



"""simple docstring"""







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def     UpperCamelCase_   (   lowerCAmelCase__				:    BertModel   ,       lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    Tuple								=			('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

  lowerCAmelCase_       :    Union[str, Any]								=			(
      ('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__						)

  lowerCAmelCase_       :    str								=			model.state_dict()

  def to_tf_var_name(lowerCAmelCase__				:    str						):
    for patt, repl in iter(lowerCAmelCase__						):
      lowerCAmelCase_       :    int								=			name.replace(lowerCAmelCase__   ,       lowerCAmelCase__						)
    return f"bert/{name}"

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

    lowerCAmelCase_       :    List[Any]								=			tf.train.Saver(tf.trainable_variables()						)
    saver.save(lowerCAmelCase__   ,       os.path.join(lowerCAmelCase__   ,       model_name.replace('-'   ,       '_'						) + '.ckpt'						)						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]=None						)						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			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'						)
  lowerCAmelCase_       :    Optional[Any]								=			parser.parse_args(lowerCAmelCase__						)

  lowerCAmelCase_       :    Union[str, 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=lowerCAmelCase__   ,       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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowercase__		:    Optional[int]				   =     {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:    Dict				   =     [
         """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TrOCRForCausalLM""",
         """TrOCRPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
     from .processing_trocr import TrOCRProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
     import sys

     lowercase__		:    int				   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





