



import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__     = logging.getLogger(__name__)





def 							A			(  )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     argparse.ArgumentParser(
			    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'					)
			parser.add_argument('--file_path'							,     type=_UpperCAmelCase							,     default='data/dump.txt'							,     help='The path to the data.'					)
			parser.add_argument('--tokenizer_type'							,     type=_UpperCAmelCase							,     default='bert'							,     choices=['bert', 'roberta', 'gpt2']					)
			parser.add_argument('--tokenizer_name'							,     type=_UpperCAmelCase							,     default='bert-base-uncased'							,     help='The tokenizer to use.'					)
			parser.add_argument('--dump_file'							,     type=_UpperCAmelCase							,     default='data/dump'							,     help='The dump file prefix.'					)
			_UpperCAmelCase	 =     parser.parse_args()

			logger.info(F"Loading Tokenizer ({args.tokenizer_name})"					)
			if args.tokenizer_type == "bert":
						_UpperCAmelCase	 =     BertTokenizer.from_pretrained(args.tokenizer_name					)
						_UpperCAmelCase	 =     tokenizer.special_tokens_map['cls_token']  # `[CLS]`
						_UpperCAmelCase	 =     tokenizer.special_tokens_map['sep_token']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						_UpperCAmelCase	 =     RobertaTokenizer.from_pretrained(args.tokenizer_name					)
						_UpperCAmelCase	 =     tokenizer.special_tokens_map['cls_token']  # `<s>`
						_UpperCAmelCase	 =     tokenizer.special_tokens_map['sep_token']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						_UpperCAmelCase	 =     GPTaTokenizer.from_pretrained(args.tokenizer_name					)
						_UpperCAmelCase	 =     tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
						_UpperCAmelCase	 =     tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

			logger.info(F"Loading text from {args.file_path}"					)
			with open(args.file_path							,     'r'							,     encoding='utf8'					) as fp:
						_UpperCAmelCase	 =     fp.readlines()

			logger.info('Start encoding'					)
			logger.info(F"{len(_UpperCAmelCase					)} examples to process."					)

			_UpperCAmelCase	 =     []
			_UpperCAmelCase	 =     0
			_UpperCAmelCase	 =     10_000
			_UpperCAmelCase	 =     time.time()
			for text in data:
						_UpperCAmelCase	 =     F"{bos} {text.strip()} {sep}"
						_UpperCAmelCase	 =     tokenizer.encode(_UpperCAmelCase							,     add_special_tokens=_UpperCAmelCase					)
						rslt.append(_UpperCAmelCase					)

						iter += 1
						if iter % interval == 0:
									_UpperCAmelCase	 =     time.time()
									logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"					)
									_UpperCAmelCase	 =     time.time()
			logger.info('Finished binarization'					)
			logger.info(F"{len(_UpperCAmelCase					)} examples processed."					)

			_UpperCAmelCase	 =     F"{args.dump_file}.{args.tokenizer_name}.pickle"
			_UpperCAmelCase	 =     tokenizer.vocab_size
			if vocab_size < (1 << 16):
						_UpperCAmelCase	 =     [np.uintaa(_UpperCAmelCase					) for d in rslt]
			else:
						_UpperCAmelCase	 =     [np.intaa(_UpperCAmelCase					) for d in rslt]
			random.shuffle(rslt_					)
			logger.info(F"Dump to {dp_file}"					)
			with open(_UpperCAmelCase							,     'wb'					) as handle:
						pickle.dump(rslt_							,     _UpperCAmelCase							,     protocol=pickle.HIGHEST_PROTOCOL					)


if __name__ == "__main__":
							main()





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

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

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = 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 T5 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."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
