

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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# Initialise PyTorch model
						snake_case_   =	FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
						print(F'''Building PyTorch model from configuration: {config}'''       )
						snake_case_   =	FunnelBaseModel(SCREAMING_SNAKE_CASE__       ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE__       )

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

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


if __name__ == "__main__":
					lowerCAmelCase_       	=	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_       	=	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
					)


from __future__ import annotations

from collections.abc import Generator


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	{}
						snake_case_   =	2
						while True:
												snake_case_   =	factor_map.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
												if factor:
																		snake_case_   =	factor + prime
																		while x in factor_map:
																								x += factor
																		snake_case_   =	factor
												else:
																		snake_case_   =	prime
																		yield prime
												prime += 1


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 1E10       ):
						snake_case_   =	sieve()

						snake_case_   =	1
						while True:
												snake_case_   =	next(SCREAMING_SNAKE_CASE__       )
												if (2 * prime * n) > limit:
																		return n
												# Ignore the next prime as the reminder will be 2.
												next(SCREAMING_SNAKE_CASE__       )
												n += 2


if __name__ == "__main__":
					print(solution())