


'''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 			_a			(		_lowercase      :			Any    ,      _lowercase      :			Any    ,      _lowercase      :			Tuple    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    FunnelConfig.from_json_file(_lowercase						)
     print(F'Building PyTorch model from configuration: {config}'						)
     __UpperCAmelCase			:				Any													=    FunnelBaseModel(_lowercase						) if base_model else FunnelModel(_lowercase						)

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

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'						)
     torch.save(model.state_dict()    ,      _lowercase						)


if __name__ == "__main__":
      __UpperCAmelCase    :List[str]				       =     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."
      )
      __UpperCAmelCase    :Any				       =     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
      )



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    len(_lowercase						)
     while cur > 1:
          # Find the maximum number in arr
          __UpperCAmelCase			:				Union[str, Any]													=    arr.index(max(arr[0:cur]						)						)
          # Reverse from 0 to mi
          __UpperCAmelCase			:				Any													=    arr[mi::-1] + arr[mi + 1 : len(_lowercase						)]
          # Reverse whole list
          __UpperCAmelCase			:				List[str]													=    arr[cur - 1 :: -1] + arr[cur : len(_lowercase						)]
          cur -= 1
     return arr


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     input("Enter numbers separated by a comma:\n").strip()
      __UpperCAmelCase    :Optional[int]				       =     [int(item) for item in user_input.split(",")]
      print(pancake_sort(unsorted))