






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       :       Optional[Any]        =				r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					@add_start_docstrings(__a							)
					def __call__(     self :		Optional[Any]						,   __a :		torch.LongTensor						,   __a :		torch.FloatTensor						,   **__a :		Tuple							)	->     bool:


												"""simple docstring"""

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







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					def __init__(     self :		List[Any]						,   __a :		int						,   __a :		Optional[int] = None							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   max_length
												__lowercase					:							Tuple			=   max_position_embeddings



					@add_start_docstrings(__a							)
					def __call__(     self :		Tuple						,   __a :		torch.LongTensor						,   __a :		torch.FloatTensor						,   **__a :		Any							)	->     bool:


												"""simple docstring"""

												__lowercase					:							Dict			=   input_ids.shape[-1]
												__lowercase					:							Dict			=   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   (       __a	):

					'''simple docstring'''

					def __init__(     self :		List[str]						,   __a :		int						,   __a :		int							)	->     Any:


												"""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						,   )
												__lowercase					:							List[str]			=   start_length
												__lowercase					:							List[Any]			=   max_new_tokens
												__lowercase					:							List[str]			=   start_length + max_new_tokens



					@add_start_docstrings(__a							)
					def __call__(     self :		List[str]						,   __a :		torch.LongTensor						,   __a :		torch.FloatTensor						,   **__a :		List[Any]							)	->     bool:


												"""simple docstring"""

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







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					def __init__(     self :		Dict						,   __a :		float						,   __a :		Optional[float] = None							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   max_time
												__lowercase					:							List[str]			=   time.time() if initial_timestamp is None else initial_timestamp



					@add_start_docstrings(__a							)
					def __call__(     self :		str						,   __a :		torch.LongTensor						,   __a :		torch.FloatTensor						,   **__a :		Any							)	->     bool:


												"""simple docstring"""

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







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					@add_start_docstrings(__a							)
					def __call__(     self :		Any						,   __a :		torch.LongTensor						,   __a :		torch.FloatTensor						,   **__a :		Tuple							)	->     bool:


												"""simple docstring"""

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



					@property
					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Optional[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 					snake_case_						(   lowerCAmelCase_				:  StoppingCriteriaList				,				lowerCAmelCase_				:  int					):
							__lowercase					:							Tuple			=   stopping_criteria.max_length
							__lowercase					:							int			=   deepcopy(lowerCAmelCase_					)
							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"""				,				lowerCAmelCase_					)
							elif stopping_max_length is None:
														new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCAmelCase_					)					)
							return new_stopping_criteria







from itertools import permutations
def 					snake_case_						(   lowerCAmelCase_				:  tuple					):
							if num[3] % 2 != 0:
														return False

							if (num[2] + num[3] + num[4]) % 3 != 0:
														return False

							if num[5] % 5 != 0:
														return False

							__lowercase					:							Dict			=   [7, 11, 13, 17]
							for i, test in enumerate(lowerCAmelCase_					):
														if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10					):
							return sum(
							    int("""""".join(map(lowerCAmelCase_				,				lowerCAmelCase_					)					)					)
							    for num in permutations(range(lowerCAmelCase_					)					)
							    if is_substring_divisible(lowerCAmelCase_					)					)


if __name__ == "__main__":
	print(f'''{solution() = }''')