


"""simple docstring"""



import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]						,							lowercase  :  List[str]			)     ->						int:


    """simple docstring"""





    if openai_config_file == "":
        snake_case   :				Optional[Any]				 =   OpenAIGPTConfig()
    else:
        snake_case   :				Any				 =   OpenAIGPTConfig.from_json_file(a__			)
    snake_case   :				List[str]				 =   OpenAIGPTModel(a__			)

    # Load weights from numpy
    load_tf_weights_in_openai_gpt(a__						,							a__						,							a__			)

    # Save pytorch-model
    snake_case   :				List[str]				 =   pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    snake_case   :				Dict				 =   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__":
  __snake_case             =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--openai_checkpoint_folder_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(
      """--openai_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
          """This specifies the model architecture."""
      ),
  )
  __snake_case             =   parser.parse_args()
  convert_openai_checkpoint_to_pytorch(
      args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
  )









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase    (				unittest.TestCase     ):







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   UNetaDModel(
           block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("DownBlock2D", "AttnDownBlock2D")						,     up_block_types=("AttnUpBlock2D", "UpBlock2D")						,     )
       return model







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   self.dummy_uncond_unet
       snake_case   :				Tuple				 =   KarrasVeScheduler()

       snake_case   :				int				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   torch.manual_seed(0						)
       snake_case   :				List[Any]				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Dict				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						,     return_dict=UpperCamelCase__						)[0]

       snake_case   :				Tuple				 =   image[0, -3:, -3:, -1]
       snake_case   :				Tuple				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2







@slow
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   "google/ncsnpp-celebahq-256"
       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   KarrasVeScheduler()

       snake_case   :				Optional[int]				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Union[str, Any]				 =   pipe(num_inference_steps=20						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				int				 =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       snake_case   :				Any				 =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





