


'''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				(      __a			, __a			, __a			, __a       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :Tuple							 =			FunnelConfig.from_json_file(__a       )
					print(f'''Building PyTorch model from configuration: {config}'''       )
					UpperCamelCase__       :List[Any]							 =			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__":
		__snake_case											=					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.'''
		)
		__snake_case											=					parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
		)



'''simple docstring'''
from __future__ import annotations

from typing import Any


def       a				(      __a       )  ->     None:



					'''simple docstring'''






					create_state_space_tree(__a			, []			, 0       )


def       a				(      __a			, __a			, __a       )  ->     None:



					'''simple docstring'''






					if index == len(__a       ):
										print(__a       )
										return

					create_state_space_tree(__a			, __a			, index + 1       )
					current_subsequence.append(sequence[index]       )
					create_state_space_tree(__a			, __a			, index + 1       )
					current_subsequence.pop()


if __name__ == "__main__":
		__snake_case											=					[3, 1, 2, 4]
		generate_all_subsequences(seq)

		seq.clear()
		seq.extend(['''A''', '''B''', '''C'''])
		generate_all_subsequences(seq)