"""simple docstring"""




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

_a				:		Optional[Any]	   =       logging.get_logger(__name__)


_a				:		Any	   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_a				:		int	   =       {
    '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'
    },
}

_a				:		Any	   =       {'facebook/blenderbot-3B': 128}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[Any]:
  _lowerCAmelCase						:	List[Any]        =					(
      list(range(ord("""!"""     )      ,ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )      ,ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )      ,ord("""ÿ"""     ) + 1     )     )
  )
  _lowerCAmelCase						:	Dict        =					bs[:]
  _lowerCAmelCase						:	int        =					0
  for b in range(2**8     ):
    if b not in bs:
      bs.append(_lowerCamelCase     )
      cs.append(2**8 + n     )
      n += 1
  _lowerCAmelCase						:	Tuple        =					[chr(_lowerCamelCase     ) for n in cs]
  return dict(zip(_lowerCamelCase      ,_lowerCamelCase     )     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any     )	->				Tuple:
  _lowerCAmelCase						:	Dict        =					set()
  _lowerCAmelCase						:	Dict        =					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)     )
    _lowerCAmelCase						:	Optional[Any]        =					char
  return pairs






class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      List[str]		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      Any		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      Optional[Any]		 =		["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__    ,					):
       _lowerCAmelCase						:	Optional[Any]        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else bos_token
       _lowerCAmelCase						:	Tuple        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else eos_token
       _lowerCAmelCase						:	int        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else sep_token
       _lowerCAmelCase						:	Dict        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else cls_token
       _lowerCAmelCase						:	Tuple        =					AddedToken(a__    ,					lstrip=a__    ,					rstrip=a__  ) if isinstance(a__    ,					a__  ) else unk_token
       _lowerCAmelCase						:	Union[str, Any]        =					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
       _lowerCAmelCase						:	Optional[Any]        =					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:
         _lowerCAmelCase						:	Union[str, Any]        =					json.load(a__  )
       _lowerCAmelCase						:	str        =					{v: k for k, v in self.encoder.items()}
       _lowerCAmelCase						:	List[Any]        =					errors  # how to handle errors in decoding
       _lowerCAmelCase						:	Union[str, Any]        =					bytes_to_unicode()
       _lowerCAmelCase						:	Tuple        =					{v: k for k, v in self.byte_encoder.items()}
       with open(a__    ,					encoding="""utf-8"""  ) as merges_handle:
         _lowerCAmelCase						:	Any        =					merges_handle.read().split("""\n"""  )[1:-1]
       _lowerCAmelCase						:	int        =					[tuple(merge.split()  ) for merge in bpe_merges]
       _lowerCAmelCase						:	int        =					dict(zip(a__    ,					range(len(a__  )  )  )  )
       _lowerCAmelCase						:	Union[str, Any]        =					{}
       _lowerCAmelCase						:	str        =					add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       _lowerCAmelCase						:	Dict        =					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    __A  (		self  ):
       return len(self.encoder  )






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






     def    __A  (		self    ,					a__  ):
       if token in self.cache:
         return self.cache[token]
       _lowerCAmelCase						:	Union[str, Any]        =					tuple(a__  )
       _lowerCAmelCase						:	Tuple        =					get_pairs(a__  )

       if not pairs:
         return token

       while True:
         _lowerCAmelCase						:	str        =					min(a__    ,					key=lambda a__  : self.bpe_ranks.get(a__    ,					float("""inf"""  )  )  )
         if bigram not in self.bpe_ranks:
           break
         _lowerCAmelCase						:	str        =					bigram
         _lowerCAmelCase						:	Any        =					[]
         _lowerCAmelCase						:	Optional[Any]        =					0
         while i < len(a__  ):
           try:
             _lowerCAmelCase						:	List[str]        =					word.index(a__    ,					a__  )
           except ValueError:
             new_word.extend(word[i:]  )
             break
           else:
             new_word.extend(word[i:j]  )
             _lowerCAmelCase						:	Any        =					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
         _lowerCAmelCase						:	Union[str, Any]        =					tuple(a__  )
         _lowerCAmelCase						:	Tuple        =					new_word
         if len(a__  ) == 1:
           break
         else:
           _lowerCAmelCase						:	Union[str, Any]        =					get_pairs(a__  )
       _lowerCAmelCase						:	Dict        =					""" """.join(a__  )
       _lowerCAmelCase						:	List[Any]        =					word
       return word






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Union[str, Any]        =					[]
       for token in re.findall(self.pat    ,					a__  ):
         _lowerCAmelCase						:	List[Any]        =					"""""".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    __A  (		self    ,					a__  ):
       return self.encoder.get(a__    ,					self.encoder.get(self.unk_token  )  )






     def    __A  (		self    ,					a__  ):
       return self.decoder.get(a__  )






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






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       if not os.path.isdir(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:	Optional[Any]        =					os.path.join(
           a__    ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       _lowerCAmelCase						:	Optional[Any]        =					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"""  )

       _lowerCAmelCase						:	Optional[int]        =					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!"""  )
             _lowerCAmelCase						:	Tuple        =					token_index
           writer.write(""" """.join(a__  ) + """\n"""  )
           index += 1

       return vocab_file, merge_file






     def    __A  (		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    __A  (		self    ,					a__    ,					a__ = None  ):
       _lowerCAmelCase						:	Dict        =					[self.sep_token_id]
       _lowerCAmelCase						:	int        =					[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    __A  (		self    ,					a__    ,					a__=False    ,					**a__  ):
       _lowerCAmelCase						:	int        =					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()):
         _lowerCAmelCase						:	Optional[int]        =					""" """ + text
       return (text, kwargs)






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






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Tuple        =					[]
       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__  )

       _lowerCAmelCase						:	Union[str, Any]        =					"""  """.join(a__  )
       _lowerCAmelCase						:	Dict        =					self.encode(a__  )
       if len(a__  ) > self.model_max_length:
         _lowerCAmelCase						:	str        =					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"""




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = True    ,					a__ = "arrow"    ,					**a__    ,					):
       super().__init__(
           split=a__    ,					features=a__    ,					cache_dir=a__    ,					keep_in_memory=a__    ,					streaming=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					load_from_cache_file
       _lowerCAmelCase						:	str        =					file_format
       _lowerCAmelCase						:	Dict        =					Spark(
           df=a__    ,					features=a__    ,					cache_dir=a__    ,					working_dir=a__    ,					**a__    ,					)






     def    __A  (		self  ):
       if self.streaming:
         return self.builder.as_streaming_dataset(split=self.split  )
       _lowerCAmelCase						:	Dict        =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=a__    ,					file_format=self._file_format    ,					)
       return self.builder.as_dataset(split=self.split  )




