

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


__UpperCAmelCase =				transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 if isinstance(__A							,    torch.Tensor      ):
  return image
 elif isinstance(__A							,    PIL.Image.Image      ):
  UpperCAmelCase__					:  Union[str, Any]					   =					[image]

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







class      __lowercase			(    _snake_case					):

  def __init__(  self		:			Optional[Any]					,A		:			Optional[Any]					,A		:			Any    ):






   '''simple docstring'''
   super().__init__()

   # make sure scheduler can always be converted to DDIM
   UpperCAmelCase__					:  Union[str, Any]					   =					DDIMScheduler.from_config(scheduler.config    )

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

  def    __lowercase       (  self		:			Tuple					,A		:			str    ):






   '''simple docstring'''
   if strength < 0 or strength > 1:
    raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"    )

  def    __lowercase       (  self		:			Tuple					,A		:			str					,A		:			int					,A		:			Optional[Any]    ):






   '''simple docstring'''
   # get the original timestep using init_timestep
   UpperCAmelCase__					:  List[str]					   =					min(int(num_inference_steps * strength    )					,lowerCAmelCase__    )

   UpperCAmelCase__					:  Optional[Any]					   =					max(num_inference_steps - init_timestep					,0    )
   UpperCAmelCase__					:  Optional[int]					   =					self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start

  def    __lowercase       (  self		:			List[Any]					,A		:			int					,A		:			str					,A		:			Optional[int]					,A		:			Any					,A		:			Any					,A		:			Any=None    ):






   '''simple docstring'''
   if not isinstance(lowerCAmelCase__					,(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(lowerCAmelCase__    )}"    )

   UpperCAmelCase__					:  str					   =					image.to(device=lowerCAmelCase__					,dtype=lowerCAmelCase__    )

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

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

   # get latents
   print("""add noise to latents at timestep"""					,lowerCAmelCase__    )
   UpperCAmelCase__					:  List[Any]					   =					self.scheduler.add_noise(lowerCAmelCase__					,lowerCAmelCase__					,lowerCAmelCase__    )
   UpperCAmelCase__					:  Optional[int]					   =					init_latents

   return latents




  @torch.no_grad()
  def __call__(  self		:			Tuple					,A		:			Union[torch.FloatTensor, PIL.Image.Image] = None					,A		:			float = 0.8					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			float = 0.0					,A		:			int = 50					,A		:			Optional[bool] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''
   self.check_inputs(lowerCAmelCase__    )

   # 2. Preprocess image
   UpperCAmelCase__					:  str					   =					preprocess(lowerCAmelCase__    )

   # 3. set timesteps
   self.scheduler.set_timesteps(lowerCAmelCase__					,device=self.device    )
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self.get_timesteps(lowerCAmelCase__					,lowerCAmelCase__					,self.device    )
   UpperCAmelCase__					:  Dict					   =					timesteps[:1].repeat(lowerCAmelCase__    )

   # 4. Prepare latent variables
   UpperCAmelCase__					:  Union[str, Any]					   =					self.prepare_latents(lowerCAmelCase__					,lowerCAmelCase__					,lowerCAmelCase__					,self.unet.dtype					,self.device					,lowerCAmelCase__    )
   UpperCAmelCase__					:  str					   =					latents

   # 5. Denoising loop
   for t in self.progress_bar(lowerCAmelCase__    ):
    # 1. predict noise model_output
    UpperCAmelCase__					:  Union[str, Any]					   =					self.unet(lowerCAmelCase__					,lowerCAmelCase__    ).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__					:  Dict					   =					self.scheduler.step(
        lowerCAmelCase__					,lowerCAmelCase__					,lowerCAmelCase__					,eta=lowerCAmelCase__					,use_clipped_model_output=lowerCAmelCase__					,generator=lowerCAmelCase__					,).prev_sample

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

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

   return ImagePipelineOutput(images=lowerCAmelCase__    )








"""simple docstring"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 if "model" in orig_key:
  UpperCAmelCase__					:  List[str]					   =					orig_key.replace("""model."""							,    """"""      )
 if "norm1" in orig_key:
  UpperCAmelCase__					:  List[Any]					   =					orig_key.replace("""norm1"""							,    """attention.output.LayerNorm"""      )
 if "norm2" in orig_key:
  UpperCAmelCase__					:  Optional[Any]					   =					orig_key.replace("""norm2"""							,    """output.LayerNorm"""      )
 if "norm" in orig_key:
  UpperCAmelCase__					:  int					   =					orig_key.replace("""norm"""							,    """LayerNorm"""      )
 if "transformer" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.split("""."""      )[0].split("""_"""      )[-1]
  UpperCAmelCase__					:  Dict					   =					orig_key.replace(F"transformer_{layer_num}"							,    F"encoder.layer.{layer_num}"      )
 if "mha.attn" in orig_key:
  UpperCAmelCase__					:  Tuple					   =					orig_key.replace("""mha.attn"""							,    """attention.self"""      )
 if "mha" in orig_key:
  UpperCAmelCase__					:  Dict					   =					orig_key.replace("""mha"""							,    """attention"""      )
 if "W_q" in orig_key:
  UpperCAmelCase__					:  str					   =					orig_key.replace("""W_q"""							,    """self.query"""      )
 if "W_k" in orig_key:
  UpperCAmelCase__					:  List[Any]					   =					orig_key.replace("""W_k"""							,    """self.key"""      )
 if "W_v" in orig_key:
  UpperCAmelCase__					:  str					   =					orig_key.replace("""W_v"""							,    """self.value"""      )
 if "ff1" in orig_key:
  UpperCAmelCase__					:  Optional[int]					   =					orig_key.replace("""ff1"""							,    """intermediate.dense"""      )
 if "ff2" in orig_key:
  UpperCAmelCase__					:  Dict					   =					orig_key.replace("""ff2"""							,    """output.dense"""      )
 if "ff" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.replace("""ff"""							,    """output.dense"""      )
 if "mlm_class" in orig_key:
  UpperCAmelCase__					:  Any					   =					orig_key.replace("""mlm.mlm_class"""							,    """cls.predictions.decoder"""      )
 if "mlm" in orig_key:
  UpperCAmelCase__					:  Optional[Any]					   =					orig_key.replace("""mlm"""							,    """cls.predictions.transform"""      )
 if "cls" not in orig_key:
  UpperCAmelCase__					:  Dict					   =					"""yoso.""" + orig_key

 return orig_key
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__					:  Optional[Any]					   =					orig_state_dict.pop(__UpperCamelCase      )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   UpperCAmelCase__					:  Dict					   =					val

 UpperCAmelCase__					:  Dict					   =					orig_state_dict["""cls.predictions.decoder.bias"""]
 UpperCAmelCase__					:  Optional[Any]					   =					torch.arange(__UpperCamelCase      ).expand((1, -1)      ) + 2

 return orig_state_dict
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					torch.load(__UpperCamelCase							,    map_location="""cpu"""      )["""model_state_dict"""]
 UpperCAmelCase__					:  List[str]					   =					YosoConfig.from_json_file(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					YosoForMaskedLM(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					convert_checkpoint_helper(config.max_position_embeddings							,    __UpperCamelCase      )

 print(model.load_state_dict(__UpperCamelCase      )      )
 model.eval()
 model.save_pretrained(__UpperCamelCase      )

 print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The json file for YOSO model config.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





