).manual_seed(lowercase_      )

			__magic_name__	:   Optional[Any]					     =					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(lowercase_      )      ).to(lowercase_      )

			if pil_image:
				__magic_name__	:   int					     =					input_image * 0.5 + 0.5
				__magic_name__	:   Tuple					     =					input_image.clamp(0  ,					1      )
				__magic_name__	:   List[str]					     =					input_image.cpu().permute(0  ,					2  ,					3  ,					1      ).float().numpy()
				__magic_name__	:   List[Any]					     =					DiffusionPipeline.numpy_to_pil(lowercase_      )[0]

			return {
			    "prompt": "An anime racoon running a marathon",
			    "image": input_image,
			    "generator": generator,
			    "num_inference_steps": 2,
			    "output_type": "np",
			}







		@skip_mps
		def        __lowerCAmelCase		( self		:						Any      )  ->			Dict:
			__magic_name__	:   List[Any]					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
			__magic_name__	:   List[str]					     =					self.get_dummy_components()
			__magic_name__	:   Optional[Any]					     =					StableUnCLIPImgaImgPipeline(**lowercase_      )
			__magic_name__	:   str					     =					sd_pipe.to(lowercase_      )
			sd_pipe.set_progress_bar_config(disable=lowercase_      )

			__magic_name__	:   int					     =					self.get_dummy_inputs(lowercase_      )
			inputs.update({'image_embeds': None}      )
			__magic_name__	:   Tuple					     =					sd_pipe(**lowercase_      ).images
			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   Dict					     =					np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]      )

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







		def        __lowerCAmelCase		( self		:						Any      )  ->			Union[str, Any]:
			__magic_name__	:   Union[str, Any]					     =					torch_device in ['cpu', 'mps']

			self._test_attention_slicing_forward_pass(test_max_difference=lowercase_      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[Any]:
			__magic_name__	:   int					     =					torch_device in ['cpu', 'mps']

			self._test_inference_batch_single_identical(test_max_difference=lowercase_      )







		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()  ,					reason='XFormers attention is only available with CUDA and `xformers` installed'  ,					)
		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Tuple:
			self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase_      )







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



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Any:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			str:
			__magic_name__	:   Optional[Any]					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'      )

			__magic_name__	:   int					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'      )

			__magic_name__	:   Tuple					     =					StableUnCLIPImgaImgPipeline.from_pretrained(
			    'fusing/stable-unclip-2-1-l-img2img'  ,					torch_dtype=torch.floataa      )
			pipe.to(lowercase_      )
			pipe.set_progress_bar_config(disable=lowercase_      )
			# stable unclip will oom when integration tests are run on a V100,
			# so turn on memory savings
			pipe.enable_attention_slicing()
			pipe.enable_sequential_cpu_offload()

			__magic_name__	:   Optional[int]					     =					torch.Generator(device='cpu'      ).manual_seed(0      )
			__magic_name__	:   Dict					     =					pipe(lowercase_  ,					'anime turle'  ,					generator=lowercase_  ,					output_type='np'      )

			__magic_name__	:   Dict					     =					output.images[0]

			assert image.shape == (768, 768, 3)

			assert_mean_pixel_difference(lowercase_  ,					lowercase_      )







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'      )

			__magic_name__	:   Optional[Any]					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'      )

			__magic_name__	:   Union[str, Any]					     =					StableUnCLIPImgaImgPipeline.from_pretrained(
			    'fusing/stable-unclip-2-1-h-img2img'  ,					torch_dtype=torch.floataa      )
			pipe.to(lowercase_      )
			pipe.set_progress_bar_config(disable=lowercase_      )
			# stable unclip will oom when integration tests are run on a V100,
			# so turn on memory savings
			pipe.enable_attention_slicing()
			pipe.enable_sequential_cpu_offload()

			__magic_name__	:   List[str]					     =					torch.Generator(device='cpu'      ).manual_seed(0      )
			__magic_name__	:   str					     =					pipe(lowercase_  ,					'anime turle'  ,					generator=lowercase_  ,					output_type='np'      )

			__magic_name__	:   List[str]					     =					output.images[0]

			assert image.shape == (768, 768, 3)

			assert_mean_pixel_difference(lowercase_  ,					lowercase_      )







		def        __lowerCAmelCase		( self		:						int      )  ->			Optional[int]:
			__magic_name__	:   int					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'      )

