





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''openai-gpt'''
    UpperCamelCase     = {
        '''max_position_embeddings''': '''n_positions''',
        '''hidden_size''': '''n_embd''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }


    def __init__(		self ,		A=4_0478 ,		A=512 ,		A=768 ,		A=12 ,		A=12 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=0.1 ,		A=1e-5 ,		A=0.02 ,		A="cls_index" ,		A=True ,		A=None ,		A=True ,		A=0.1 ,		**A ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			n_positions
        _SCREAMING_SNAKE_CASE									=			n_embd
        _SCREAMING_SNAKE_CASE									=			n_layer
        _SCREAMING_SNAKE_CASE									=			n_head
        _SCREAMING_SNAKE_CASE									=			afn
        _SCREAMING_SNAKE_CASE									=			resid_pdrop
        _SCREAMING_SNAKE_CASE									=			embd_pdrop
        _SCREAMING_SNAKE_CASE									=			attn_pdrop
        _SCREAMING_SNAKE_CASE									=			layer_norm_epsilon
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			summary_type
        _SCREAMING_SNAKE_CASE									=			summary_use_proj
        _SCREAMING_SNAKE_CASE									=			summary_activation
        _SCREAMING_SNAKE_CASE									=			summary_first_dropout
        _SCREAMING_SNAKE_CASE									=			summary_proj_to_labels
        super().__init__(**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
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= 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=30_522, type=int)
      lowercase_					= parser.parse_args()

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

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



