





import argparse

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


logging.set_verbosity_info()


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	snake_case_		:      str           =						TaConfig.from_json_file(__UpperCamelCase )
	print(f"""Building PyTorch model from configuration: {config}""" )
	snake_case_		:      Any           =						TaForConditionalGeneration(__UpperCamelCase )

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

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		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."""
						)
						_SCREAMING_SNAKE_CASE   						=		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, is_vision_available


__lowercase    =				{
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				[
				    '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VideoMAEForPreTraining''',
				    '''VideoMAEModel''',
				    '''VideoMAEPreTrainedModel''',
				    '''VideoMAEForVideoClassification''',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				['''VideoMAEFeatureExtractor''']
				__lowercase    =				['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
				from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_videomae import (
								    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VideoMAEForPreTraining,
								    VideoMAEForVideoClassification,
								    VideoMAEModel,
								    VideoMAEPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_videomae import VideoMAEFeatureExtractor
								from .image_processing_videomae import VideoMAEImageProcessor

else:
				import sys

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



