

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						# Initialize PyTorch model
						SCREAMING_SNAKE_CASE_:				List[Any]							= CanineConfig()
						SCREAMING_SNAKE_CASE_:				Dict							= CanineModel(_UpperCAmelCase					)
						model.eval()

						print(f"Building PyTorch model from configuration: {config}"					)

						# Load weights from tf checkpoint
						load_tf_weights_in_canine(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)

						# Save pytorch-model (weights and configuration)
						print(f"Save PyTorch model to {pytorch_dump_path}"					)
						model.save_pretrained(_UpperCAmelCase					)

						# Save tokenizer files
						SCREAMING_SNAKE_CASE_:				str							= CanineTokenizer()
						print(f"Save tokenizer files to {pytorch_dump_path}"					)
						tokenizer.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
						lowerCAmelCase							:						Dict     			=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--tf_checkpoint_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to a folder where the PyTorch model will be placed.""",
						)
						lowerCAmelCase							:						List[str]     			=		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= ArgumentParser(
						    description=(
						        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
						    )					)

						# Optional arguments for the launch helper
						parser.add_argument("--num_cores"       ,			type=_UpperCAmelCase       ,			default=1       ,			help="Number of TPU cores to use (1 or 8)."					)

						# positional
						parser.add_argument(
						    "training_script"       ,			type=_UpperCAmelCase       ,			help=(
						        "The full path to the single TPU training "
						        "program/script to be launched in parallel, "
						        "followed by all the arguments for the "
						        "training script"
						    )       ,			)

						# rest from the training program
						parser.add_argument("training_script_args"       ,			nargs=_UpperCAmelCase					)

						return parser.parse_args()







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= parse_args()

						# Import training_script as a module.
						SCREAMING_SNAKE_CASE_:				Tuple							= Path(args.training_script					)
						sys.path.append(str(script_fpath.parent.resolve()					)					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= script_fpath.stem
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= importlib.import_module(_UpperCAmelCase					)

						# Patch sys.argv
						SCREAMING_SNAKE_CASE_:				int							= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores					)]

						xmp.spawn(mod._mp_fn       ,			args=()       ,			nprocs=args.num_cores					)


if __name__ == "__main__":
						main()






