




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      			=						transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]				)  ->							str:
    if isinstance(_lowerCAmelCase	,					torch.Tensor				):
        return image
    elif isinstance(_lowerCAmelCase	,					PIL.Image.Image				):
        UpperCAmelCase_										=					[image]

    UpperCAmelCase_										=					[trans(img.convert('''RGB'''				)				) for img in image]
    UpperCAmelCase_										=					torch.stack(_lowerCAmelCase				)
    return image



class       a					(       lowerCamelCase__      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Optional[Any]    ):
         super().__init__()

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

         self.register_modules(unet=__snake_case ,   scheduler=__snake_case    )
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Tuple    ):




         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       :      Union[str, Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Tuple    ):
         UpperCAmelCase_										=					min(int(num_inference_steps * strength    ) ,   __snake_case    )

         UpperCAmelCase_										=					max(num_inference_steps - init_timestep ,   0    )
         UpperCAmelCase_										=					self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      List[Any] ,   __snake_case       :      Any ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      List[str]=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    )}'    )

         UpperCAmelCase_										=					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_										=					init_latents.shape
         UpperCAmelCase_										=					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_										=					self.scheduler.add_noise(__snake_case ,   __snake_case ,   __snake_case    )
         UpperCAmelCase_										=					init_latents

         return latents




     @torch.no_grad()
     def __call__(     self       :      int ,   __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
         UpperCAmelCase_										=					preprocess(__snake_case    )

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

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

         # 5. Denoising loop
         for t in self.progress_bar(__snake_case    ):
             # 1. predict noise model_output
             UpperCAmelCase_										=					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_										=					self.scheduler.step(
                 __snake_case ,   __snake_case ,   __snake_case ,   eta=__snake_case ,   use_clipped_model_output=__snake_case ,   generator=__snake_case ,   ).prev_sample

         UpperCAmelCase_										=					(image / 2 + 0.5).clamp(0 ,   1    )
         UpperCAmelCase_										=					image.cpu().permute(0 ,   2 ,   3 ,   1    ).numpy()
         if output_type == "pil":
             UpperCAmelCase_										=					self.numpy_to_pil(__snake_case    )

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

         return ImagePipelineOutput(images=__snake_case    )






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=_A      )
class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  field(default='audio-classification'     ,       metadata={'include_in_asdict_even_if_is_default': True}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'audio': Audio()}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'labels': ClassLabel}      )
     lowerCAmelCase    :	str       		=  "audio"
     lowerCAmelCase    :	str       		=  "labels"
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      List[Any]    ):
         if self.label_column not in features:
             raise ValueError(F'Column {self.label_column} is not present in features.'    )
         if not isinstance(features[self.label_column] ,   __snake_case    ):
             raise ValueError(F'Column {self.label_column} is not a ClassLabel.'    )
         UpperCAmelCase_										=					copy.deepcopy(self    )
         UpperCAmelCase_										=					self.label_schema.copy()
         UpperCAmelCase_										=					features[self.label_column]
         UpperCAmelCase_										=					label_schema
         return task_template




     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





