
'''simple docstring'''



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)  # pylint: disable=invalid-name

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     ['safety_checker', 'feature_extractor']






       def __init__(							self				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__				,							A__ = True				,							)			->	List[Any]:
          super().__init__()
          if hasattr(scheduler.config				,							"""steps_offset"""					) and scheduler.config.steps_offset != 1:
             _SCREAMING_SNAKE_CASE          = (
                 F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                 F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                 """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
                 """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
                 """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
                 """ file"""
             )
             deprecate("""steps_offset!=1"""				,							"""1.0.0"""				,							A__				,							standard_warn=A__					)
             _SCREAMING_SNAKE_CASE          = dict(scheduler.config					)
             _SCREAMING_SNAKE_CASE          = 1
             _SCREAMING_SNAKE_CASE          = FrozenDict(A__					)

          self.register_modules(
              vae=A__				,							text_encoder=A__				,							tokenizer=A__				,							unet=A__				,							scheduler=A__				,							safety_checker=A__				,							feature_extractor=A__				,							)
          self.register_to_config(requires_safety_checker=A__					)






       @property
       def 				UpperCamelCase (							self					)			->	Dict[str, Any]:
          return {k: getattr(self				,							A__					) for k in self.config.keys() if not k.startswith("""_"""					)}






       def 				UpperCamelCase (							self				,							A__ = "auto"					)			->	Dict:
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             _SCREAMING_SNAKE_CASE          = self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(A__					)






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          self.enable_attention_slicing(A__					)






       @torch.no_grad()
       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__				,							A__ = 0.8				,							A__ = 50				,							A__ = 7.5				,							A__ = None				,							A__ = 1				,							A__ = 0.0				,							A__ = None				,							A__ = "pil"				,							A__ = True				,							A__ = None				,							A__ = 1				,							)			->	int:
          # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
          return StableDiffusionInpaintPipelineLegacy(**self.components					)(
              prompt=A__				,							image=A__				,							mask_image=A__				,							strength=A__				,							num_inference_steps=A__				,							guidance_scale=A__				,							negative_prompt=A__				,							num_images_per_prompt=A__				,							eta=A__				,							generator=A__				,							output_type=A__				,							return_dict=A__				,							callback=A__				,							)






       @torch.no_grad()
       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__ = 0.8				,							A__ = 50				,							A__ = 7.5				,							A__ = None				,							A__ = 1				,							A__ = 0.0				,							A__ = None				,							A__ = "pil"				,							A__ = True				,							A__ = None				,							A__ = 1				,							**A__				,							)			->	Union[str, Any]:
          # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
          return StableDiffusionImgaImgPipeline(**self.components					)(
              prompt=A__				,							image=A__				,							strength=A__				,							num_inference_steps=A__				,							guidance_scale=A__				,							negative_prompt=A__				,							num_images_per_prompt=A__				,							eta=A__				,							generator=A__				,							output_type=A__				,							return_dict=A__				,							callback=A__				,							callback_steps=A__				,							)






       @torch.no_grad()
       def 				UpperCamelCase (							self				,							A__				,							A__ = 5_12				,							A__ = 5_12				,							A__ = 50				,							A__ = 7.5				,							A__ = None				,							A__ = 1				,							A__ = 0.0				,							A__ = None				,							A__ = None				,							A__ = "pil"				,							A__ = True				,							A__ = None				,							A__ = 1				,							)			->	Optional[int]:
          # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
          return StableDiffusionPipeline(**self.components					)(
              prompt=A__				,							height=A__				,							width=A__				,							num_inference_steps=A__				,							guidance_scale=A__				,							negative_prompt=A__				,							num_images_per_prompt=A__				,							eta=A__				,							generator=A__				,							latents=A__				,							output_type=A__				,							return_dict=A__				,							callback=A__				,							callback_steps=A__				,							)











from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





