


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					  =     logging.get_logger(__name__)


a					  =     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        UpperCamelCase__   (	__magic_name__   ):

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






										'''simple docstring'''






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







class        UpperCamelCase__   (	__magic_name__   ):

					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       :   Union[str, Any]							,					UpperCamelCase__       :   torch.LongTensor							,					UpperCamelCase__       :   torch.FloatTensor							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''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        UpperCamelCase__   (	__magic_name__   ):

					def __init__(     self       :   str							,					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       :   Optional[int]							,					UpperCamelCase__       :   torch.LongTensor							,					UpperCamelCase__       :   torch.FloatTensor							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






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







class        UpperCamelCase__   (	__magic_name__   ):

					def __init__(     self       :   Tuple							,					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[str]							,					UpperCamelCase__       :   torch.LongTensor							,					UpperCamelCase__       :   torch.FloatTensor							,					**UpperCamelCase__       :   List[str]       ):






										'''simple docstring'''






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







class        UpperCamelCase__   (	__magic_name__   ):

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






										'''simple docstring'''






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







					@property
					def   UpperCAmelCase__       (     self       :   int       ):






										'''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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			stopping_criteria.max_length
					lowercase_  							=			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






def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					return " ".join(input_str.split()[::-1]  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()


