



"""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     =			logging.get_logger(__name__)


_UpperCAmelCase     =			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 					a	(  UpperCAmelCase__       ):



  @add_start_docstrings(lowerCAmelCase		)
  def __call__(						self :					int       ,		lowerCAmelCase :					torch.LongTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		**lowerCAmelCase :					Dict		)     ->					bool:


     '''simple docstring'''




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







class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Optional[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Optional[int] = None		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=max_length
     SCREAMING_SNAKE_CASE_: Dict     			=max_position_embeddings





  @add_start_docstrings(lowerCAmelCase		)
  def __call__(						self :					str       ,		lowerCAmelCase :					torch.LongTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		**lowerCAmelCase :					Any		)     ->					bool:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=input_ids.shape[-1]
     SCREAMING_SNAKE_CASE_: int     			=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 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Optional[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int		)     ->					Optional[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       ,		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=start_length
     SCREAMING_SNAKE_CASE_: Optional[int]     			=max_new_tokens
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=start_length + max_new_tokens





  @add_start_docstrings(lowerCAmelCase		)
  def __call__(						self :					Optional[int]       ,		lowerCAmelCase :					torch.LongTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		**lowerCAmelCase :					str		)     ->					bool:


     '''simple docstring'''




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







class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					Optional[float] = None		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=max_time
     SCREAMING_SNAKE_CASE_: Optional[int]     			=time.time() if initial_timestamp is None else initial_timestamp





  @add_start_docstrings(lowerCAmelCase		)
  def __call__(						self :					str       ,		lowerCAmelCase :					torch.LongTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		**lowerCAmelCase :					Dict		)     ->					bool:


     '''simple docstring'''




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







class 					a	(  UpperCAmelCase__       ):



  @add_start_docstrings(lowerCAmelCase		)
  def __call__(						self :					List[str]       ,		lowerCAmelCase :					torch.LongTensor       ,		lowerCAmelCase :					torch.FloatTensor       ,		**lowerCAmelCase :					int		)     ->					bool:


     '''simple docstring'''




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





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


     '''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      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=stopping_criteria.max_length
   SCREAMING_SNAKE_CASE_: Tuple     			=deepcopy(lowercase )
   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"""							,			lowercase )
   elif stopping_max_length is None:
      new_stopping_criteria.append(MaxLengthCriteria(max_length=lowercase ) )
   return new_stopping_criteria








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   return str(lowercase ) == str(lowercase )[::-1]





def      __magic_name__				(							lowercase ):
   return int(lowercase ) + int(str(lowercase )[::-1] )





def      __magic_name__				(							lowercase = 1_0000 ):
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   for num in range(1							,			lowercase ):
      SCREAMING_SNAKE_CASE_: List[Any]     			=0
      SCREAMING_SNAKE_CASE_: int     			=num
      while iterations < 50:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=sum_reverse(lowercase )
         iterations += 1
         if is_palindrome(lowercase ):
            break
      else:
         lychrel_nums.append(lowercase )
   return len(lowercase )


if __name__ == "__main__":
   print(f"""{solution() = }""")



