






"""simple docstring"""







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """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(_snake_case							):
            os.makedirs(_snake_case							)

      UpperCAmelCase          =	model.state_dict()

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

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

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

def _a			(		_snake_case=None							):







      """simple docstring"""






      UpperCAmelCase          =	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          =	parser.parse_args(_snake_case							)

      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=_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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase					      =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""RoFormerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RoFormerForCausalLM""",
      """RoFormerForMaskedLM""",
      """RoFormerForMultipleChoice""",
      """RoFormerForQuestionAnswering""",
      """RoFormerForSequenceClassification""",
      """RoFormerForTokenClassification""",
      """RoFormerLayer""",
      """RoFormerModel""",
      """RoFormerPreTrainedModel""",
      """load_tf_weights_in_roformer""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRoFormerForCausalLM""",
      """TFRoFormerForMaskedLM""",
      """TFRoFormerForMultipleChoice""",
      """TFRoFormerForQuestionAnswering""",
      """TFRoFormerForSequenceClassification""",
      """TFRoFormerForTokenClassification""",
      """TFRoFormerLayer""",
      """TFRoFormerModel""",
      """TFRoFormerPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaxRoFormerForMaskedLM""",
      """FlaxRoFormerForMultipleChoice""",
      """FlaxRoFormerForQuestionAnswering""",
      """FlaxRoFormerForSequenceClassification""",
      """FlaxRoFormerForTokenClassification""",
      """FlaxRoFormerModel""",
      """FlaxRoFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



