






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 	_UpperCamelCase       (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Union[str, Any]		)  ->   List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						lowerCAmelCase__    						=						(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
												os.makedirs(__SCREAMING_SNAKE_CASE		)

						lowerCAmelCase__    						=						model.state_dict()

						def to_tf_var_name(UpperCamelCase_       :					Optional[int]		):
												for patt, repl in iter(__SCREAMING_SNAKE_CASE		):
																		lowerCAmelCase__    						=						name.replace(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE		)
												return F"bert/{name}"

						def create_tf_var(UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Dict		):
												lowerCAmelCase__    						=						tf.dtypes.as_dtype(tensor.dtype		)
												lowerCAmelCase__    						=						tf.get_variable(dtype=__SCREAMING_SNAKE_CASE    ,       shape=tensor.shape    ,       name=__SCREAMING_SNAKE_CASE    ,       initializer=tf.zeros_initializer()		)
												session.run(tf.variables_initializer([tf_var]		)		)
												session.run(__SCREAMING_SNAKE_CASE		)
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		lowerCAmelCase__    						=						to_tf_var_name(__SCREAMING_SNAKE_CASE		)
																		lowerCAmelCase__    						=						state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose		):
																								lowerCAmelCase__    						=						torch_tensor.T
																		lowerCAmelCase__    						=						create_tf_var(tensor=__SCREAMING_SNAKE_CASE    ,       name=__SCREAMING_SNAKE_CASE    ,       session=__SCREAMING_SNAKE_CASE		)
																		tf.keras.backend.set_value(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE		)
																		lowerCAmelCase__    						=						session.run(__SCREAMING_SNAKE_CASE		)
																		print(F"Successfully created {tf_name}: {np.allclose(__SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE		)}"		)

												lowerCAmelCase__    						=						tf.train.Saver(tf.trainable_variables()		)
												saver.save(__SCREAMING_SNAKE_CASE    ,       os.path.join(__SCREAMING_SNAKE_CASE    ,       model_name.replace('-'    ,       '_'		) + '.ckpt'		)		)







def 	_UpperCamelCase       (    UpperCamelCase_       :					Optional[int]=None		)  ->   List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						argparse.ArgumentParser()
						parser.add_argument('--model_name'    ,       type=__SCREAMING_SNAKE_CASE    ,       required=__SCREAMING_SNAKE_CASE    ,       help='model name e.g. bert-base-uncased'		)
						parser.add_argument(
						    '--cache_dir'    ,       type=__SCREAMING_SNAKE_CASE    ,       default=__SCREAMING_SNAKE_CASE    ,       required=__SCREAMING_SNAKE_CASE    ,       help='Directory containing pytorch model'		)
						parser.add_argument('--pytorch_model_path'    ,       type=__SCREAMING_SNAKE_CASE    ,       required=__SCREAMING_SNAKE_CASE    ,       help='/path/to/<pytorch-model-name>.bin'		)
						parser.add_argument('--tf_cache_dir'    ,       type=__SCREAMING_SNAKE_CASE    ,       required=__SCREAMING_SNAKE_CASE    ,       help='Directory in which to save tensorflow model'		)
						lowerCAmelCase__    						=						parser.parse_args(__SCREAMING_SNAKE_CASE		)

						lowerCAmelCase__    						=						BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name    ,       state_dict=torch.load(args.pytorch_model_path		)    ,       cache_dir=args.cache_dir    ,       )

						convert_pytorch_checkpoint_to_tf(model=__SCREAMING_SNAKE_CASE    ,       ckpt_dir=args.tf_cache_dir    ,       model_name=args.model_name		)


if __name__ == "__main__":
					main()













from __future__ import annotations

from functools import lru_cache
from math import ceil

__snake_case    :    Union[str, Any]         =     1_00

__snake_case    :    Tuple         =     set(range(3, NUM_PRIMES, 2))
primes.add(2)
__snake_case    :    int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=100		)
def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   set[int]:

						"""simple docstring"""



						if number_to_partition < 0:
												return set()
						elif number_to_partition == 0:
												return {1}

						lowerCAmelCase__    						=						set()
						lowerCAmelCase__    						=						42
						lowerCAmelCase__    						=						42

						for prime in primes:
												if prime > number_to_partition:
																		continue
												for sub in partition(number_to_partition - prime		):
																		ret.add(sub * prime		)

						return ret







def 	_UpperCamelCase   (    UpperCamelCase_       :					int = 5000		)  ->   int | None:

						"""simple docstring"""



						for number_to_partition in range(1    ,       UpperCamelCase_		):
												if len(partition(UpperCamelCase_		)		) > number_unique_partitions:
																		return number_to_partition
						return None


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





