



'''simple docstring'''





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((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Any							      =					[trans(img.convert("""RGB"""			)			) for img in image]
    lowerCAmelCase__ :  str							      =					torch.stack(UpperCamelCase			)
    return image






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[Any]:
      super().__init__()

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

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      if strength < 0 or strength > 1:
          raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      # get the original timestep using init_timestep
      lowerCAmelCase__ :  str							      =					min(int(num_inference_steps * strength      )  ,__UpperCAmelCase      )

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

      return timesteps, num_inference_steps - t_start




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None      )    ->		List[str]:
      if not isinstance(__UpperCAmelCase  ,(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(__UpperCAmelCase      )}"""      )

      lowerCAmelCase__ :  Tuple							      =					image.to(device=__UpperCAmelCase  ,dtype=__UpperCAmelCase      )

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

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

      # get latents
      print("""add noise to latents at timestep"""  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					self.scheduler.add_noise(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					init_latents

      return latents






  @torch.no_grad()
  def __call__(    self  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0.8  ,__UpperCAmelCase = 1  ,__UpperCAmelCase = None  ,__UpperCAmelCase = 0.0  ,__UpperCAmelCase = 50  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "pil"  ,__UpperCAmelCase = True  ,)    ->		Union[ImagePipelineOutput, Tuple]:
      self.check_inputs(__UpperCAmelCase      )

      # 2. Preprocess image
      lowerCAmelCase__ :  List[str]							      =					preprocess(__UpperCAmelCase      )

      # 3. set timesteps
      self.scheduler.set_timesteps(__UpperCAmelCase  ,device=self.device      )
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[Any]							      =					self.get_timesteps(__UpperCAmelCase  ,__UpperCAmelCase  ,self.device      )
      lowerCAmelCase__ :  Optional[int]							      =					timesteps[:1].repeat(__UpperCAmelCase      )

      # 4. Prepare latent variables
      lowerCAmelCase__ :  Tuple							      =					self.prepare_latents(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,self.unet.dtype  ,self.device  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					latents

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

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

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

      return ImagePipelineOutput(images=__UpperCAmelCase      )










"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

    # Load weights from numpy
    load_tf_weights_in_gpta(__a    ,				__a    ,				__a							)

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    print(f'Save PyTorch model to {pytorch_weights_dump_path}'							)
    torch.save(model.state_dict()    ,				__a							)
    print(f'Save configuration file to {pytorch_config_dump_path}'							)
    with open(__a    ,				'''w'''    ,				encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--gpt2_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
          """This specifies the model architecture."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


