code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  __A :		Dict      							=    '''none'''
  __A :		Dict      							=    '''none'''
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   List[Any]          =							value.split(snake_case_    )
      return len(snake_case_    ) > 1 and any((len(snake_case_    ) == 3 for x in split)    )
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(snake_case_						,snake_case_    ):
          return value
      if isinstance(snake_case_						,snake_case_    ):
          return float(snake_case_    )
      if not isinstance(snake_case_						,snake_case_    ):
          raise ValueError("""Argument value is not a string. Can't parse it as float"""    )
      UpperCamelCase				:   Dict          =							value
  
      try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
              return float(sanitized.replace(""","""						,""""""    )    )
          # 1,000
          if "," in sanitized and _split_thousands(""","""						,snake_case_    ):
              return float(sanitized.replace(""","""						,""""""    )    )
          # 5,5556
          if "," in sanitized and sanitized.count(""","""    ) == 1 and not _split_thousands(""","""						,snake_case_    ):
              return float(sanitized.replace(""","""						,"""."""    )    )
          # 0.0.0.1
          if sanitized.count("""."""    ) > 1:
              return float(sanitized.replace("""."""						,""""""    )    )
          # 0,0,0,1
          if sanitized.count(""","""    ) > 1:
              return float(sanitized.replace(""","""						,""""""    )    )
          return float(snake_case_    )
      except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError("""Unable to convert value to float"""    )
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if answer is None:
          return None
      try:
          UpperCamelCase				:   Optional[Any]          =							convert_to_float(snake_case_    )
          if isinstance(snake_case_						,snake_case_    ) and math.isnan(snake_case_    ):
              return None
          return value
      except ValueError:
          return answer.lower()
  
  
  __A :		List[str]      							=    {
      '''text''': lambda x: x,
      '''real''': convert_to_float,
  }
  class 							lowerCamelCase		( enum.Enum				):
     lowercase       :  str      =				0
     lowercase       :  Tuple      =				1
     lowercase       :  str      =				2
     lowercase       :  Optional[int]      =				3
     lowercase       :  Optional[int]      =				4
     lowercase       :  List[str]      =				5
  
  
  
  
  
  
  
  class 							lowerCamelCase		( enum.Enum				):
     lowercase       :  List[Any]      =				0
     lowercase       :  List[Any]      =				1
     lowercase       :  Optional[Any]      =				2
  
  
  
  
  
  
  
  @dataclasses.dataclass
  class 							lowerCamelCase		:
     lowercase       :  Text
     lowercase       :  _Operator
     lowercase       :  Any
  
  
  
  
  
  
  
  __A :		Optional[int]      							=    re.compile(R'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)
  def 					A_			(		snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      return list(_TOKENIZER.findall(x.lower()    )    )
  def 					A_			(		snake_case_     :       Optional[int]						,snake_case_     :       Any						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if operator == _Operator.EQUALS:
          return src == tgt
      elif operator == _Operator.GREATER:
          return src > tgt
      elif operator == _Operator.LESSER:
          return src < tgt
      raise ValueError(f'Unknown operator: {operator}'    )
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       Union[str, Any]						,snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							table["""types"""]
      return _TYPE_CONVERTER[types[column]](snake_case_    )
  def 					A_			(		snake_case_     :       Any    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      return isinstance(snake_case_						,snake_case_    )
  def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       List[str]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      for cond in conditions:
          UpperCamelCase				:   Optional[int]          =							row[cond.column]
  
          UpperCamelCase				:   Tuple          =							_parse_value(snake_case_						,cond.column						,cond.cmp_value    )
  
          if _is_string(snake_case_    ) and _is_string(snake_case_    ):
              UpperCamelCase				:   str          =							_normalize_for_match(snake_case_    )
              UpperCamelCase				:   List[str]          =							_normalize_for_match(snake_case_    )
  
          if not isinstance(snake_case_						,type(snake_case_    )    ):
              raise ValueError("""Type difference {} != {}""".format(type(snake_case_    )						,type(snake_case_    )    )    )
  
          if not _compare(cond.operator						,snake_case_						,snake_case_    ):
              return False
      return True
  def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Any						,snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if not answer_coordinates:
          if aggregation_op == _Aggregation.COUNT:
              return 0.0
          else:
              return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
      if aggregation_op == _Aggregation.COUNT:
          return float(len(snake_case_    )    )
  
      # If we have just one answer, if float returns it or try a conversion.
      UpperCamelCase				:   Dict          =							[table["""rows"""][i][j] for (i, j) in answer_coordinates]
      if len(snake_case_    ) == 1:
          try:
              return convert_to_float(values[0]    )
          except ValueError as e:
              if aggregation_op != _Aggregation.NONE:
                  raise e
  
      if aggregation_op == _Aggregation.NONE:
          return None
  
      # Other aggregation only support numeric values. Bail out if we have strings.
      if not all((isinstance(snake_case_						,(int, float)    ) for v in values)    ):
          return None
  
      if aggregation_op == _Aggregation.SUM:
          return float(sum(snake_case_    )    )
      elif aggregation_op == _Aggregation.AVERAGE:
          return sum(snake_case_    ) / len(snake_case_    )
      else:
          raise ValueError(f'Unknown aggregation: {aggregation_op}'    )
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   str          =							sql_query["""agg"""]
      if aggregation_op_index >= 3:
          UpperCamelCase				:   int          =							_Aggregation(snake_case_    )
      else:
          UpperCamelCase				:   Dict          =							_Aggregation.NONE
  
