
import argparse

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


logging.set_verbosity_info()


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  # Initialise PyTorch model
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						TaConfig.from_json_file(snake_case    )
  print(F'''Building PyTorch model from configuration: {config}'''    )
  __SCREAMING_SNAKE_CASE     :					int			=						TaForConditionalGeneration(snake_case    )

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

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


if __name__ == "__main__":
   lowercase_											=	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."""
   )
   lowercase_											=	parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


from typing import TYPE_CHECKING

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


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

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