
from typing import TYPE_CHECKING

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


lowerCamelCase_				:							List[Any]       =    {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_				:							Dict       =    ["CamembertTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_				:							Tuple       =    ["CamembertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_				:							Optional[int]       =    [
     "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "CamembertForCausalLM",
     "CamembertForMaskedLM",
     "CamembertForMultipleChoice",
     "CamembertForQuestionAnswering",
     "CamembertForSequenceClassification",
     "CamembertForTokenClassification",
     "CamembertModel",
     "CamembertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_				:							Dict       =    [
     "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFCamembertForCausalLM",
     "TFCamembertForMaskedLM",
     "TFCamembertForMultipleChoice",
     "TFCamembertForQuestionAnswering",
     "TFCamembertForSequenceClassification",
     "TFCamembertForTokenClassification",
     "TFCamembertModel",
     "TFCamembertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_camembert import CamembertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_camembert_fast import CamembertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_camembert import (
      CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CamembertForCausalLM,
      CamembertForMaskedLM,
      CamembertForMultipleChoice,
      CamembertForQuestionAnswering,
      CamembertForSequenceClassification,
      CamembertForTokenClassification,
      CamembertModel,
      CamembertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_camembert import (
      TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCamembertForCausalLM,
      TFCamembertForMaskedLM,
      TFCamembertForMultipleChoice,
      TFCamembertForQuestionAnswering,
      TFCamembertForSequenceClassification,
      TFCamembertForTokenClassification,
      TFCamembertModel,
      TFCamembertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase_				:							Dict       =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[Any]       =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 lowerCamelCase_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
