



'''simple docstring'''





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      # Initialise PyTorch model
      snake_case__  :    Optional[Any]    						=      FunnelConfig.from_json_file(_lowerCAmelCase		)
      print(f"Building PyTorch model from configuration: {config}"		)
      snake_case__  :    int    						=      FunnelBaseModel(_lowerCAmelCase		) if base_model else FunnelModel(_lowerCAmelCase		)

      # Load weights from tf checkpoint
      load_tf_weights_in_funnel(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"		)
      torch.save(model.state_dict()     ,   _lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		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 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."
       )
       parser.add_argument(
           "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
       )
       __a          =		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
