

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__      						=			{
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      						=			[
     '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TapasForMaskedLM''',
     '''TapasForQuestionAnswering''',
     '''TapasForSequenceClassification''',
     '''TapasModel''',
     '''TapasPreTrainedModel''',
     '''load_tf_weights_in_tapas''',
 ]
try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      						=			[
     '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFTapasForMaskedLM''',
     '''TFTapasForQuestionAnswering''',
     '''TFTapasForSequenceClassification''',
     '''TFTapasModel''',
     '''TFTapasPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
 from .tokenization_tapas import TapasTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tapas import (
      TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
      TapasForMaskedLM,
      TapasForQuestionAnswering,
      TapasForSequenceClassification,
      TapasModel,
      TapasPreTrainedModel,
      load_tf_weights_in_tapas,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_tapas import (
      TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTapasForMaskedLM,
      TFTapasForQuestionAnswering,
      TFTapasForSequenceClassification,
      TFTapasModel,
      TFTapasPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase__      						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

