





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_    =				logging.get_logger(__name__)


lowerCAmelCase_    =				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_     ):






			@add_start_docstrings(__magic_name__    )
			def __call__(self					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )     ->   Dict:


							'''simple docstring'''



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




class 					__lowerCAmelCase       (			lowercase_     ):






			def __init__(self					,							__magic_name__					,							__magic_name__ = None    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Dict				=  max_length
							snake_case_						:    List[Any]				=  max_position_embeddings






			@add_start_docstrings(__magic_name__    )
			def __call__(self					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Any				=  input_ids.shape[-1]
							snake_case_						:    Tuple				=  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_     ):






			def __init__(self					,							__magic_name__					,							__magic_name__    )     ->   str:


							'''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.'''					,							__magic_name__					,							)
							snake_case_						:    Optional[int]				=  start_length
							snake_case_						:    Tuple				=  max_new_tokens
							snake_case_						:    Any				=  start_length + max_new_tokens






			@add_start_docstrings(__magic_name__    )
			def __call__(self					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



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




class 					__lowerCAmelCase       (			lowercase_     ):






			def __init__(self					,							__magic_name__					,							__magic_name__ = None    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    int				=  max_time
							snake_case_						:    Union[str, Any]				=  time.time() if initial_timestamp is None else initial_timestamp






			@add_start_docstrings(__magic_name__    )
			def __call__(self					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )     ->   List[str]:


							'''simple docstring'''



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




class 					__lowerCAmelCase       (			lowercase_     ):






			@add_start_docstrings(__magic_name__    )
			def __call__(self					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )     ->   Dict:


							'''simple docstring'''



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






			@property
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



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






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						StoppingCriteriaList:






				"""simple docstring"""


				snake_case_						:    Optional[int]				=  stopping_criteria.max_length
				snake_case_						:    Dict				=  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 ....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




