

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     : Tuple       =						logging.get_logger(__name__)


__lowercase     : Union[str, Any]       =						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     (				snake_case				):





  '''simple docstring'''



  @add_start_docstrings(SCREAMING_SNAKE_CASE_    )
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      raise NotImplementedError("""StoppingCriteria needs to be subclassed"""    )







class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		max_length
      snake_case							:		Union[str, Any]        =		max_position_embeddings





  @add_start_docstrings(SCREAMING_SNAKE_CASE_    )
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		input_ids.shape[-1]
      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 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''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							:		List[str]        =		start_length
      snake_case							:		str        =		max_new_tokens
      snake_case							:		Union[str, Any]        =		start_length + max_new_tokens





  @add_start_docstrings(SCREAMING_SNAKE_CASE_    )
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ):

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







class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		Any        =		max_time
      snake_case							:		Dict        =		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_    ):

      '''simple docstring'''
      return time.time() - self.initial_timestamp > self.max_time







class 						_A     (				snake_case				):





  '''simple docstring'''



  @add_start_docstrings(SCREAMING_SNAKE_CASE_    )
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return any(criteria(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) for criteria in self    )





  @property
  def    snake_case_       (  self    ):

      '''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       lowercase					(	__A :					StoppingCriteriaList ,    __A :					int		)				->			StoppingCriteriaList:





    '''simple docstring'''




    snake_case							:		List[str]        =		stopping_criteria.max_length
    snake_case							:		int        =		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 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


