





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def       UpperCamelCase__(			UpperCamelCase__     :			Any    )->List[str]:
       for param in module.parameters():
              A__		   =		False


def       UpperCamelCase__(			)->Union[str, Any]:
       A__		   =		'''cuda''' if torch.cuda.is_available() else '''cpu'''
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              A__		   =		'''mps'''
       if device == "mps":
              print(
                  '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                  ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                  ''' with generations.'''    )
       return device


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]    )->Tuple:
       A__		   =		plt.imshow(UpperCamelCase__    )
       fig.axes.get_xaxis().set_visible(UpperCamelCase__    )
       fig.axes.get_yaxis().set_visible(UpperCamelCase__    )
       plt.show()


def       UpperCamelCase__(			)->List[str]:
       A__		   =		datetime.now()
       A__		   =		current_time.strftime('''%H:%M:%S'''    )
       return timestamp












import argparse

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


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

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

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


if __name__ == "__main__":
       a__: Optional[int]							   =						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.'
       )
       a__: Tuple							   =						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
       )





