


import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def 	_lowerCAmelCase					( UpperCamelCase__:  Dict				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Any    )			-> Optional[Any]:





	"""simple docstring"""







	A             =					FunnelConfig.from_json_file(UpperCamelCase__    )
	print(f'Building PyTorch model from configuration: {config}'    )
	A             =					FunnelBaseModel(UpperCamelCase__    ) if base_model else FunnelModel(UpperCamelCase__    )

	# Load weights from tf checkpoint
	load_tf_weights_in_funnel(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__    )

	# Save pytorch-model
	print(f'Save PyTorch model to {pytorch_dump_path}'    )
	torch.save(model.state_dict()				,       UpperCamelCase__    )


if __name__ == "__main__":
				_lowercase							:   Any						=						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 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."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				_lowercase							:   List[Any]						=						parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)







_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



