
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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				# Initialise PyTorch model
				lowerCamelCase_						=			FunnelConfig.from_json_file(lowerCamelCase__    )
				print(F'Building PyTorch model from configuration: {config}'    )
				lowerCamelCase_						=			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'''






def 				UpperCamelCase_			(    A__					:   int = 10_00					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   3
  lowerCAmelCase_			:      Dict			=   0
  while a < n:
    if a % 3 == 0 or a % 5 == 0:
      result += a
    elif a % 15 == 0:
      result -= a
    a += 1
  return result


if __name__ == "__main__":
  print(F'''{solution() = }''')



