

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
)
a_     =			logging.getLogger(__name__)

if __name__ == "__main__":
			a_     =			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_0522, type=int)
			a_     =			parser.parse_args()

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

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



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase	:     Dict   ={
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							"""ernie_m"""
						SCREAMING_SNAKE_CASE__					:     Dict[str, str]    =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




						def __init__( self      ,  __lowercase = 2_5_0_0_0_2      ,  __lowercase = 7_6_8      ,  __lowercase = 1_2      ,  __lowercase = 1_2      ,  __lowercase = 3_0_7_2      ,  __lowercase = "gelu"      ,  __lowercase = 0.1      ,  __lowercase = 0.1      ,  __lowercase = 5_1_4      ,  __lowercase = 0.02      ,  __lowercase = 1      ,  __lowercase = 1e-05      ,  __lowercase=None      ,  __lowercase=False      ,  __lowercase=0.0      ,  **__lowercase      ,  )    ->			Tuple:
										super().__init__(pad_token_id=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Tuple			 = vocab_size
										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Tuple			 = num_hidden_layers
										lowerCAmelCase_       :					int			 = num_attention_heads
										lowerCAmelCase_       :					Dict			 = intermediate_size
										lowerCAmelCase_       :					int			 = hidden_act
										lowerCAmelCase_       :					Union[str, Any]			 = hidden_dropout_prob
										lowerCAmelCase_       :					Any			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					Union[str, Any]			 = max_position_embeddings
										lowerCAmelCase_       :					str			 = initializer_range
										lowerCAmelCase_       :					List[str]			 = layer_norm_eps
										lowerCAmelCase_       :					List[Any]			 = classifier_dropout
										lowerCAmelCase_       :					Any			 = is_decoder
										lowerCAmelCase_       :					List[Any]			 = act_dropout