




'''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


UpperCAmelCase_							:	int	     =		logging.get_logger(__name__)


UpperCAmelCase_							:	List[str]	     =		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      lowerCAmelCase (			__lowerCAmelCase):


       @add_start_docstrings(__SCREAMING_SNAKE_CASE	)
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	bool:



            '''simple docstring'''




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





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	max_length
            __snake_case            =	max_position_embeddings




       @add_start_docstrings(__SCREAMING_SNAKE_CASE	)
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	bool:



            '''simple docstring'''




            __snake_case            =	input_ids.shape[-1]
            __snake_case            =	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      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''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.'''     ,	__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	start_length
            __snake_case            =	max_new_tokens
            __snake_case            =	start_length + max_new_tokens




       @add_start_docstrings(__SCREAMING_SNAKE_CASE	)
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	bool:



            '''simple docstring'''




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





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	max_time
            __snake_case            =	time.time() if initial_timestamp is None else initial_timestamp




       @add_start_docstrings(__SCREAMING_SNAKE_CASE	)
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	bool:



            '''simple docstring'''




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





class      lowerCAmelCase (			__lowerCAmelCase):


       @add_start_docstrings(__SCREAMING_SNAKE_CASE	)
       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	bool:



            '''simple docstring'''




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




       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




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







def       _UpperCamelCase			(_lowerCamelCase				:  StoppingCriteriaList ,						_lowerCamelCase				:  int						)-> StoppingCriteriaList:




     '''simple docstring'''





     __snake_case            =	stopping_criteria.max_length
     __snake_case            =	deepcopy(_lowerCamelCase						)
     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''' ,						_lowerCamelCase						)
     elif stopping_max_length is None:
          new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCamelCase						)						)
     return new_stopping_criteria












'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






