

from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
a_      =     HfArgumentParser(InitializationArguments)
a_      =     parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
a_      =     AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
a_      =     {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
a_      =     AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
a_      =     AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)







"""simple docstring"""







import argparse
import logging
import pickle
from collections import Counter


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

if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser(
				    description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
				)
				parser.add_argument(
				    '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
				)
				parser.add_argument(
				    '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
				)
				parser.add_argument('--vocab_size', default=3_05_22, type=int)
				_lowerCAmelCase            =	parser.parse_args()

				logger.info(F'Loading data from {args.data_file}')
				with open(args.data_file, 'rb') as fp:
								_lowerCAmelCase            =	pickle.load(fp)

				logger.info('Counting occurrences for MLM.')
				_lowerCAmelCase            =	Counter()
				for tk_ids in data:
								counter.update(tk_ids)
				_lowerCAmelCase            =	[0] * args.vocab_size
				for k, v in counter.items():
								_lowerCAmelCase            =	v

				logger.info(F'Dump to {args.token_counts_dump}')
				with open(args.token_counts_dump, 'wb') as handle:
								pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
