




"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

 UpperCAmelCase__					:  List[str]					   =					(
     ("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      )

 UpperCAmelCase__					:  Tuple					   =					model.state_dict()

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

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

  UpperCAmelCase__					:  List[Any]					   =					tf.train.Saver(tf.trainable_variables()      )
  saver.save(__snake_case							,    os.path.join(__snake_case							,    model_name.replace("""-"""							,    """_"""      ) + """.ckpt"""      )      )
def   lowerCAmelCase						(					__UpperCamelCase=None      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					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"""      )
 UpperCAmelCase__					:  Optional[int]					   =					parser.parse_args(__snake_case      )

 UpperCAmelCase__					:  Any					   =					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"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase =				{
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TableTransformerForObjectDetection',
          'TableTransformerModel',
          'TableTransformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          TableTransformerConfig,
          TableTransformerOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_table_transformer import (
                TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TableTransformerForObjectDetection,
                TableTransformerModel,
                TableTransformerPreTrainedModel,
            )

else:
      import sys

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





