






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      lowerCAmelCase__	(    _a       :			BertModel  ,      _a       :			str  ,      _a       :			str    ):
		snake_case_				:							int						 =       ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

		snake_case_				:							Union[str, Any]						 =       (
		    ("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(UpperCamelCase__    ):
				os.makedirs(UpperCamelCase__    )

		snake_case_				:							List[Any]						 =       model.state_dict()

		def to_tf_var_name(_a       :			str    ):
				for patt, repl in iter(UpperCamelCase__    ):
						snake_case_				:							str						 =       name.replace(UpperCamelCase__  ,      UpperCamelCase__    )
				return F'''bert/{name}'''

		def create_tf_var(_a       :			np.ndarray  ,      _a       :			str  ,      _a       :			tf.Session    ):
				snake_case_				:							Tuple						 =       tf.dtypes.as_dtype(tensor.dtype    )
				snake_case_				:							List[Any]						 =       tf.get_variable(dtype=UpperCamelCase__  ,      shape=tensor.shape  ,      name=UpperCamelCase__  ,      initializer=tf.zeros_initializer()    )
				session.run(tf.variables_initializer([tf_var]    )    )
				session.run(UpperCamelCase__    )
				return tf_var

		tf.reset_default_graph()
		with tf.Session() as session:
				for var_name in state_dict:
						snake_case_				:							Tuple						 =       to_tf_var_name(UpperCamelCase__    )
						snake_case_				:							List[Any]						 =       state_dict[var_name].numpy()
						if any(x in var_name for x in tensors_to_transpose    ):
								snake_case_				:							Dict						 =       torch_tensor.T
						snake_case_				:							int						 =       create_tf_var(tensor=UpperCamelCase__  ,      name=UpperCamelCase__  ,      session=UpperCamelCase__    )
						tf.keras.backend.set_value(UpperCamelCase__  ,      UpperCamelCase__    )
						snake_case_				:							str						 =       session.run(UpperCamelCase__    )
						print(F'''Successfully created {tf_name}: {np.allclose(UpperCamelCase__  ,      UpperCamelCase__    )}'''    )

				snake_case_				:							Optional[Any]						 =       tf.train.Saver(tf.trainable_variables()    )
				saver.save(UpperCamelCase__  ,      os.path.join(UpperCamelCase__  ,      model_name.replace("-"  ,      "_"    ) + ".ckpt"    )    )

def      lowerCAmelCase__	(    _a       :			Optional[int]=None    ):
		snake_case_				:							List[str]						 =       argparse.ArgumentParser()
		parser.add_argument("--model_name"  ,      type=UpperCamelCase__  ,      required=UpperCamelCase__  ,      help="model name e.g. bert-base-uncased"    )
		parser.add_argument(
		    "--cache_dir"  ,      type=UpperCamelCase__  ,      default=UpperCamelCase__  ,      required=UpperCamelCase__  ,      help="Directory containing pytorch model"    )
		parser.add_argument("--pytorch_model_path"  ,      type=UpperCamelCase__  ,      required=UpperCamelCase__  ,      help="/path/to/<pytorch-model-name>.bin"    )
		parser.add_argument("--tf_cache_dir"  ,      type=UpperCamelCase__  ,      required=UpperCamelCase__  ,      help="Directory in which to save tensorflow model"    )
		snake_case_				:							List[str]						 =       parser.parse_args(UpperCamelCase__    )

		snake_case_				:							Optional[int]						 =       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=UpperCamelCase__  ,      ckpt_dir=args.tf_cache_dir  ,      model_name=args.model_name    )


if __name__ == "__main__":
							main()








import math

def      lowerCAmelCase__	(    _a       :			int    ):

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5  ,      int(math.sqrt(_a    ) + 1    )  ,      6    ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True

def      lowerCAmelCase__	(    _a       :			float = 0.1    ):
		snake_case_				:							int						 =       3
		snake_case_				:							Optional[Any]						 =       3

		while primes / (2 * j - 1) >= ratio:
				for i in range(j * j + j + 1  ,      (j + 2) * (j + 2)  ,      j + 1    ):
						primes += is_prime(_a    )
				j += 2
		return j


if __name__ == "__main__":
							import doctest

							doctest.testmod()
