
"""simple docstring"""






def       A_  (				__lowercase	):
 UpperCamelCase_     :					str   	=current_set.copy()
 for row_index, row in enumerate(__lowercase	):
  UpperCamelCase_     :					Any   	=row[0]
  for column_index, column in enumerate(__lowercase	):
   if magnitude == 0:
    UpperCamelCase_     :					int   	=column
    continue
   UpperCamelCase_     :					Optional[int]   	=column / magnitude
    # Subtract to cancel term
 UpperCamelCase_     :					Optional[int]   	=current_set[0]
 UpperCamelCase_     :					List[Any]   	=[first_row]
 UpperCamelCase_     :					Union[str, Any]   	=current_set[1::]
 for row in current_set:
  UpperCamelCase_     :					Optional[int]   	=[]
  # If first term is 0, it is already in form we want, so we preserve it
  if row[0] == 0:
   final_set.append(__lowercase	)
   continue
  for column_index in range(len(__lowercase	)	):
   temp_row.append(first_row[column_index] - row[column_index]	)
  final_set.append(__lowercase	)
 # Create next recursion iteration set
 if len(final_set[0]	) != 3:
  UpperCamelCase_     :					Dict   	=final_set[0]
  UpperCamelCase_     :					Optional[Any]   	=[]
  UpperCamelCase_     :					Dict   	=[]
  for row in final_set[1::]:
   current_first_column.append(row[0]	)
   next_iteration.append(row[1::]	)
  UpperCamelCase_     :					Optional[int]   	=simplify(__lowercase	)
  for i in range(len(__lowercase	)	):
   resultant[i].insert(0						,	current_first_column[i]	)
  resultant.insert(0						,	__lowercase	)
  UpperCamelCase_     :					Any   	=resultant
 return final_set




def       A_  (				__lowercase	):
 if len(__lowercase	) == 0:
  raise IndexError('solve_simultaneous() requires n lists of length n+1'	)
 UpperCamelCase_     :					Union[str, Any]   	=len(__lowercase	) + 1
 if any(len(__lowercase	) != _length for item in equations	):
  raise IndexError('solve_simultaneous() requires n lists of length n+1'	)
 for row in equations:
  if any(not isinstance(__lowercase						,	(int, float)	) for column in row	):
   raise ValueError('solve_simultaneous() requires lists of integers'	)
 if len(__lowercase	) == 1:
  return [equations[0][-1] / equations[0][0]]
 UpperCamelCase_     :					Optional[Any]   	=equations.copy()
 if any(0 in row for row in data_set	):
  UpperCamelCase_     :					int   	=data_set.copy()
  UpperCamelCase_     :					List[Any]   	=[]
  for row_index, row in enumerate(__lowercase	):
   if 0 not in row:
    UpperCamelCase_     :					Tuple   	=data_set.pop(__lowercase	)
    break
  if not full_row:
   raise ValueError('solve_simultaneous() requires at least 1 full equation'	)
  data_set.insert(0						,	__lowercase	)
 UpperCamelCase_     :					List[str]   	=data_set.copy()
 UpperCamelCase_     :					Tuple   	=simplify(__lowercase	)
 UpperCamelCase_     :					List[str]   	=simplified[::-1]
 UpperCamelCase_     :					list   	=[]
 for row in simplified:
  UpperCamelCase_     :					str   	=row[-1]
  if not solutions:
   if row[-2] == 0:
    solutions.append(0	)
    continue
   solutions.append(current_solution / row[-2]	)
   continue
  UpperCamelCase_     :					str   	=row.copy()[: len(__lowercase	) - 1 :]
  while temp_row[0] == 0:
   temp_row.pop(0	)
  if len(__lowercase	) == 0:
   solutions.append(0	)
   continue
  UpperCamelCase_     :					List[Any]   	=temp_row[1::]
  UpperCamelCase_     :					Tuple   	=temp_row[::-1]
  for column_index, column in enumerate(__lowercase	):
   current_solution -= column * solutions[column_index]
  solutions.append(__lowercase	)
 UpperCamelCase_     :					str   	=[]
 for item in solutions:
  final.append(float(round(__lowercase						,	5	)	)	)
 return final[::-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 __SCREAMING_SNAKE_CASE							=     [
     [2, 1, 1, 1, 1, 4],
     [1, 2, 1, 1, 1, 5],
     [1, 1, 2, 1, 1, 6],
     [1, 1, 1, 2, 1, 7],
     [1, 1, 1, 1, 2, 8],
 ]
 print(solve_simultaneous(eq))
 print(solve_simultaneous([[4, 2]]))


"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE							=     {
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

__SCREAMING_SNAKE_CASE							=     {
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


__SCREAMING_SNAKE_CASE							=     {
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      VOCAB_FILES_NAMES
     UpperCAmelCase__         =      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__         =      PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__         =      RoFormerTokenizer



     def __init__(							self :Tuple      ,  _lowerCamelCase :Dict=None      ,  _lowerCamelCase :Dict=None      ,  _lowerCamelCase :List[Any]=True      ,  _lowerCamelCase :Dict="[UNK]"      ,  _lowerCamelCase :List[str]="[SEP]"      ,  _lowerCamelCase :str="[PAD]"      ,  _lowerCamelCase :Optional[Any]="[CLS]"      ,  _lowerCamelCase :Optional[int]="[MASK]"      ,  _lowerCamelCase :str=True      ,  _lowerCamelCase :Tuple=None      ,  **_lowerCamelCase :Any      ,  ):







      '''simple docstring'''



      super().__init__(
          _lowerCamelCase      ,  tokenizer_file=_lowerCamelCase      ,  do_lower_case=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  tokenize_chinese_chars=_lowerCamelCase      ,  strip_accents=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

      UpperCamelCase_     :					Any   	=json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
      if (
          pre_tok_state.get('lowercase'      ,  _lowerCamelCase   ) != do_lower_case
          or pre_tok_state.get('strip_accents'      ,  _lowerCamelCase   ) != strip_accents
      ):
       UpperCamelCase_     :					Optional[Any]   	=getattr(_lowerCamelCase      ,  pre_tok_state.pop('type'   )   )
       UpperCamelCase_     :					Tuple   	=do_lower_case
       UpperCamelCase_     :					Union[str, Any]   	=strip_accents
       UpperCamelCase_     :					Union[str, Any]   	=pre_tok_class(**_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=do_lower_case



     def __getstate__(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=self.__dict__.copy()
      UpperCamelCase_     :					Union[str, Any]   	=BertPreTokenizer()
      return state



     def __setstate__(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=d
      UpperCamelCase_     :					Optional[int]   	=self.__dict__['_tokenizer'].get_vocab()
      UpperCamelCase_     :					Any   	=PreTokenizer.custom(JiebaPreTokenizer(_lowerCamelCase   )   )



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :Any=None   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

      return output



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					List[Any]   	=[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   ) * [0] + len(token_ids_a + sep   ) * [1]



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self._tokenizer.model.save(_lowerCamelCase      ,  name=_lowerCamelCase   )
      return tuple(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :int=False      ,  **_lowerCamelCase :Optional[int]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=BertPreTokenizer()
      return super().save_pretrained(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  **_lowerCamelCase   )
