


'''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


lowerCamelCase							:   str  						=	logging.get_logger(__name__)


lowerCamelCase							:   Tuple  						=	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   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      @add_start_docstrings(UpperCamelCase )
      def __call__(self    :    int   ,						UpperCamelCase    :    torch.LongTensor   ,						UpperCamelCase    :    torch.FloatTensor   ,						**UpperCamelCase    :    str ):

             '''simple docstring'''







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






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






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

             '''simple docstring'''







             lowercase__      			= max_length
             lowercase__      			= max_position_embeddings






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

             '''simple docstring'''







             lowercase__      			= input_ids.shape[-1]
             lowercase__      			= 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   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Optional[int]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    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.'''   ,						UpperCamelCase   ,						)
             lowercase__      			= start_length
             lowercase__      			= max_new_tokens
             lowercase__      			= start_length + max_new_tokens






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

             '''simple docstring'''







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






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    List[Any]   ,						UpperCamelCase    :    float   ,						UpperCamelCase    :    Optional[float] = None ):

             '''simple docstring'''







             lowercase__      			= max_time
             lowercase__      			= time.time() if initial_timestamp is None else initial_timestamp






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

             '''simple docstring'''







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






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






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

             '''simple docstring'''







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






      @property
      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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






def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       StoppingCriteriaList:






       """simple docstring"""







       lowercase__      			= stopping_criteria.max_length
       lowercase__      			= 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








'''simple docstring'''

from ....utils import logging


lowerCamelCase							:   Optional[Any]  						=	logging.get_logger(__name__)
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Optional[int]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any]=None   ,						UpperCamelCase    :    int=2048 ):

             '''simple docstring'''







             lowercase__      			= config.__dict__
             lowercase__      			= modal_hidden_size
             if num_labels:
                    lowercase__      			= num_labels




