


"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


a__				:							Union[str, Any]						=     logging.get_logger(__name__)

a__				:							str						=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__				:							Dict						=     {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

a__				:							Dict						=     {
    '''gpt-neox-20b''': 2_0_4_8,
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[Any]    					=			VOCAB_FILES_NAMES
	snake_case__				:  Dict    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Union[str, Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  Union[str, Any]    					=			["input_ids", "attention_mask"]




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				List[str]=None			,			UpperCAmelCase__			:				str=None			,			UpperCAmelCase__			:				Union[str, Any]="<|endoftext|>"			,			UpperCAmelCase__			:				Optional[Any]="<|endoftext|>"			,			UpperCAmelCase__			:				List[str]="<|endoftext|>"			,			UpperCAmelCase__			:				List[Any]=False			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	Optional[Any]:
						super().__init__(
						    UpperCAmelCase__			,			UpperCAmelCase__			,			tokenizer_file=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			bos_token=UpperCAmelCase__			,			eos_token=UpperCAmelCase__			,			add_prefix_space=UpperCAmelCase__			,			**UpperCAmelCase__			,			)

						__SCREAMING_SNAKE_CASE						   =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
						if pre_tok_state.get("add_prefix_space"			,			UpperCAmelCase__   ) != add_prefix_space:
											__SCREAMING_SNAKE_CASE						   =				getattr(UpperCAmelCase__			,			pre_tok_state.pop("type"   )   )
											__SCREAMING_SNAKE_CASE						   =				add_prefix_space
											__SCREAMING_SNAKE_CASE						   =				pre_tok_class(**UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				add_prefix_space




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						__SCREAMING_SNAKE_CASE						   =				self._tokenizer.model.save(UpperCAmelCase__			,			name=UpperCAmelCase__   )
						return tuple(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				"Conversation"   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				[]
						for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   ) + [self.eos_token_id]   )

						if len(UpperCAmelCase__   ) > self.model_max_length:
											__SCREAMING_SNAKE_CASE						   =				input_ids[-self.model_max_length :]
						return input_ids








"""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
)
UpperCAmelCase_   : Dict   			= logging.getLogger(__name__)

if __name__ == "__main__":
  UpperCAmelCase_   : List[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)
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()

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

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


