


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     UNetaDModel
      snake_case__				:     ScoreSdeVeScheduler
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       UNetaDModel     ,							SCREAMING_SNAKE_CASE__				:       ScoreSdeVeScheduler     )		->			Optional[int]:
             super().__init__()
             self.register_modules(unet=SCREAMING_SNAKE_CASE__     ,							scheduler=SCREAMING_SNAKE_CASE__     )

      @torch.no_grad()
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_0_0     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[str] = "pil"     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							**SCREAMING_SNAKE_CASE__				:       str     ,							)		->			Union[ImagePipelineOutput, Tuple]:
             a_					:		Tuple							=  self.unet.config.sample_size
             a_					:		Optional[Any]							=  (batch_size, 3, img_size, img_size)

             a_					:		Optional[Any]							=  self.unet

             a_					:		List[str]							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ) * self.scheduler.init_noise_sigma
             a_					:		Tuple							=  sample.to(self.device     )

             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )
             self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE__     )

             for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
                    a_					:		Dict							=  self.scheduler.sigmas[i] * torch.ones(shape[0]     ,							device=self.device     )

                    # correction step
                    for _ in range(self.scheduler.config.correct_steps     ):
                           a_					:		Tuple							=  self.unet(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ).sample
                           a_					:		Optional[Any]							=  self.scheduler.step_correct(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ).prev_sample

                    # prediction step
                    a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ).sample
                    a_					:		Tuple							=  self.scheduler.step_pred(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     )

                    a_							,		a_					:		Union[str, Any]							=  output.prev_sample, output.prev_sample_mean

             a_					:		Dict							=  sample_mean.clamp(0     ,							1     )
             a_					:		Optional[Any]							=  sample.cpu().permute(0     ,							2     ,							3     ,							1     ).numpy()
             if output_type == "pil":
                    a_					:		List[Any]							=  self.numpy_to_pil(SCREAMING_SNAKE_CASE__     )

             if not return_dict:
                    return (sample,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__     )






'''simple docstring'''







from typing import TYPE_CHECKING

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


A__:    List[str]					    =       {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]					    =       ['''ChineseCLIPFeatureExtractor''']
   A__:    Any					    =       ['''ChineseCLIPImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict					    =       [
       '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ChineseCLIPModel''',
       '''ChineseCLIPPreTrainedModel''',
       '''ChineseCLIPTextModel''',
       '''ChineseCLIPVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_chinese_clip import (
       CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ChineseCLIPConfig,
       ChineseCLIPOnnxConfig,
       ChineseCLIPTextConfig,
       ChineseCLIPVisionConfig,
   )
   from .processing_chinese_clip import ChineseCLIPProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_chinese_clip import (
          CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ChineseCLIPModel,
          ChineseCLIPPreTrainedModel,
          ChineseCLIPTextModel,
          ChineseCLIPVisionModel,
      )

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


