





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


UpperCamelCase_									=logging.get_logger(__name__)


UpperCamelCase_									=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 			_a						(				_lowerCAmelCase    ):






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




										'''simple docstring'''



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




class 			_a						(				_lowerCAmelCase    ):






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




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   max_length
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings






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




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   input_ids.shape[-1]
										_UpperCamelCase      : List[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						(				_lowerCAmelCase    ):






				def __init__(			self		:   Any,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int							)     ->				Optional[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__,			)
										_UpperCamelCase      : Tuple         =   start_length
										_UpperCamelCase      : Union[str, Any]         =   max_new_tokens
										_UpperCamelCase      : Optional[Any]         =   start_length + max_new_tokens






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




										'''simple docstring'''



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




class 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   str,			lowerCAmelCase__		:   float,			lowerCAmelCase__		:   Optional[float] = None							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   max_time
										_UpperCamelCase      : Optional[int]         =   time.time() if initial_timestamp is None else initial_timestamp






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




										'''simple docstring'''



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




class 			_a						(				_lowerCAmelCase    ):






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




										'''simple docstring'''



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






				@property
				def     snake_case     (			self		:   List[Any]							)     ->				Optional[int]:




										'''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_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   stopping_criteria.max_length
						_UpperCamelCase      : Tuple         =   deepcopy(_lowercase       )
						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'''	,						_lowercase       )
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowercase       )       )
						return new_stopping_criteria










"""simple docstring"""




from __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



