

import argparse

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


logging.set_verbosity_info()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			# Initialise PyTorch model
			lowercase__					  =				TaConfig.from_json_file(SCREAMING_SNAKE_CASE_  )
			print(f'''Building PyTorch model from configuration: {config}'''  )
			lowercase__					  =				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__":
							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_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

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




