


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase   : List[str]             =				logging.get_logger(__name__)

lowerCAmelCase   : Optional[int]             =				{
    """tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
        """https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase	(    __snake_case):


				'''simple docstring'''


				_snake_case						       =				"""gptsan-japanese"""
				_snake_case						       =				[
				    """past_key_values""",
				]
				_snake_case						       =				{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(     self							,      a_=3_6_0_0_0							,      a_=1_2_8_0							,      a_=1_0_2_4							,      a_=8_1_9_2							,      a_=4_0_9_6							,      a_=1_2_8							,      a_=1_0							,      a_=0							,      a_=1_6							,      a_=1_6							,      a_=1_2_8							,      a_=0.0							,      a_=1e-5							,      a_=False							,      a_=0.0							,      a_="float32"							,      a_=False							,      a_=False							,      a_=False							,      a_=0.0_02							,      a_=False							,      a_=True							,      a_=3_5_9_9_8							,      a_=3_5_9_9_5							,      a_=3_5_9_9_9							,      **a_							,      )    ->  List[str]:
					lowercase						:		List[Any]   					=		vocab_size
					lowercase						:		List[Any]   					=		max_position_embeddings
					lowercase						:		int   					=		d_model
					lowercase						:		List[Any]   					=		d_ff
					lowercase						:		Tuple   					=		d_ext
					lowercase						:		Tuple   					=		d_spout
					lowercase						:		Union[str, Any]   					=		num_switch_layers
					lowercase						:		int   					=		num_ext_layers
					lowercase						:		Tuple   					=		num_switch_layers + num_ext_layers
					lowercase						:		Optional[int]   					=		num_heads
					lowercase						:		Optional[Any]   					=		num_experts
					lowercase						:		List[str]   					=		expert_capacity
					lowercase						:		Optional[int]   					=		dropout_rate
					lowercase						:		Any   					=		layer_norm_epsilon
					lowercase						:		str   					=		router_bias
					lowercase						:		Optional[int]   					=		router_jitter_noise
					lowercase						:		Optional[Any]   					=		router_dtype
					lowercase						:		List[str]   					=		router_ignore_padding_tokens
					lowercase						:		Dict   					=		output_hidden_states
					lowercase						:		Optional[Any]   					=		output_attentions
					lowercase						:		Dict   					=		initializer_factor
					lowercase						:		Union[str, Any]   					=		output_router_logits
					lowercase						:		List[str]   					=		use_cache

					super().__init__(
					    separator_token_id=a_							,      pad_token_id=a_							,      eos_token_id=a_							,      **a_							,      )










"""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)
