

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	a (     A__      :  BertModel ,  A__      :  str ,  A__      :  str	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

      _lowercase      			=(
          ('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(A__	):
            os.makedirs(A__	)

      _lowercase      			=model.state_dict()

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

      def create_tf_var(A__      :  np.ndarray ,  A__      :  str ,  A__      :  tf.Session	):
            _lowercase      			=tf.dtypes.as_dtype(tensor.dtype	)
            _lowercase      			=tf.get_variable(dtype=A__ ,  shape=tensor.shape ,  name=A__ ,  initializer=tf.zeros_initializer()	)
            session.run(tf.variables_initializer([tf_var]	)	)
            session.run(A__	)
            return tf_var

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  _lowercase      			=to_tf_var_name(A__	)
                  _lowercase      			=state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose	):
                        _lowercase      			=torch_tensor.T
                  _lowercase      			=create_tf_var(tensor=A__ ,  name=A__ ,  session=A__	)
                  tf.keras.backend.set_value(A__ ,  A__	)
                  _lowercase      			=session.run(A__	)
                  print(F'''Successfully created {tf_name}: {np.allclose(A__ ,  A__	)}'''	)

            _lowercase      			=tf.train.Saver(tf.trainable_variables()	)
            saver.save(A__ ,  os.path.join(A__ ,  model_name.replace('-' ,  '_'	) + '.ckpt'	)	)
def 	a (     A__      :  List[str]=None	)       -> Optional[int]:





      """simple docstring"""



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

      _lowercase      			=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=A__ ,  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_torch_available


lowercase_   							=  {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TimesformerModel',
      'TimesformerForVideoClassification',
      'TimesformerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  lowercase_   							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


