
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__    :		List[str]          =   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		Union[str, Any]          =   ["""XLNetTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		int          =   ["""XLNetTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		Dict          =   [
         """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLNetForMultipleChoice""",
         """XLNetForQuestionAnswering""",
         """XLNetForQuestionAnsweringSimple""",
         """XLNetForSequenceClassification""",
         """XLNetForTokenClassification""",
         """XLNetLMHeadModel""",
         """XLNetModel""",
         """XLNetPreTrainedModel""",
         """load_tf_weights_in_xlnet""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__    :		Tuple          =   [
         """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLNetForMultipleChoice""",
         """TFXLNetForQuestionAnsweringSimple""",
         """TFXLNetForSequenceClassification""",
         """TFXLNetForTokenClassification""",
         """TFXLNetLMHeadModel""",
         """TFXLNetMainLayer""",
         """TFXLNetModel""",
         """TFXLNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet import XLNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet_fast import XLNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlnet import (
              XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLNetForMultipleChoice,
              XLNetForQuestionAnswering,
              XLNetForQuestionAnsweringSimple,
              XLNetForSequenceClassification,
              XLNetForTokenClassification,
              XLNetLMHeadModel,
              XLNetModel,
              XLNetPreTrainedModel,
              load_tf_weights_in_xlnet,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlnet import (
              TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLNetForMultipleChoice,
              TFXLNetForQuestionAnsweringSimple,
              TFXLNetForSequenceClassification,
              TFXLNetForTokenClassification,
              TFXLNetLMHeadModel,
              TFXLNetMainLayer,
              TFXLNetModel,
              TFXLNetPreTrainedModel,
          )

else:
     import sys

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








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Dict    		=      ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

  lowercase__  :   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(lowercase_   ):
    os.makedirs(lowercase_   )

  lowercase__  :   Optional[Any]    		=      model.state_dict()

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

  def create_tf_var(lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   List[str]    		=      tf.dtypes.as_dtype(tensor.dtype   )
    lowercase__  :   Tuple    		=      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:
      lowercase__  :   Any    		=      to_tf_var_name(lowercase_   )
      lowercase__  :   Dict    		=      state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose   ):
        lowercase__  :   Optional[int]    		=      torch_tensor.T
      lowercase__  :   Any    		=      create_tf_var(tensor=lowercase_ , name=lowercase_ , session=lowercase_   )
      tf.keras.backend.set_value(lowercase_ , lowercase_   )
      lowercase__  :   Optional[int]    		=      session.run(lowercase_   )
      print(F'Successfully created {tf_name}: {np.allclose(lowercase_ , lowercase_   )}'   )

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

def 		UpperCamelCase	(      lowercase_=None   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      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"""   )
  lowercase__  :   List[str]    		=      parser.parse_args(lowercase_   )

  lowercase__  :   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=lowercase_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name   )


if __name__ == "__main__":
     main()