      UpperCamelCase				:   Dict          =							sql_query["""sel"""]
      UpperCamelCase				:   int          =							[
          _Condition(snake_case_						,_Operator(snake_case_    )						,snake_case_    )
          for column, operator, cmp_value in zip(
              sql_query["""conds"""]["""column_index"""]						,sql_query["""conds"""]["""operator_index"""]						,sql_query["""conds"""]["""condition"""]    )
      ]
  
      UpperCamelCase				:   List[str]          =							[]
      for row in range(len(table["""rows"""]    )    ):
          if _respect_conditions(snake_case_						,table["""rows"""][row]						,snake_case_    ):
              indices.append((row, target_column)    )
  
      if not indices:
          return [], aggregation_op
  
      if len(snake_case_    ) == 1:
          return indices, aggregation_op
  
      # Parsing of MIN/MAX.
      if aggregation_op_index in (1, 2):
          UpperCamelCase				:   Union[str, Any]          =							{2: min, 1: max}
          UpperCamelCase				:   List[str]          =							[(table["""rows"""][i][j], index) for index, (i, j) in enumerate(snake_case_    )]
          UpperCamelCase				:   Optional[int]          =							functools.reduce(operators[sql_query["""agg"""]]						,snake_case_    )
  
          UpperCamelCase				:   Optional[Any]          =							[indices[reduced[1]]]
          return ret, _Aggregation.NONE
  
      return indices, aggregation_op
  def 					A_			(		snake_case_     :       List[Any]						,snake_case_     :       Any						,snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if float_answer is not None:
          return [str(snake_case_    )]
      return [str(table["""real_rows"""][r][c]    ) for r, c in answer_coordinates]
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							_get_answer_coordinates(snake_case_						,snake_case_    )
      UpperCamelCase				:   Dict          =							_get_float_answer(snake_case_						,snake_case_						,snake_case_    )
      UpperCamelCase				:   Tuple          =							_get_answer_text(snake_case_						,snake_case_						,snake_case_    )
      # keep the original data the same with TaPas
      if len(snake_case_    ) == 0:
          UpperCamelCase				:   Optional[int]          =							[EMPTY_ANSWER]
      return answer_text
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
  else:
    __A :		Tuple      							=    None
  
  
  __A :		Optional[Any]      							=    logging.get_logger(__name__)
  
  __A :		Optional[Any]      							=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __A :		Optional[Any]      							=    {
      '''vocab_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
          ),
      },
      '''tokenizer_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
          ),
      },
  }
  
  __A :		List[Any]      							=    {
      '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
  }
  
  # fmt: off
  __A :		Dict      							=    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				VOCAB_FILES_NAMES
     lowercase       :  Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  List[Any]      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  Tuple      =				['input_ids', 'attention_mask']
     lowercase       :  Optional[int]      =				MBartaaTokenizer
  
     lowercase       :  List[int]      =				[]
     lowercase       :  List[int]      =				[]
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="<s>"    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="<mask>"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase				:   Optional[int]          =							AddedToken(SCREAMING_SNAKE_CASE_    ,      lstrip=SCREAMING_SNAKE_CASE_    ,      rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) else mask_token
  
         UpperCamelCase				:   Dict          =							kwargs.get("""additional_special_tokens"""    ,      [] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]
  
         super().__init__(
             SCREAMING_SNAKE_CASE_    ,      src_lang=SCREAMING_SNAKE_CASE_    ,      tgt_lang=SCREAMING_SNAKE_CASE_    ,      tokenizer_file=SCREAMING_SNAKE_CASE_    ,      eos_token=SCREAMING_SNAKE_CASE_    ,      sep_token=SCREAMING_SNAKE_CASE_    ,      cls_token=SCREAMING_SNAKE_CASE_    ,      unk_token=SCREAMING_SNAKE_CASE_    ,      pad_token=SCREAMING_SNAKE_CASE_    ,      mask_token=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   int          =							vocab_file
         UpperCamelCase				:   Optional[Any]          =							False if not self.vocab_file else True
  
         UpperCamelCase				:   List[Any]          =							{
             lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }
  
         UpperCamelCase				:   List[str]          =							src_lang if src_lang is not None else """en_XX"""
         UpperCamelCase				:   Union[str, Any]          =							tgt_lang
         UpperCamelCase				:   Union[str, Any]          =							self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._src_lang
  
  
  
  
  
  
  
     @src_lang.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "en_XX"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "ro_RO"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   str          =							src_lang
         UpperCamelCase				:   Optional[Any]          =							tgt_lang
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_src_lang_special_tokens(self.src_lang )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[Any]          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Optional[int]          =							[self.eos_token_id]
  
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Any          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Any          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Tuple          =							[self.eos_token_id]
  
         UpperCamelCase				:   str          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Dict          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCamelCase				:   Optional[int]          =							src_lang
         UpperCamelCase				:   Optional[int]          =							self(SCREAMING_SNAKE_CASE_    ,      add_special_tokens=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							tgt_lang_id
         return inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer.""" )
  
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   Optional[int]          =							os.path.join(
             SCREAMING_SNAKE_CASE_    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
             copyfile(self.vocab_file    ,      SCREAMING_SNAKE_CASE_ )
  
         return (out_vocab_file,)
  
  
  
  
  