




import os
from pathlib import Path


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	from torch.utils.cpp_extension import load

	snake_case_						       =	Path(_A				).resolve().parent.parent.parent / "kernels" / "deformable_detr"
	snake_case_						       =	[
	    root / filename
	    for filename in [
	        "vision.cpp",
	        os.path.join("cpu"	,      "ms_deform_attn_cpu.cpp"				),
	        os.path.join("cuda"	,      "ms_deform_attn_cuda.cu"				),
	    ]
	]

	load(
	    "MultiScaleDeformableAttention"	,      _A	,      with_cuda=_A	,      extra_include_paths=[str(_A				)]	,      extra_cflags=["-DWITH_CUDA=1"]	,      extra_cuda_cflags=[
	        "-DCUDA_HAS_FP16=1",
	        "-D__CUDA_NO_HALF_OPERATORS__",
	        "-D__CUDA_NO_HALF_CONVERSIONS__",
	        "-D__CUDA_NO_HALF2_OPERATORS__",
	    ]	,      )

	import MultiScaleDeformableAttention as MSDA

	return MSDA






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
