



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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       UNetaDModel							,   __lowerCAmelCase			:       ScoreSdeVeScheduler							)       ->				str:


    """simple docstring"""






    super().__init__()
    self.register_modules(unet=__lowerCAmelCase							,   scheduler=__lowerCAmelCase							)




   @torch.no_grad()
   def __call__(      self			:       int							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   __lowerCAmelCase			:       Optional[str] = "pil"							,   __lowerCAmelCase			:       bool = True							,   **__lowerCAmelCase			:       Any							,   )       ->				Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""






    a						=		self.unet.config.sample_size
    a						=		(batch_size, 3, img_size, img_size)

    a						=		self.unet

    a						=		randn_tensor(__lowerCAmelCase							,   generator=__lowerCAmelCase							) * self.scheduler.init_noise_sigma
    a						=		sample.to(self.device							)

    self.scheduler.set_timesteps(__lowerCAmelCase							)
    self.scheduler.set_sigmas(__lowerCAmelCase							)

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

     # correction step
     for _ in range(self.scheduler.config.correct_steps							):
      a						=		self.unet(__lowerCAmelCase							,   __lowerCAmelCase							).sample
      a						=		self.scheduler.step_correct(__lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							).prev_sample

     # prediction step
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							).sample
     a						=		self.scheduler.step_pred(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)

     a		,  a						=		output.prev_sample, output.prev_sample_mean

    a						=		sample_mean.clamp(0							,   1							)
    a						=		sample.cpu().permute(0							,   2							,   3							,   1							).numpy()
    if output_type == "pil":
     a						=		self.numpy_to_pil(__lowerCAmelCase							)

    if not return_dict:
     return (sample,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

