






"""simple docstring"""
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    str						     = ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

	A_		:    Tuple						     = (
	    ("""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__       )

	A_		:    List[str]						     = model.state_dict()

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

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

		A_		:    str						     = tf.train.Saver(tf.trainable_variables()       )
		saver.save(snake_case__ , os.path.join(snake_case__ , model_name.replace("""-""" , """_"""       ) + """.ckpt"""       )       )






def       __UpperCamelCase						(		snake_case__=None       ):
	A_		:    Dict						     = 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"""       )
	A_		:    Dict						     = parser.parse_args(snake_case__       )

	A_		:    List[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=snake_case__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name       )


if __name__ == "__main__":
							main()









"""simple docstring"""
_lowerCAmelCase       =				[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages

