"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :      int							={
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__   :      Union[str, Any]							=[
      """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AltCLIPPreTrainedModel""",
      """AltCLIPModel""",
      """AltCLIPTextModel""",
      """AltCLIPVisionModel""",
  ]


if TYPE_CHECKING:
  from .configuration_altclip import (
      ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AltCLIPConfig,
      AltCLIPTextConfig,
      AltCLIPVisionConfig,
  )
  from .processing_altclip import AltCLIPProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_altclip import (
        ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        AltCLIPModel,
        AltCLIPPreTrainedModel,
        AltCLIPTextModel,
        AltCLIPVisionModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__   :      str							=_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       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							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     :  str											=    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     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

