


'''simple docstring'''


import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							OmegaConf.load(SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							torch.load(SCREAMING_SNAKE_CASE       ,					map_location="cpu"							)["model"]
     __A	:       List[str]      =							list(state_dict.keys()							)

     # extract state_dict for VQVAE
     __A	:       Dict      =							{}
     __A	:       str      =							"first_stage_model."
     for key in keys:
          if key.startswith(SCREAMING_SNAKE_CASE							):
               __A	:       Optional[Any]      =							state_dict[key]

    # extract state_dict for UNetLDM
     __A	:       str      =							{}
     __A	:       str      =							"model.diffusion_model."
     for key in keys:
          if key.startswith(SCREAMING_SNAKE_CASE							):
               __A	:       str      =							state_dict[key]

     __A	:       Union[str, Any]      =							config.model.params.first_stage_config.params
     __A	:       str      =							config.model.params.unet_config.params

     __A	:       Optional[int]      =							VQModel(**SCREAMING_SNAKE_CASE							).eval()
     vqvae.load_state_dict(SCREAMING_SNAKE_CASE							)

     __A	:       str      =							UNetLDMModel(**SCREAMING_SNAKE_CASE							).eval()
     unet.load_state_dict(SCREAMING_SNAKE_CASE							)

     __A	:       Any      =							DDIMScheduler(
         timesteps=config.model.params.timesteps       ,					beta_schedule="scaled_linear"       ,					beta_start=config.model.params.linear_start       ,					beta_end=config.model.params.linear_end       ,					clip_sample=SCREAMING_SNAKE_CASE       ,					)

     __A	:       Optional[Any]      =							LDMPipeline(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     pipeline.save_pretrained(SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser()
      parser.add_argument("""--checkpoint_path""", type=str, required=True)
      parser.add_argument("""--config_path""", type=str, required=True)
      parser.add_argument("""--output_path""", type=str, required=True)
      _UpperCamelCase 							=  parser.parse_args()

      convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)





'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

     # Load weights from tf checkpoint
     load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					is_trivia_qa=SCREAMING_SNAKE_CASE							)

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


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(
          """--big_bird_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained BERT model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
      )
      _UpperCamelCase 							=  parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

