

"""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'\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      __lowercase			(    __lowerCamelCase					):

  @add_start_docstrings(A    )
  def __call__(  self		:			List[Any]					,A		:			torch.LongTensor					,A		:			torch.FloatTensor					,**A		:			Tuple    ):






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







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			int					,A		:			int					,A		:			Optional[int] = None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					max_length
   UpperCAmelCase__					:  Dict					   =					max_position_embeddings




  @add_start_docstrings(A    )
  def __call__(  self		:			Optional[Any]					,A		:			torch.LongTensor					,A		:			torch.FloatTensor					,**A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					input_ids.shape[-1]
   UpperCAmelCase__					:  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      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			Dict					,A		:			int					,A		:			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."""					,A					,)
   UpperCAmelCase__					:  Union[str, Any]					   =					start_length
   UpperCAmelCase__					:  Optional[int]					   =					max_new_tokens
   UpperCAmelCase__					:  Dict					   =					start_length + max_new_tokens




  @add_start_docstrings(A    )
  def __call__(  self		:			Dict					,A		:			torch.LongTensor					,A		:			torch.FloatTensor					,**A		:			int    ):






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







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			int					,A		:			float					,A		:			Optional[float] = None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					max_time
   UpperCAmelCase__					:  Optional[int]					   =					time.time() if initial_timestamp is None else initial_timestamp




  @add_start_docstrings(A    )
  def __call__(  self		:			Dict					,A		:			torch.LongTensor					,A		:			torch.FloatTensor					,**A		:			List[Any]    ):






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







class      __lowercase			(    __lowerCamelCase					):

  @add_start_docstrings(A    )
  def __call__(  self		:			List[str]					,A		:			torch.LongTensor					,A		:			torch.FloatTensor					,**A		:			Tuple    ):






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




  @property
  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   for stopping_criterium in self:
    if isinstance(A					,A    ):
     return stopping_criterium.max_length
    elif isinstance(A					,A    ):
     return stopping_criterium.max_length
   return None







def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Any					   =					stopping_criteria.max_length
 UpperCAmelCase__					:  str					   =					deepcopy(__UpperCamelCase      )
 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"""							,    __UpperCamelCase      )
 elif stopping_max_length is None:
  new_stopping_criteria.append(MaxLengthCriteria(max_length=__UpperCamelCase      )      )
 return new_stopping_criteria








"""simple docstring"""




from __future__ import annotations
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					list(range(len(__UpperCamelCase      )      )      )
 UpperCAmelCase__					:  Union[str, Any]					   =					[v / w for v, w in zip(__UpperCamelCase							,    __UpperCamelCase      )]
 index.sort(key=lambda __UpperCamelCase      : ratio[i]							,    reverse=__UpperCamelCase      )

 UpperCAmelCase__					:  float					   =					0
 UpperCAmelCase__					:  list[float]					   =					[0] * len(__UpperCamelCase      )
 for i in index:
  if weight[i] <= capacity:
   UpperCAmelCase__					:  Optional[Any]					   =					1
   max_value += value[i]
   capacity -= weight[i]
  else:
   UpperCAmelCase__					:  Union[str, Any]					   =					capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
      import doctest

      doctest.testmod()





