




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def 						lowerCAmelCase		(		_lowerCAmelCase							:   BertModel						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

 UpperCAmelCase__							 =						(
     ("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__							 =						model.state_dict()

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

 def create_tf_var(_lowerCAmelCase							:   np.ndarray						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   tf.Session	):
  UpperCAmelCase__							 =						tf.dtypes.as_dtype(tensor.dtype	)
  UpperCAmelCase__							 =						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__							 =						to_tf_var_name(_lowerCAmelCase	)
   UpperCAmelCase__							 =						state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose	):
    UpperCAmelCase__							 =						torch_tensor.T
   UpperCAmelCase__							 =						create_tf_var(tensor=_lowerCAmelCase						,							name=_lowerCAmelCase						,							session=_lowerCAmelCase	)
   tf.keras.backend.set_value(_lowerCAmelCase						,							_lowerCAmelCase	)
   UpperCAmelCase__							 =						session.run(_lowerCAmelCase	)
   print(F'''Successfully created {tf_name}: {np.allclose(_lowerCAmelCase						,							_lowerCAmelCase	)}'''	)

  UpperCAmelCase__							 =						tf.train.Saver(tf.trainable_variables()	)
  saver.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							model_name.replace("-"						,							"_"	) + ".ckpt"	)	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any=None	):




 """simple docstring"""







 UpperCAmelCase__							 =						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__							 =						parser.parse_args(_lowerCAmelCase	)

 UpperCAmelCase__							 =						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





