





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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(lowercase__  ):
      os.makedirs(lowercase__  )

   UpperCAmelCase_											=model.state_dict()

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

   def create_tf_var(lowercase__  ,    lowercase__  ,    lowercase__  ):
      UpperCAmelCase_											=tf.dtypes.as_dtype(tensor.dtype  )
      UpperCAmelCase_											=tf.get_variable(dtype=lowercase__  ,    shape=tensor.shape  ,    name=lowercase__  ,    initializer=tf.zeros_initializer()  )
      session.run(tf.variables_initializer([tf_var]  )  )
      session.run(lowercase__  )
      return tf_var

   tf.reset_default_graph()
   with tf.Session() as session:
      for var_name in state_dict:
         UpperCAmelCase_											=to_tf_var_name(lowercase__  )
         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=lowercase__  ,    name=lowercase__  ,    session=lowercase__  )
         tf.keras.backend.set_value(lowercase__  ,    lowercase__  )
         UpperCAmelCase_											=session.run(lowercase__  )
         print(F'Successfully created {tf_name}: {np.allclose(lowercase__  ,    lowercase__  )}'  )

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




def     a__ (					lowercase__=None  ):






   '''simple docstring'''






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

   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=lowercase__  ,    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_tf_available, is_torch_available


__lowercase  :   Optional[Any]						      ={
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[Any]						      =[
         """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GroupViTModel""",
         """GroupViTPreTrainedModel""",
         """GroupViTTextModel""",
         """GroupViTVisionModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[str]						      =[
         """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGroupViTModel""",
         """TFGroupViTPreTrainedModel""",
         """TFGroupViTTextModel""",
         """TFGroupViTVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

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




