"""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


SCREAMING_SNAKE_CASE									=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if isinstance(__UpperCAmelCase     ,torch.Tensor			):
		return image
	elif isinstance(__UpperCAmelCase     ,PIL.Image.Image			):
		_lowercase :    Any							=							[image]

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







class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Tuple			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_lowercase :    Union[str, Any]							=							DDIMScheduler.from_config(scheduler.config						)

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







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					if strength < 0 or strength > 1:
						raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''						)







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Tuple						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							min(int(num_inference_steps * strength						)			,					lowerCamelCase_						)

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

					return timesteps, num_inference_steps - t_start







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       int=None						):

					"""simple docstring"""






					if not isinstance(lowerCamelCase_			,					(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(lowerCamelCase_						)}'''						)

					_lowercase :    Optional[Any]							=							image.to(device=lowerCamelCase_			,					dtype=lowerCamelCase_						)

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

					_lowercase :    List[Any]							=							init_latents.shape
					_lowercase :    int							=							randn_tensor(lowerCamelCase_			,					generator=lowerCamelCase_			,					device=lowerCamelCase_			,					dtype=lowerCamelCase_						)

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

					return latents







				@torch.no_grad()
				def __call__( self    :       str			,					lowerCamelCase_    :       Union[torch.FloatTensor, PIL.Image.Image] = None			,					lowerCamelCase_    :       float = 0.8			,					lowerCamelCase_    :       int = 1			,					lowerCamelCase_    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None			,					lowerCamelCase_    :       float = 0.0			,					lowerCamelCase_    :       int = 5_0			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[str] = "pil"			,					lowerCamelCase_    :       bool = True			,					):

					"""simple docstring"""






					self.check_inputs(lowerCamelCase_						)

					# 2. Preprocess image
					_lowercase :    Union[str, Any]							=							preprocess(lowerCamelCase_						)

					# 3. set timesteps
					self.scheduler.set_timesteps(lowerCamelCase_			,					device=self.device						)
					_lowercase :    str							=							self.get_timesteps(lowerCamelCase_			,					lowerCamelCase_			,					self.device						)
					_lowercase :    List[Any]							=							timesteps[:1].repeat(lowerCamelCase_						)

					# 4. Prepare latent variables
					_lowercase :    Dict							=							self.prepare_latents(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					self.unet.dtype			,					self.device			,					lowerCamelCase_						)
					_lowercase :    str							=							latents

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

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

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

					return ImagePipelineOutput(images=lowerCamelCase_						)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



