




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 			__magic_name__				( A		:					List[Any],  A		:					Tuple,  A		:					int,  A		:					List[str]   ):






			'''simple docstring'''







			a    =			FunnelConfig.from_json_file(A   )
			print(F"""Building PyTorch model from configuration: {config}"""   )
			a    =			FunnelBaseModel(A   ) if base_model else FunnelModel(A   )

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

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


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












from bisect import bisect
from itertools import accumulate




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[str],  A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			sorted(zip(A,  A   ),  key=lambda A   : x[0] / x[1],  reverse=A   )
			a    ,		a    =			[i[0] for i in r], [i[1] for i in r]
			a    =			list(accumulate(A   )   )
			a    =			bisect(A,  A   )
			return (
			    0
			    if k == 0
			    else sum(vl[:k]   ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
			    if k != n
			    else sum(vl[:k]   )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






