


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Optional[int]    				=				('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				lowerCAmelCase_ :      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(lowerCAmelCase_ ):
								os.makedirs(lowerCAmelCase_ )

				lowerCAmelCase_ :      str    				=				model.state_dict()

				def to_tf_var_name(lowerCAmelCase_ ):
								for patt, repl in iter(lowerCAmelCase_ ):
												lowerCAmelCase_ :      Dict    				=				name.replace(lowerCAmelCase_ ,   lowerCAmelCase_ )
								return f"""bert/{name}"""

				def create_tf_var(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ):
								lowerCAmelCase_ :      Any    				=				tf.dtypes.as_dtype(tensor.dtype )
								lowerCAmelCase_ :      List[str]    				=				tf.get_variable(dtype=lowerCAmelCase_ ,   shape=tensor.shape ,   name=lowerCAmelCase_ ,   initializer=tf.zeros_initializer() )
								session.run(tf.variables_initializer([tf_var] ) )
								session.run(lowerCAmelCase_ )
								return tf_var

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

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



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

				lowerCAmelCase_ :      str    				=				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=lowerCAmelCase_ ,   ckpt_dir=args.tf_cache_dir ,   model_name=args.model_name )


if __name__ == "__main__":
						main()



from __future__ import annotations

from math import pi



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict[str, float]:

				if (inductance, frequency, reactance).count(0 ) != 1:
								raise ValueError('''One and only one argument must be 0''' )
				if inductance < 0:
								raise ValueError('''Inductance cannot be negative''' )
				if frequency < 0:
								raise ValueError('''Frequency cannot be negative''' )
				if reactance < 0:
								raise ValueError('''Inductive reactance cannot be negative''' )
				if inductance == 0:
								return {"inductance": reactance / (2 * pi * frequency)}
				elif frequency == 0:
								return {"frequency": reactance / (2 * pi * inductance)}
				elif reactance == 0:
								return {"reactance": 2 * pi * frequency * inductance}
				else:
								raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
						import doctest

						doctest.testmod()