"""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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Optional[Any]:
				# Construct model
				if gpta_config_file == "":
								SCREAMING_SNAKE_CASE__		  =				GPTaConfig()
				else:
								SCREAMING_SNAKE_CASE__		  =				GPTaConfig.from_json_file(lowerCAmelCase__       )
				SCREAMING_SNAKE_CASE__		  =				GPTaModel(lowerCAmelCase__       )

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

				# Save pytorch-model
				SCREAMING_SNAKE_CASE__		  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
				SCREAMING_SNAKE_CASE__		  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
				print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
				torch.save(model.state_dict()  ,					lowerCAmelCase__       )
				print(F"""Save configuration file to {pytorch_config_dump_path}"""       )
				with open(lowerCAmelCase__  ,					"w"  ,					encoding="utf-8"       ) as f:
								f.write(config.to_json_string()       )


if __name__ == "__main__":
							_A   					=    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.'
							    ),
							)
							_A   					=    parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

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

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



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

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).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
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

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

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




