import json
import os
from typing import Optional, Tuple

import regex as re

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


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

__lowerCamelCase    :				str							     =	{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__lowerCamelCase    :				Any							     =	{
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

__lowerCamelCase    :				Tuple							     =	{
    '''ctrl''': 256,
}

__lowerCamelCase    :				Union[str, Any]							     =	{
    '''Pregnancy''': 16_8629,
    '''Christianity''': 7675,
    '''Explain''': 10_6423,
    '''Fitness''': 6_3440,
    '''Saving''': 6_3163,
    '''Ask''': 2_7171,
    '''Ass''': 9_5985,
    '''Joke''': 16_3509,
    '''Questions''': 4_5622,
    '''Thoughts''': 4_9605,
    '''Retail''': 5_2342,
    '''Feminism''': 16_4338,
    '''Writing''': 1_1992,
    '''Atheism''': 19_2263,
    '''Netflix''': 4_8616,
    '''Computing''': 3_9639,
    '''Opinion''': 4_3213,
    '''Alone''': 4_4967,
    '''Funny''': 5_8917,
    '''Gaming''': 4_0358,
    '''Human''': 4088,
    '''India''': 1331,
    '''Joker''': 7_7138,
    '''Diet''': 3_6206,
    '''Legal''': 1_1859,
    '''Norman''': 4939,
    '''Tip''': 7_2689,
    '''Weight''': 5_2343,
    '''Movies''': 4_6273,
    '''Running''': 2_3425,
    '''Science''': 2090,
    '''Horror''': 3_7793,
    '''Confession''': 6_0572,
    '''Finance''': 1_2250,
    '''Politics''': 1_6360,
    '''Scary''': 19_1985,
    '''Support''': 1_2654,
    '''Technologies''': 3_2516,
    '''Teenage''': 6_6160,
    '''Event''': 3_2769,
    '''Learned''': 6_7460,
    '''Notion''': 18_2770,
    '''Wikipedia''': 3_7583,
    '''Books''': 6665,
    '''Extract''': 7_6050,
    '''Confessions''': 10_2701,
    '''Conspiracy''': 7_5932,
    '''Links''': 6_3674,
    '''Narcissus''': 15_0425,
    '''Relationship''': 5_4766,
    '''Relationships''': 13_4796,
    '''Reviews''': 4_1671,
    '''News''': 4256,
    '''Translation''': 2_6820,
    '''multilingual''': 12_8406,
}



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   set()
  SCREAMING_SNAKE_CASE__    							=   word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)			)
    SCREAMING_SNAKE_CASE__    							=   char

  SCREAMING_SNAKE_CASE__    							=   set(__UpperCamelCase			)
  return pairs







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							CONTROL_CODES




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict ,			_lowercase    :       Tuple="<unk>" ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     super().__init__(unk_token=_lowercase ,			**_lowercase   )

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




   @property
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     return len(self.encoder   )




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return dict(self.encoder ,			**self.added_tokens_encoder   )




   def 			__a		(					self    :       int ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     if token in self.cache:
       return self.cache[token]
     SCREAMING_SNAKE_CASE__    							=   tuple(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tuple(list(word[:-1]   ) + [word[-1] + """</w>"""]   )
     SCREAMING_SNAKE_CASE__    							=   get_pairs(_lowercase   )

     if not pairs:
       return token

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

         if word[i] == first and i < len(_lowercase   ) - 1 and word[i + 1] == second:
           new_word.append(first + second   )
           i += 2
         else:
           new_word.append(word[i]   )
           i += 1
       SCREAMING_SNAKE_CASE__    							=   tuple(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   new_word
       if len(_lowercase   ) == 1:
         break
       else:
         SCREAMING_SNAKE_CASE__    							=   get_pairs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   """@@ """.join(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   word[:-4]
     SCREAMING_SNAKE_CASE__    							=   word
     return word




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []

     SCREAMING_SNAKE_CASE__    							=   re.findall(R"""\S+\n?""" ,			_lowercase   )

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




   def 			__a		(					self    :       List[str] ,			_lowercase    :       str   ):



     """simple docstring"""






     return self.encoder.get(_lowercase ,			self.encoder.get(self.unk_token   )   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     return self.decoder.get(_lowercase ,			self.unk_token   )




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """ """.join(_lowercase   ).replace("""@@ """ ,			""""""   ).strip()
     return out_string




   def 			__a		(					self    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if not os.path.isdir(_lowercase   ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
       return
     SCREAMING_SNAKE_CASE__    							=   os.path.join(
         _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
     SCREAMING_SNAKE_CASE__    							=   os.path.join(
         _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

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

     SCREAMING_SNAKE_CASE__    							=   0
     with open(_lowercase ,			"""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 _lowercase   : 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!"""   )
           SCREAMING_SNAKE_CASE__    							=   token_index
         writer.write(""" """.join(_lowercase   ) + """\n"""   )
         index += 1

     return vocab_file, merge_file

   # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
   #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
   #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
   #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
   #     return ''.join(tokens_generated_so_far)

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowerCamelCase    :				Union[str, Any]							     =	datasets.utils.logging.get_logger(__name__)







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							ParquetConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     return datasets.DatasetInfo(features=self.config.features   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     if not self.config.data_files:
       raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
     SCREAMING_SNAKE_CASE__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
         for file in itertools.chain.from_iterable(_lowercase   ):
           with open(_lowercase ,			"""rb"""   ) as f:
             SCREAMING_SNAKE_CASE__    							=   datasets.Features.from_arrow_schema(pq.read_schema(_lowercase   )   )
           break
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       pa.Table   ):



     """simple docstring"""






     if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       SCREAMING_SNAKE_CASE__    							=   table_cast(_lowercase ,			self.info.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
         raise ValueError(
             f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""   )
     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase   )   ):
       with open(_lowercase ,			"""rb"""   ) as f:
         SCREAMING_SNAKE_CASE__    							=   pq.ParquetFile(_lowercase   )
         try:
           for batch_idx, record_batch in enumerate(
               parquet_file.iter_batches(batch_size=self.config.batch_size ,			columns=self.config.columns   )   ):
             SCREAMING_SNAKE_CASE__    							=   pa.Table.from_batches([record_batch]   )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield f"""{file_idx}_{batch_idx}""", self._cast_table(_lowercase   )
         except ValueError as e:
           logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
           raise
