



'''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


UpperCamelCase__:     List[Any]										=    transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     Tuple:
							if isinstance(_lowerCAmelCase     ,   torch.Tensor						):
														return image
							elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image						):
														UpperCAmelCase				: Tuple											=	[image]

							UpperCAmelCase				: str											=	[trans(img.convert('''RGB'''						)						) for img in image]
							UpperCAmelCase				: List[Any]											=	torch.stack(_lowerCAmelCase						)
							return image




class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Dict	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	)    ->	Tuple:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								UpperCAmelCase				: Dict											=	DDIMScheduler.from_config(scheduler.config	)

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







	def   A			(		self    :  Dict	,    __snake_case    :  Tuple	)    ->	List[Any]:
								if strength < 0 or strength > 1:
															raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  List[Any]	,    __snake_case    :  List[Any]	,    __snake_case    :  Tuple	)    ->	Tuple:
								# get the original timestep using init_timestep
								UpperCAmelCase				: int											=	min(int(num_inference_steps * strength	)	,    __snake_case	)

								UpperCAmelCase				: Optional[Any]											=	max(num_inference_steps - init_timestep	,    0	)
								UpperCAmelCase				: Optional[int]											=	self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    __snake_case    :  List[str]	,    __snake_case    :  List[Any]=None	)    ->	Dict:
								if not isinstance(__snake_case	,    (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(__snake_case	)}"""	)

								UpperCAmelCase				: int											=	image.to(device=__snake_case	,    dtype=__snake_case	)

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

								UpperCAmelCase				: List[str]											=	init_latents.shape
								UpperCAmelCase				: Dict											=	randn_tensor(__snake_case	,    generator=__snake_case	,    device=__snake_case	,    dtype=__snake_case	)

								# get latents
								print('''add noise to latents at timestep'''	,    __snake_case	)
								UpperCAmelCase				: List[Any]											=	self.scheduler.add_noise(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: int											=	init_latents

								return latents







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

								# 2. Preprocess image
								UpperCAmelCase				: List[Any]											=	preprocess(__snake_case	)

								# 3. set timesteps
								self.scheduler.set_timesteps(__snake_case	,    device=self.device	)
								UpperCAmelCase							,	UpperCAmelCase				: Any											=	self.get_timesteps(__snake_case	,    __snake_case	,    self.device	)
								UpperCAmelCase				: Union[str, Any]											=	timesteps[:1].repeat(__snake_case	)

								# 4. Prepare latent variables
								UpperCAmelCase				: Dict											=	self.prepare_latents(__snake_case	,    __snake_case	,    __snake_case	,    self.unet.dtype	,    self.device	,    __snake_case	)
								UpperCAmelCase				: Dict											=	latents

								# 5. Denoising loop
								for t in self.progress_bar(__snake_case	):
															# 1. predict noise model_output
															UpperCAmelCase				: List[Any]											=	self.unet(__snake_case	,    __snake_case	).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
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(
															    __snake_case	,    __snake_case	,    __snake_case	,    eta=__snake_case	,    use_clipped_model_output=__snake_case	,    generator=__snake_case	,    ).prev_sample

								UpperCAmelCase				: str											=	(image / 2 + 0.5).clamp(0	,    1	)
								UpperCAmelCase				: Optional[Any]											=	image.cpu().permute(0	,    2	,    3	,    1	).numpy()
								if output_type == "pil":
															UpperCAmelCase				: str											=	self.numpy_to_pil(__snake_case	)

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

								return ImagePipelineOutput(images=__snake_case	)










'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict'''     ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42     ,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]     ,   )
def 				snake_case_  (      _lowerCAmelCase						:		SplitDict						)   ->     List[Any]:
							UpperCAmelCase				: str											=	split_dict._to_yaml_list()
							assert len(_lowerCAmelCase						) == len(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	SplitDict._from_yaml_list(_lowerCAmelCase						)
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														UpperCAmelCase				: Optional[Any]											=	None
														# the split name of split_dict takes over the name of the split info object
														UpperCAmelCase				: Dict											=	split_name
							assert split_dict == reloaded




@pytest.mark.parametrize(
    '''split_info'''     ,   [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[Any]:
							# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
							# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
							UpperCAmelCase				: Any											=	asdict(SplitDict({'''train''': split_info}						)						)
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





