


"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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




class 	lowercase__				(					A_				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Tuple:
      super().__init__()

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

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





   @torch.no_grad()
   def __call__(							self      ,   SCREAMING_SNAKE_CASE = 1      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 0.0      ,   SCREAMING_SNAKE_CASE = 50      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = "pil"      ,   SCREAMING_SNAKE_CASE = True      ,   )  ->  Union[ImagePipelineOutput, Tuple]:

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size      ,   SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					int   =		(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
      else:
         _lowerCamelCase	:					List[str]   =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

      _lowerCamelCase	:					int   =		randn_tensor(SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE      ,   device=self.device      ,   dtype=self.unet.dtype)

      # set step values
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE)

      for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _lowerCamelCase	:					int   =		self.unet(SCREAMING_SNAKE_CASE      ,   SCREAMING_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
         _lowerCamelCase	:					Optional[int]   =		self.scheduler.step(
             SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   eta=SCREAMING_SNAKE_CASE      ,   use_clipped_model_output=SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

      _lowerCamelCase	:					List[str]   =		(image / 2 + 0.5).clamp(0      ,   1)
      _lowerCamelCase	:					List[str]   =		image.cpu().permute(0      ,   2      ,   3      ,   1).numpy()
      if output_type == "pil":
         _lowerCamelCase	:					Tuple   =		self.numpy_to_pil(SCREAMING_SNAKE_CASE)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE)




"""simple docstring"""


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

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   field(default='''summarization'''     ,   metadata={'''include_in_asdict_even_if_is_default''': True}    )
    _lowerCamelCase					=   Features({'''text''': Value('''string'''    )}    )
    _lowerCamelCase					=   Features({'''summary''': Value('''string'''    )}    )
    _lowerCamelCase					=   "text"
    _lowerCamelCase					=   "summary"




    @property
    def UpperCamelCase__		( self    )    ->      Dict[str, str]:
          return {self.text_column: "text", self.summary_column: "summary"}




