


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE							    =							{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE							    =							{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

SCREAMING_SNAKE_CASE							    =							{"""facebook/blenderbot-3B""": 1_2_8}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def  a   ():
   __a					=  (
       list(range(ord("""!"""   )		,				ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )		,				ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )		,				ord("""ÿ"""   ) + 1   )   )
   )
   __a					=  bs[:]
   __a					=  0
   for b in range(2**8   ):
      if b not in bs:
         bs.append(__lowercase   )
         cs.append(2**8 + n   )
         n += 1
   __a					=  [chr(__lowercase   ) for n in cs]
   return dict(zip(__lowercase		,				__lowercase   )   )




def  a   (lowerCAmelCase__   ):
   __a					=  set()
   __a					=  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)   )
      __a					=  char
   return pairs


class     __UpperCAmelCase							(       a__							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A  ,       __A  ,       __A="replace"  ,       __A="<s>"  ,       __A="</s>"  ,       __A="</s>"  ,       __A="<s>"  ,       __A="<unk>"  ,       __A="<pad>"  ,       __A="<mask>"  ,       __A=False  ,       **__A  ,       ):
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else bos_token
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else eos_token
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else sep_token
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else cls_token
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else unk_token
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __a					=  AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    ) if isinstance(__a  ,       __a    ) else mask_token

        super().__init__(
            errors=__a  ,       bos_token=__a  ,       eos_token=__a  ,       unk_token=__a  ,       sep_token=__a  ,       cls_token=__a  ,       pad_token=__a  ,       mask_token=__a  ,       add_prefix_space=__a  ,       **__a  ,       )

        with open(__a  ,       encoding="""utf-8"""    ) as vocab_handle:
           __a					=  json.load(__a    )
        __a					=  {v: k for k, v in self.encoder.items()}
        __a					=  errors  # how to handle errors in decoding
        __a					=  bytes_to_unicode()
        __a					=  {v: k for k, v in self.byte_encoder.items()}
        with open(__a  ,       encoding="""utf-8"""    ) as merges_handle:
           __a					=  merges_handle.read().split("""\n"""    )[1:-1]
        __a					=  [tuple(merge.split()    ) for merge in bpe_merges]
        __a					=  dict(zip(__a  ,       range(len(__a    )    )    )    )
        __a					=  {}
        __a					=  add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __a					=  re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""    )


     @property
     # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
     def 	snake_case_  (	self    ):
        return len(self.encoder    )


     def 	snake_case_  (	self    ):
        return dict(self.encoder  ,       **self.added_tokens_encoder    )


     def 	snake_case_  (	self  ,       __A    ):
        if token in self.cache:
           return self.cache[token]
        __a					=  tuple(__a    )
        __a					=  get_pairs(__a    )

        if not pairs:
           return token

        while True:
           __a					=  min(__a  ,       key=lambda __A    : self.bpe_ranks.get(__a  ,       float("""inf"""    )    )    )
           if bigram not in self.bpe_ranks:
              break
           __a					=  bigram
           __a					=  []
           __a					=  0
           while i < len(__a    ):
              try:
                 __a					=  word.index(__a  ,       __a    )
              except ValueError:
                 new_word.extend(word[i:]    )
                 break
              else:
                 new_word.extend(word[i:j]    )
                 __a					=  j

              if word[i] == first and i < len(__a    ) - 1 and word[i + 1] == second:
                 new_word.append(first + second    )
                 i += 2
              else:
                 new_word.append(word[i]    )
                 i += 1
           __a					=  tuple(__a    )
           __a					=  new_word
           if len(__a    ) == 1:
              break
           else:
              __a					=  get_pairs(__a    )
        __a					=  """ """.join(__a    )
        __a					=  word
        return word


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  []
        for token in re.findall(self.pat  ,       __a    ):
           __a					=  """""".join(
               self.byte_encoder[b] for b in token.encode("""utf-8"""    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__a    ).split(""" """    )    )
        return bpe_tokens


     def 	snake_case_  (	self  ,       __A    ):
        return self.encoder.get(__a  ,       self.encoder.get(self.unk_token    )    )


     def 	snake_case_  (	self  ,       __A    ):
        return self.decoder.get(__a    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """""".join(__a    )
        __a					=  bytearray([self.byte_decoder[c] for c in text]    ).decode("""utf-8"""  ,       errors=self.errors    )
        return text


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__a    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  os.path.join(
            __a  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
        __a					=  os.path.join(
            __a  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]    )

        with open(__a  ,       """w"""  ,       encoding="""utf-8"""    ) as f:
           f.write(json.dumps(self.encoder  ,       indent=2  ,       sort_keys=__a  ,       ensure_ascii=__a    ) + """\n"""    )

        __a					=  0
        with open(__a  ,       """w"""  ,       encoding="""utf-8"""    ) as writer:
           writer.write("""#version: 0.2\n"""    )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,       key=lambda __A    : kv[1]    ):
              if index != token_index:
                 logger.warning(
                     f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                     """ Please check that the tokenizer is not corrupted!"""    )
                 __a					=  token_index
              writer.write(""" """.join(__a    ) + """\n"""    )
              index += 1

        return vocab_file, merge_file


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__a  ,       token_ids_a=__a  ,       already_has_special_tokens=__a    )

        if token_ids_a is None:
           return [1] + ([0] * len(__a    )) + [1]
        return [1] + ([0] * len(__a    )) + [1, 1] + ([0] * len(__a    )) + [1]


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


     def 	snake_case_  (	self  ,       __A  ,       __A=False  ,       **__A    ):
        __a					=  kwargs.pop("""add_prefix_space"""  ,       self.add_prefix_space    )
        if (is_split_into_words or add_prefix_space) and (len(__a    ) > 0 and not text[0].isspace()):
           __a					=  """ """ + text
        return (text, kwargs)


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        return token_ids_a + [self.eos_token_id]


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  []
        for is_user, text in conversation.iter_texts():
           if is_user:
              # We need to space prefix as it's being done within blenderbot
              inputs.append(""" """ + text    )
           else:
              # Generated responses should contain them already.
              inputs.append(__a    )

        __a					=  """  """.join(__a    )
        __a					=  self.encode(__a    )
        if len(__a    ) > self.model_max_length:
           __a					=  input_ids[-self.model_max_length :]
           logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''    )
        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
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       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)
      lowercase      :   str      =       parser.parse_args()

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

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



