


'''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_					(				__SCREAMING_SNAKE_CASE :					Optional[int]	,   __SCREAMING_SNAKE_CASE :					Tuple	,   __SCREAMING_SNAKE_CASE :					List[str]       )     ->  Any:
					# Initialise PyTorch model
					__SCREAMING_SNAKE_CASE					:							Tuple			   = TaConfig.from_json_file(__SCREAMING_SNAKE_CASE       )
					print(f"""Building PyTorch model from configuration: {config}"""       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = TaForConditionalGeneration(__SCREAMING_SNAKE_CASE       )

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

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


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










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      =     {
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A      =     [
						    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DecisionTransformerGPT2Model""",
						    """DecisionTransformerGPT2PreTrainedModel""",
						    """DecisionTransformerModel""",
						    """DecisionTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_decision_transformer import (
						    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DecisionTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_decision_transformer import (
												    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DecisionTransformerGPTaModel,
												    DecisionTransformerGPTaPreTrainedModel,
												    DecisionTransformerModel,
												    DecisionTransformerPreTrainedModel,
												)


else:
						import sys

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


