



"""simple docstring"""






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


lowerCAmelCase__              =							logging.get_logger(__name__)


lowerCAmelCase__              =							R'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''






class 			__snake_case			(      _lowercase):




  @add_start_docstrings(__lowerCAmelCase						)
  def __call__(					self	:		List[str]		,   __lowerCAmelCase	:		torch.LongTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   **__lowerCAmelCase	:		int						):






         """simple docstring"""






         raise NotImplementedError('''StoppingCriteria needs to be subclassed'''						)






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		Optional[int]		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		Optional[int] = None						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					max_length
         _lowerCamelCase		:							int            =					max_position_embeddings




  @add_start_docstrings(__lowerCAmelCase						)
  def __call__(					self	:		Tuple		,   __lowerCAmelCase	:		torch.LongTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   **__lowerCAmelCase	:		List[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					input_ids.shape[-1]
         _lowerCamelCase		:							Union[str, Any]            =					cur_len >= self.max_length
         if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
                logger.warning_once(
                    '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
                    f'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
                    '''exceptions, performance degradation, or nothing at all.'''						)
         return is_done






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		List[Any]		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         warnings.warn(
             '''The class `MaxNewTokensCriteria` is deprecated. '''
             f'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
             '''with `max_length = start_length + max_new_tokens` instead.'''		,   __lowerCAmelCase		,   )
         _lowerCamelCase		:							Dict            =					start_length
         _lowerCamelCase		:							str            =					max_new_tokens
         _lowerCamelCase		:							str            =					start_length + max_new_tokens




  @add_start_docstrings(__lowerCAmelCase						)
  def __call__(					self	:		str		,   __lowerCAmelCase	:		torch.LongTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   **__lowerCAmelCase	:		Any						):






         """simple docstring"""






         return input_ids.shape[-1] >= self.max_length






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		float		,   __lowerCAmelCase	:		Optional[float] = None						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					max_time
         _lowerCamelCase		:							Any            =					time.time() if initial_timestamp is None else initial_timestamp




  @add_start_docstrings(__lowerCAmelCase						)
  def __call__(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		torch.LongTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   **__lowerCAmelCase	:		Dict						):






         """simple docstring"""






         return time.time() - self.initial_timestamp > self.max_time






class 			__snake_case			(      _lowercase):




  @add_start_docstrings(__lowerCAmelCase						)
  def __call__(					self	:		List[str]		,   __lowerCAmelCase	:		torch.LongTensor		,   __lowerCAmelCase	:		torch.FloatTensor		,   **__lowerCAmelCase	:		List[str]						):






         """simple docstring"""






         return any(criteria(__lowerCAmelCase		,   __lowerCAmelCase						) for criteria in self						)




  @property
  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         for stopping_criterium in self:
                if isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                       return stopping_criterium.max_length
                elif isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                       return stopping_criterium.max_length
         return None






def 			snake_case_    ( A_   :						StoppingCriteriaList,							A_   :						int		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					stopping_criteria.max_length
       _lowerCamelCase		:							str            =					deepcopy(A_		)
       if stopping_max_length is not None and stopping_max_length != max_length:
              warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter''',							A_		)
       elif stopping_max_length is None:
              new_stopping_criteria.append(MaxLengthCriteria(max_length=A_		)		)
       return new_stopping_criteria



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()