



'''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


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





def 		__snake_case(							_lowerCAmelCase						)						->					List[Any]:
      if isinstance(_lowerCAmelCase	,  torch.Tensor						):
            return image
      elif isinstance(_lowerCAmelCase	,  PIL.Image.Image						):
            snake_case__    :       List[str]       		= [image]

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







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	str					,	snake_case_   :	List[Any]					,	snake_case_   :	Optional[int]		):
          super().__init__()

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

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




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	Optional[Any]		):
          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   :	Dict					,	snake_case_   :	int					,	snake_case_   :	Any					,	snake_case_   :	List[str]		):
          # get the original timestep using init_timestep
          snake_case__    :       str       		= min(int(num_inference_steps * strength		)					,	snake_case_		)

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

          return timesteps, num_inference_steps - t_start




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	Any					,	snake_case_   :	str					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	Any					,	snake_case_   :	Optional[Any]=None		):
          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_		)}"		)

          snake_case__    :       str       		= 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."		)

          snake_case__    :       Union[str, Any]       		= init_latents.shape
          snake_case__    :       Union[str, Any]       		= randn_tensor(snake_case_					,	generator=snake_case_					,	device=snake_case_					,	dtype=snake_case_		)

          # get latents
          print("""add noise to latents at timestep"""					,	snake_case_		)
          snake_case__    :       Optional[Any]       		= self.scheduler.add_noise(snake_case_					,	snake_case_					,	snake_case_		)
          snake_case__    :       Dict       		= init_latents

          return latents




    @torch.no_grad()
    def __call__(       self   :	List[str]					,	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					,	):
          self.check_inputs(snake_case_		)

          # 2. Preprocess image
          snake_case__    :       int       		= preprocess(snake_case_		)

          # 3. set timesteps
          self.scheduler.set_timesteps(snake_case_					,	device=self.device		)
          snake_case__		,							snake_case__    :       Optional[Any]       		= self.get_timesteps(snake_case_					,	snake_case_					,	self.device		)
          snake_case__    :       List[Any]       		= timesteps[:1].repeat(snake_case_		)

          # 4. Prepare latent variables
          snake_case__    :       List[Any]       		= self.prepare_latents(snake_case_					,	snake_case_					,	snake_case_					,	self.unet.dtype					,	self.device					,	snake_case_		)
          snake_case__    :       List[Any]       		= latents

          # 5. Denoising loop
          for t in self.progress_bar(snake_case_		):
                # 1. predict noise model_output
                snake_case__    :       Dict       		= 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
                snake_case__    :       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

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

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

          return ImagePipelineOutput(images=snake_case_		)





'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						"dinat"

    lowercase   =						{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	List[str]=3					,	snake_case_   :	Tuple=64					,	snake_case_   :	List[Any]=[3, 4, 6, 5]					,	snake_case_   :	List[Any]=[2, 4, 8, 16]					,	snake_case_   :	List[str]=7					,	snake_case_   :	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,	snake_case_   :	List[Any]=3.0					,	snake_case_   :	Dict=True					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	str=0.1					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-5					,	snake_case_   :	str=0.0					,	snake_case_   :	Dict=None					,	snake_case_   :	Dict=None					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Dict       		= depths
          snake_case__    :       Dict       		= len(snake_case_		)
          snake_case__    :       Optional[int]       		= num_heads
          snake_case__    :       Any       		= kernel_size
          snake_case__    :       str       		= dilations
          snake_case__    :       Any       		= mlp_ratio
          snake_case__    :       int       		= qkv_bias
          snake_case__    :       int       		= hidden_dropout_prob
          snake_case__    :       int       		= attention_probs_dropout_prob
          snake_case__    :       Optional[Any]       		= drop_path_rate
          snake_case__    :       int       		= hidden_act
          snake_case__    :       Tuple       		= layer_norm_eps
          snake_case__    :       Any       		= initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case__    :       Optional[Any]       		= int(embed_dim * 2 ** (len(snake_case_		) - 1)		)
          snake_case__    :       int       		= layer_scale_init_value
          snake_case__    :       Dict       		= ["""stem"""] + [f"stage{idx}" for idx in range(1					,	len(snake_case_		) + 1		)]
          snake_case__		,							snake_case__    :       Tuple       		= get_aligned_output_features_output_indices(
              out_features=snake_case_					,	out_indices=snake_case_					,	stage_names=self.stage_names		)
