


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						)     ->			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(__snake_case						):
  os.makedirs(__snake_case						)

 _lowercase							      =model.state_dict()

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

 def create_tf_var(__snake_case     ,						__snake_case     ,						__snake_case						):
  _lowercase							      =tf.dtypes.as_dtype(tensor.dtype						)
  _lowercase							      =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:
   _lowercase							      =to_tf_var_name(__snake_case						)
   _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=__snake_case     ,						name=__snake_case     ,						session=__snake_case						)
   tf.keras.backend.set_value(__snake_case     ,						__snake_case						)
   _lowercase							      =session.run(__snake_case						)
   print(F"Successfully created {tf_name}: {np.allclose(__snake_case     ,						__snake_case						)}"						)

  _lowercase							      =tf.train.Saver(tf.trainable_variables()						)
  saver.save(__snake_case     ,						os.path.join(__snake_case     ,						model_name.replace('''-'''     ,						'''_'''						) + '''.ckpt'''						)						)
def 				UpperCAmelCase_					(							__snake_case=None						)     ->			int:







 """simple docstring"""


 _lowercase							      =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'''						)
 _lowercase							      =parser.parse_args(__snake_case						)

 _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=__snake_case     ,						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


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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''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

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

