"""simple docstring"""




import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a				:		List[str]	   =       logging.get_logger(__name__)


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

_a				:		Tuple	   =       {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_a				:		Dict	   =       {'facebook/blenderbot_small-90M': 512}





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

  _lowerCAmelCase						:	Optional[Any]        =					set(_lowerCamelCase     )
  return pairs






class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      int		 =		VOCAB_FILES_NAMES
     _UpperCamelCase							:      Any		 =		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:      Optional[Any]		 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:      Optional[Any]		 =		["input_ids", "attention_mask"]






     def __init__(		self    ,					a__    ,					a__    ,					a__="__start__"    ,					a__="__end__"    ,					a__="__unk__"    ,					a__="__null__"    ,					**a__    ,					):
       super().__init__(unk_token=a__    ,					bos_token=a__    ,					eos_token=a__    ,					pad_token=a__    ,					**a__  )

       with open(a__    ,					encoding="""utf-8"""  ) as vocab_handle:
         _lowerCAmelCase						:	Tuple        =					json.load(a__  )
       _lowerCAmelCase						:	int        =					{v: k for k, v in self.encoder.items()}
       with open(a__    ,					encoding="""utf-8"""  ) as merges_handle:
         _lowerCAmelCase						:	int        =					merges_handle.read().split("""\n"""  )[1:-1]
       _lowerCAmelCase						:	int        =					[tuple(merge.split()  ) for merge in merges]
       _lowerCAmelCase						:	str        =					dict(zip(a__    ,					range(len(a__  )  )  )  )
       _lowerCAmelCase						:	Union[str, Any]        =					{}






     @property
     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						:	Optional[int]        =					re.sub("""([.,!?()])"""    ,					r""" \1"""    ,					a__  )
       _lowerCAmelCase						:	Optional[int]        =					re.sub("""(')"""    ,					r""" \1 """    ,					a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					re.sub(r"""\s{2,}"""    ,					""" """    ,					a__  )
       if "\n" in token:
         _lowerCAmelCase						:	Dict        =					token.replace("""\n"""    ,					""" __newln__"""  )

       _lowerCAmelCase						:	List[str]        =					token.split(""" """  )
       _lowerCAmelCase						:	Any        =					[]
       for token in tokens:
         if not len(a__  ):
           continue

         _lowerCAmelCase						:	str        =					token.lower()
         _lowerCAmelCase						:	Optional[Any]        =					tuple(a__  )
         _lowerCAmelCase						:	str        =					tuple(list(word[:-1]  ) + [word[-1] + """</w>"""]  )
         _lowerCAmelCase						:	Any        =					get_pairs(a__  )

         if not pairs:
           words.append(a__  )
           continue

         while True:
           _lowerCAmelCase						:	Dict        =					min(a__    ,					key=lambda a__  : self.bpe_ranks.get(a__    ,					float("""inf"""  )  )  )
           if bigram not in self.bpe_ranks:
             break
           _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					bigram
           _lowerCAmelCase						:	str        =					[]
           _lowerCAmelCase						:	List[Any]        =					0

           while i < len(a__  ):
             try:
               _lowerCAmelCase						:	List[str]        =					word.index(a__    ,					a__  )
               new_word.extend(word[i:j]  )
               _lowerCAmelCase						:	List[Any]        =					j
             except ValueError:
               new_word.extend(word[i:]  )
               break

             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						:	Optional[int]        =					tuple(a__  )
           _lowerCAmelCase						:	str        =					new_word
           if len(a__  ) == 1:
             break
           else:
             _lowerCAmelCase						:	Tuple        =					get_pairs(a__  )
         _lowerCAmelCase						:	Union[str, Any]        =					"""@@ """.join(a__  )
         _lowerCAmelCase						:	Optional[int]        =					word[:-4]

         _lowerCAmelCase						:	List[str]        =					word
         words.append(a__  )
       return " ".join(a__  )






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

       _lowerCAmelCase						:	int        =					re.findall(r"""\S+\n?"""    ,					a__  )

       for token in words:
         split_tokens.extend(list(self.bpe(a__  ).split(""" """  )  )  )
       return split_tokens






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Union[str, Any]        =					token.lower()
       return self.encoder.get(a__    ,					self.encoder.get(self.unk_token  )  )






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






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					""" """.join(a__  ).replace("""@@ """    ,					""""""  ).strip()
       return out_string






     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						:	List[str]        =					os.path.join(
           a__    ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       _lowerCAmelCase						:	Tuple        =					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						:	Any        =					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						:	int        =					token_index
           writer.write(""" """.join(a__  ) + """\n"""  )
           index += 1

       return vocab_file, merge_file





"""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  )




