
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
)
lowercase						:      List[Any]									= logging.getLogger(__name__)

if __name__ == "__main__":
		lowercase						:      str									= 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=30522, type=int)
		lowercase						:      List[str]									= parser.parse_args()

		logger.info(f"Loading data from {args.data_file}")
		with open(args.data_file, 'rb') as fp:
				lowercase						:      Any									= pickle.load(fp)

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

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= 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(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)