
'''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


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


A__   :		Any 		=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  __A				( _SCREAMING_SNAKE_CASE			):
 @add_start_docstrings(lowerCamelCase       )
 def __call__(   self			:    Optional[Any]				,   lowerCamelCase			:    torch.LongTensor				,   lowerCamelCase			:    torch.FloatTensor				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





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







class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Optional[int] = None       ):



   """simple docstring"""





   __A		:     Any												=		max_length
   __A		:     Any												=		max_position_embeddings






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



   """simple docstring"""





   __A		:     Dict												=		input_ids.shape[-1]
   __A		:     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				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    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				,   )
   __A		:     Optional[Any]												=		start_length
   __A		:     int												=		max_new_tokens
   __A		:     Dict												=		start_length + max_new_tokens






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



   """simple docstring"""





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







class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    str				,   lowerCamelCase			:    float				,   lowerCamelCase			:    Optional[float] = None       ):



   """simple docstring"""





   __A		:     Any												=		max_time
   __A		:     Tuple												=		time.time() if initial_timestamp is None else initial_timestamp






 @add_start_docstrings(lowerCamelCase       )
 def __call__(   self			:    Dict				,   lowerCamelCase			:    torch.LongTensor				,   lowerCamelCase			:    torch.FloatTensor				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





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







class  __A				( _SCREAMING_SNAKE_CASE			):
 @add_start_docstrings(lowerCamelCase       )
 def __call__(   self			:    Optional[int]				,   lowerCamelCase			:    torch.LongTensor				,   lowerCamelCase			:    torch.FloatTensor				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





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






 @property
 def 		lowercase_(   self			:    Dict       ):



   """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        A_     (						__SCREAMING_SNAKE_CASE						:  StoppingCriteriaList			,     __SCREAMING_SNAKE_CASE						:  int )				->       StoppingCriteriaList:





  """simple docstring"""

  __A		:     Tuple												=		stopping_criteria.max_length
  __A		:     Optional[Any]												=		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'''
A__   :		int 		=8.3_1_4_4_6_2  # Unit - J mol-1 K-1







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or volume < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume







def        A_     (						__SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float			,     __SCREAMING_SNAKE_CASE						:  float )				->       float:





  """simple docstring"""

  if moles < 0 or kelvin < 0 or pressure < 0:
    raise ValueError("""Invalid inputs. Enter positive value.""" )
  return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()



