





'''simple docstring'''


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     )   ->Dict:



	"""simple docstring"""







	A					=   CanineConfig()
	A					=   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
	A					=   CanineTokenizer()
	print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
	tokenizer.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						str					     =							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					:						Optional[int]					     =							parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)








'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
				for line in f_p:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('')
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

