
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  :					Optional[int]						    =							logging.get_logger(__name__)


lowercase  :					Dict						    =							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		(      lowerCAmelCase     ):
    @add_start_docstrings(snake_case      )
    def __call__(  self		,snake_case		,snake_case		,**snake_case      ):



           '''simple docstring'''







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






class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      max_length
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
    @add_start_docstrings(snake_case      )
    def __call__(  self		,snake_case		,snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      input_ids.shape[-1]
           lowercase							:     Dict       	=      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		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case		,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."""		,snake_case		,)
           lowercase							:     Dict       	=      start_length
           lowercase							:     Any       	=      max_new_tokens
           lowercase							:     List[Any]       	=      start_length + max_new_tokens
    @add_start_docstrings(snake_case      )
    def __call__(  self		,snake_case		,snake_case		,**snake_case      ):



           '''simple docstring'''







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






class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      max_time
           lowercase							:     Optional[Any]       	=      time.time() if initial_timestamp is None else initial_timestamp
    @add_start_docstrings(snake_case      )
    def __call__(  self		,snake_case		,snake_case		,**snake_case      ):



           '''simple docstring'''







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






class __snake_case		(      lowerCAmelCase     ):
    @add_start_docstrings(snake_case      )
    def __call__(  self		,snake_case		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return any(criteria(snake_case		,snake_case      ) for criteria in self      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    StoppingCriteriaList:
       lowercase							:     str       	=      stopping_criteria.max_length
       lowercase							:     int       	=      deepcopy(SCREAMING_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"""					,	SCREAMING_SNAKE_CASE__				)
       elif stopping_max_length is None:
              new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE__				)				)
       return new_stopping_criteria







"""simple docstring"""
def      __lowerCamelCase   (							a_     :		int							)			->      bool:
      if not isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :int						=  f'''Input value of [number={number}] must be an integer'''
            raise TypeError(a_							)
      if number < 0:
            return False
      __SCREAMING_SNAKE_CASE   :Any						=  number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
  import doctest

  doctest.testmod()