import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				Dict	,       lowercase:				Union[str, Any]	,       lowercase:				Optional[Any]		)     ->					Union[str, Any]:







 '''simple docstring'''
 if isinstance(lowercase	,       lowercase		):
  _UpperCamelCase:  Optional[Any]    						=			np.full((len(lowercase		), sequence_length, 2)	,       lowercase		)
 else:
  _UpperCamelCase:  Optional[int]    						=			np.full((len(lowercase		), sequence_length)	,       lowercase		)

 for i, tensor in enumerate(lowercase		):
  if padding_side == "right":
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  str    						=			tensor[:sequence_length]
   else:
    _UpperCamelCase:  List[Any]    						=			tensor[:sequence_length]
  else:
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  Any    						=			tensor[:sequence_length]
   else:
    _UpperCamelCase:  str    						=			tensor[:sequence_length]

 return out_tensor.tolist()




def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			ord(lowercase		)
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 _UpperCamelCase:  List[str]    						=			unicodedata.category(lowercase		)
 if cat.startswith('''P'''		):
  return True
 return False





@dataclass
class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     PreTrainedTokenizerBase
   lowerCAmelCase   :     Union[bool, str, PaddingStrategy]	      = True
   lowerCAmelCase   :     Optional[int]	      = None
   lowerCAmelCase   :     Optional[int]	      = None
   lowerCAmelCase   :     int	      = -1_0_0
   lowerCAmelCase   :     str	      = "pt"



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	int				):



    """simple docstring"""



    import torch

    _UpperCamelCase:  Any    						=			'''label''' if '''label''' in features[0].keys() else '''labels'''
    _UpperCamelCase:  Optional[int]    						=			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
    _UpperCamelCase:  Optional[int]    						=			self.tokenizer.pad(
        _lowercase		,	padding=self.padding		,	max_length=self.max_length		,	pad_to_multiple_of=self.pad_to_multiple_of		,	return_tensors='''pt''' if labels is None else None		,	)

    if labels is None:
     return batch

    _UpperCamelCase:  str    						=			torch.tensor(batch['''entity_ids''']				).shape[1]
    _UpperCamelCase:  int    						=			self.tokenizer.padding_side
    if padding_side == "right":
     _UpperCamelCase:  str    						=			[
         list(_lowercase				) + [self.label_pad_token_id] * (sequence_length - len(_lowercase				)) for label in labels
     ]
    else:
     _UpperCamelCase:  Dict    						=			[
         [self.label_pad_token_id] * (sequence_length - len(_lowercase				)) + list(_lowercase				) for label in labels
     ]

    _UpperCamelCase:  Optional[int]    						=			[feature['''ner_tags'''] for feature in features]
    _UpperCamelCase:  List[Any]    						=			padding_tensor(_lowercase		,	-1		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			[feature['''original_entity_spans'''] for feature in features]
    _UpperCamelCase:  Tuple    						=			padding_tensor(_lowercase		,	(-1, -1)		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Any    						=			{k: torch.tensor(_lowercase		,	dtype=torch.intaa				) for k, v in batch.items()}

    return batch
def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 _UpperCamelCase:  Dict    						=			set({'''(''', '''[''', '''{'''}		)
 _UpperCamelCase:  int    						=			set({''')''', ''']''', '''}'''}		)
 _UpperCamelCase:  Dict    						=			{'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

 for i in range(len(lowercase		)		):
  if s[i] in open_brackets:
   stack.append(s[i]		)

  elif s[i] in closed_brackets and (
      len(lowercase		) == 0 or (len(lowercase		) > 0 and open_to_closed[stack.pop()] != s[i])
  ):
   return False

 return len(lowercase		) == 0




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			input('''Enter sequence of brackets: '''		)
 if is_balanced(lowercase		):
  print(lowercase	,       '''is balanced'''		)
 else:
  print(lowercase	,       '''is not balanced'''		)


if __name__ == "__main__":
  main()