


"""simple docstring"""



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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__snake_case             =   {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

__snake_case             =   {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase			:	str							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:	List[str]							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:	Any							  =  ['''input_ids''', '''attention_mask''']
   __UpperCAmelCase			:	List[str]							  =  GPTaTokenizer







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__="<|endoftext|>"						,     UpperCamelCase__="<|endoftext|>"						,     UpperCamelCase__="<|endoftext|>"						,     UpperCamelCase__=False						,     **UpperCamelCase__						,     )     ->		Optional[Any]:

       '''simple docstring'''




       super().__init__(
           UpperCamelCase__						,     UpperCamelCase__						,     tokenizer_file=UpperCamelCase__						,     unk_token=UpperCamelCase__						,     bos_token=UpperCamelCase__						,     eos_token=UpperCamelCase__						,     add_prefix_space=UpperCamelCase__						,     **UpperCamelCase__						,     )

       snake_case   :				List[str]				 =   kwargs.pop("add_bos_token"						,     UpperCamelCase__						)

       snake_case   :				int				 =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
       if pre_tok_state.get("add_prefix_space"						,     UpperCamelCase__						) != add_prefix_space:
           snake_case   :				int				 =   getattr(UpperCamelCase__						,     pre_tok_state.pop("type"						)						)
           snake_case   :				Optional[Any]				 =   add_prefix_space
           snake_case   :				Union[str, Any]				 =   pre_tok_class(**UpperCamelCase__						)

       snake_case   :				List[str]				 =   add_prefix_space







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		BatchEncoding:

       '''simple docstring'''




       snake_case   :				Any				 =   kwargs.get("is_split_into_words"						,     UpperCamelCase__						)
       assert self.add_prefix_space or not is_split_into_words, (
           F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		BatchEncoding:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   kwargs.get("is_split_into_words"						,     UpperCamelCase__						)

       assert self.add_prefix_space or not is_split_into_words, (
           F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		Tuple[str]:

       '''simple docstring'''




       snake_case   :				int				 =   self._tokenizer.model.save(UpperCamelCase__						,     name=UpperCamelCase__						)
       return tuple(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   []
       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:
           snake_case   :				Dict				 =   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
)
__snake_case             =   logging.getLogger(__name__)

if __name__ == "__main__":
  __snake_case             =   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)
  __snake_case             =   parser.parse_args()

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

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





