



import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class     __lowerCAmelCase					(    A		):



			def __init__(		self     :						int				,	A     :						VQModel				,	A     :						UNetaDModel				,	A     :						DDIMScheduler)     ->					str:



						"""simple docstring"""






						super().__init__()
						self.register_modules(vqvae=A				,	unet=A				,	scheduler=A)



			@torch.no_grad()
			def __call__(		self     :						Optional[int]				,	A     :						int = 1				,	A     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				,	A     :						float = 0.0				,	A     :						int = 50				,	A     :						Optional[str] = "pil"				,	A     :						bool = True				,	**A     :						Union[str, Any]				,	)     ->					Union[Tuple, ImagePipelineOutput]:



						"""simple docstring"""






						_UpperCAmelCase	 =     randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,	generator=A				,	)
						_UpperCAmelCase	 =     latents.to(self.device)

						# scale the initial noise by the standard deviation required by the scheduler
						_UpperCAmelCase	 =     latents * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(A)

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						_UpperCAmelCase	 =     'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())

						_UpperCAmelCase	 =     {}
						if accepts_eta:
									_UpperCAmelCase	 =     eta

						for t in self.progress_bar(self.scheduler.timesteps):
									_UpperCAmelCase	 =     self.scheduler.scale_model_input(A				,	A)
									# predict the noise residual
									_UpperCAmelCase	 =     self.unet(A				,	A).sample
									# compute the previous noisy sample x_t -> x_t-1
									_UpperCAmelCase	 =     self.scheduler.step(A				,	A				,	A				,	**A).prev_sample

						# decode the image latents with the VAE
						_UpperCAmelCase	 =     self.vqvae.decode(A).sample

						_UpperCAmelCase	 =     (image / 2 + 0.5).clamp(0				,	1)
						_UpperCAmelCase	 =     image.cpu().permute(0				,	2				,	3				,	1).numpy()
						if output_type == "pil":
									_UpperCAmelCase	 =     self.numpy_to_pil(A)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=A)





import argparse

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


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     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__":
							UpperCAmelCase__     = 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."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
