
'''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_   :       Tuple  ,		UpperCamelCase_   :       Dict )   ->    Tuple:







			'''simple docstring'''



			lowerCAmelCase__									=							CanineConfig()
			lowerCAmelCase__									=							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
			lowerCAmelCase__									=							CanineTokenizer()
			print(F"""Save tokenizer files to {pytorch_dump_path}""" )
			tokenizer.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
		UpperCAmelCase__				:	List[Any]					=						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.",
		)
		UpperCAmelCase__				:	List[Any]					=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)







'''simple docstring'''

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