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

			__magic_name__	:   Any					     =					StableUnCLIPImgaImgPipeline.from_pretrained(
			    'fusing/stable-unclip-2-1-h-img2img'  ,					torch_dtype=torch.floataa      )
			__magic_name__	:   List[Any]					     =					pipe.to(lowercase_      )
			pipe.set_progress_bar_config(disable=lowercase_      )
			pipe.enable_attention_slicing()
			pipe.enable_sequential_cpu_offload()

			__magic_name__	:   Optional[Any]					     =					pipe(
			    lowercase_  ,					'anime turtle'  ,					num_inference_steps=2  ,					output_type='np'  ,					)

			__magic_name__	:   Optional[int]					     =					torch.cuda.max_memory_allocated()
			# make sure that less than 7 GB is allocated
			assert mem_bytes < 7 * 10**9



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase				:Optional[Any]        =      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return image
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   List[Any]					     =					[image]

	__magic_name__	:   List[Any]					     =					[trans(img.convert('RGB'		)		) for img in image]
	__magic_name__	:   Dict					     =					torch.stack(lowerCAmelCase		)
	return image





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int      )  ->			Dict:
			super().__init__()

			# make sure scheduler can always be converted to DDIM
			__magic_name__	:   Optional[int]					     =					DDIMScheduler.from_config(scheduler.config      )

			self.register_modules(unet=_A  ,					scheduler=_A      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			Optional[int]:
			if strength < 0 or strength > 1:
				raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						int      )  ->			List[Any]:
			# get the original timestep using init_timestep
			__magic_name__	:   Tuple					     =					min(int(num_inference_steps * strength      )  ,					_A      )

			__magic_name__	:   Any					     =					max(num_inference_steps - init_timestep  ,					0      )
			__magic_name__	:   List[str]					     =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						Optional[int]=None      )  ->			Dict:
			if not isinstance(_A  ,					(torch.Tensor, PIL.Image.Image, list)      ):
				raise ValueError(
				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A      )}'      )

			__magic_name__	:   Union[str, Any]					     =					image.to(device=_A  ,					dtype=_A      )

			if isinstance(_A  ,					_A      ) and len(_A      ) != batch_size:
				raise ValueError(
				    F'You have passed a list of generators of length {len(_A      )}, but requested an effective batch'
				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

			__magic_name__	:   Tuple					     =					init_latents.shape
			__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )

			# get latents
			print('add noise to latents at timestep'  ,					_A      )
			__magic_name__	:   List[str]					     =					self.scheduler.add_noise(_A  ,					_A  ,					_A      )
			__magic_name__	:   List[str]					     =					init_latents

			return latents







		@torch.no_grad()
		def __call__( self		:						Tuple  ,					_A		:						Union[torch.FloatTensor, PIL.Image.Image] = None  ,					_A		:						float = 0.8  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						float = 0.0  ,					_A		:						int = 50  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			self.check_inputs(_A      )

			# 2. Preprocess image
			__magic_name__	:   int					     =					preprocess(_A      )

			# 3. set timesteps
			self.scheduler.set_timesteps(_A  ,					device=self.device      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self.get_timesteps(_A  ,					_A  ,					self.device      )
			__magic_name__	:   Dict					     =					timesteps[:1].repeat(_A      )

			# 4. Prepare latent variables
			__magic_name__	:   Optional[Any]					     =					self.prepare_latents(_A  ,					_A  ,					_A  ,					self.unet.dtype  ,					self.device  ,					_A      )
			__magic_name__	:   Optional[Any]					     =					latents

			# 5. Denoising loop
			for t in self.progress_bar(_A      ):
				# 1. predict noise model_output
				__magic_name__	:   Dict					     =					self.unet(_A  ,					_A      ).sample

				# 2. predict previous mean of image x_t-1 and add variance depending on eta
				# eta corresponds to η in paper and should be between [0, 1]
				# do x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					eta=_A  ,					use_clipped_model_output=_A  ,					generator=_A  ,					).prev_sample

			__magic_name__	:   Union[str, Any]					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Dict					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image, latent_timestep.item())

			return ImagePipelineOutput(images=_A      )