


'''simple docstring'''
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[str]:
    UpperCAmelCase__     :						List[Any]						=							('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

    UpperCAmelCase__     :						Dict						=							(
        ('''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__		)

    UpperCAmelCase__     :						int						=							model.state_dict()

    def to_tf_var_name(lowerCAmelCase__		):
        for patt, repl in iter(lowerCAmelCase__		):
            UpperCAmelCase__     :						str						=							name.replace(lowerCAmelCase__    ,   lowerCAmelCase__		)
        return F"""bert/{name}"""

    def create_tf_var(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		):
        UpperCAmelCase__     :						Union[str, Any]						=							tf.dtypes.as_dtype(tensor.dtype		)
        UpperCAmelCase__     :						Optional[int]						=							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:
            UpperCAmelCase__     :						Union[str, Any]						=							to_tf_var_name(lowerCAmelCase__		)
            UpperCAmelCase__     :						Union[str, Any]						=							state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose		):
                UpperCAmelCase__     :						List[Any]						=							torch_tensor.T
            UpperCAmelCase__     :						Optional[Any]						=							create_tf_var(tensor=lowerCAmelCase__    ,   name=lowerCAmelCase__    ,   session=lowerCAmelCase__		)
            tf.keras.backend.set_value(lowerCAmelCase__    ,   lowerCAmelCase__		)
            UpperCAmelCase__     :						Dict						=							session.run(lowerCAmelCase__		)
            print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase__    ,   lowerCAmelCase__		)}"""		)

        UpperCAmelCase__     :						Optional[int]						=							tf.train.Saver(tf.trainable_variables()		)
        saver.save(lowerCAmelCase__    ,   os.path.join(lowerCAmelCase__    ,   model_name.replace('''-'''    ,   '''_'''		) + '''.ckpt'''		)		)





def    a__			(   lowerCAmelCase__=None		)       ->      str:
    UpperCAmelCase__     :						List[Any]						=							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'''		)
    UpperCAmelCase__     :						str						=							parser.parse_args(lowerCAmelCase__		)

    UpperCAmelCase__     :						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=lowerCAmelCase__    ,   ckpt_dir=args.tf_cache_dir    ,   model_name=args.model_name		)


if __name__ == "__main__":
  main()


lowercase   =	8.314_4598



def   __lowerCAmelCase    (				UpperCAmelCase__					:   float      ,					UpperCAmelCase__					:   float							)	->     float:
							if temperature < 0:
														raise Exception("""Temperature cannot be less than 0 K"""							)
							if molar_mass <= 0:
														raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		lowercase   =	3_0_0
		lowercase   =	2_8
		lowercase   =	rms_speed_of_molecule(temperature, molar_mass)
		print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





