'''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


lowercase_     :   Any 				=				logging.get_logger(__name__)


lowercase_     :   Tuple 				=				R'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'






class 		_lowerCamelCase		(   __lowerCAmelCase    ):
    @add_start_docstrings(lowerCAmelCase_   )
    def __call__(  self , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )       ->					bool:
     raise NotImplementedError('''StoppingCriteria needs to be subclassed'''   )






class 		_lowerCamelCase		(   __lowerCAmelCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      max_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings



    @add_start_docstrings(lowerCAmelCase_   )
    def __call__(  self , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )       ->					bool:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      input_ids.shape[-1]
     SCREAMING_SNAKE_CASE__:   List[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 		_lowerCamelCase		(   __lowerCAmelCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     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_ , )
     SCREAMING_SNAKE_CASE__:   List[str]=      start_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_new_tokens
     SCREAMING_SNAKE_CASE__:   Tuple=      start_length + max_new_tokens



    @add_start_docstrings(lowerCAmelCase_   )
    def __call__(  self , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )       ->					bool:
     return input_ids.shape[-1] >= self.max_length






class 		_lowerCamelCase		(   __lowerCAmelCase    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Any=      max_time
     SCREAMING_SNAKE_CASE__:   int=      time.time() if initial_timestamp is None else initial_timestamp



    @add_start_docstrings(lowerCAmelCase_   )
    def __call__(  self , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )       ->					bool:
     return time.time() - self.initial_timestamp > self.max_time






class 		_lowerCamelCase		(   __lowerCAmelCase    ):
    @add_start_docstrings(lowerCAmelCase_   )
    def __call__(  self , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )       ->					bool:
     return any(criteria(lowerCAmelCase_ , lowerCAmelCase_   ) for criteria in self   )



    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     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 			A__      (	snake_case_ :		StoppingCriteriaList       ,				snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      stopping_criteria.max_length
 SCREAMING_SNAKE_CASE__:   str=      deepcopy(snake_case__      )
 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'''       ,				snake_case__      )
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=snake_case__      )      )
 return new_stopping_criteria


from ..utils import DummyObject, requires_backends






class 		_lowerCamelCase		(   metaclass=UpperCamelCase_    ):
    __a  =      ["torch", "scipy"]
    def __init__(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Any:
     requires_backends(self , ['''torch''', '''scipy''']   )
    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					List[str]:
     requires_backends(cls , ['''torch''', '''scipy''']   )



    @classmethod
    def 						UpperCamelCase_				(  cls , *lowerCAmelCase , **lowerCAmelCase   )       ->					Tuple:
     requires_backends(cls , ['''torch''', '''scipy''']   )

