




'''simple docstring'''




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def      a_							(      __snake_case							:			BertModel   ,       __snake_case							:			str   ,       __snake_case							:			str   )							->						str:



       """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(__snake_case   ):
              os.makedirs(__snake_case   )

       lowerCamelCase_							=model.state_dict()

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

       def create_tf_var(__snake_case							:			np.ndarray   ,       __snake_case							:			str   ,       __snake_case							:			tf.Session   ):
              lowerCamelCase_							=tf.dtypes.as_dtype(tensor.dtype   )
              lowerCamelCase_							=tf.get_variable(dtype=__snake_case   ,       shape=tensor.shape   ,       name=__snake_case   ,       initializer=tf.zeros_initializer()   )
              session.run(tf.variables_initializer([tf_var]   )   )
              session.run(__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(__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=__snake_case   ,       name=__snake_case   ,       session=__snake_case   )
                     tf.keras.backend.set_value(__snake_case   ,       __snake_case   )
                     lowerCamelCase_							=session.run(__snake_case   )
                     print(F'''Successfully created {tf_name}: {np.allclose(__snake_case   ,       __snake_case   )}'''   )

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





def      a_							(      __snake_case							:			Union[str, Any]=None   )							->						Any:



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


if __name__ == "__main__":
     main()






import random

from .binary_exp_mod import bin_exp_mod



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=1000		):
					'''simple docstring'''
					if n < 2:
										return False

					if n % 2 == 0:
										return n == 2

					# this means n is odd
					lowercase							=						n - 1
					lowercase							=						0
					while d % 2 == 0:
										d /= 2
										exp += 1

					# n - 1=d*(2**exp)
					lowercase							=						0
					while count < prec:
										lowercase							=						random.randint(2 ,			n - 1		)
										lowercase							=						bin_exp_mod(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
										if b != 1:
															lowercase							=						True
															for _ in range(lowerCAmelCase__		):
																				if b == n - 1:
																									lowercase							=						False
																									break
																				lowercase							=						b * b
																				b %= n
															if flag:
																				return False
															count += 1
					return True


if __name__ == "__main__":
							lowercase__		:Tuple   =    abs(int(input("Enter bound : ").strip()))
							print("Here's the list of primes:")
							print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))




