





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE      	=		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 SCREAMING_SNAKE_CASE_						(					snake_case_					):






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







							"""simple docstring"""


							raise NotImplementedError('StoppingCriteria needs to be subclassed'	)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






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







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	max_length
							snake_case_		:			Any											=	max_position_embeddings






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







							"""simple docstring"""


							snake_case_		:			int											=	input_ids.shape[-1]
							snake_case_		:			List[str]											=	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 SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Any		,   _A      :      int		,   _A      :      int	)							->    Union[str, 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		,   )
							snake_case_		:			int											=	start_length
							snake_case_		:			Optional[Any]											=	max_new_tokens
							snake_case_		:			Dict											=	start_length + max_new_tokens






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







							"""simple docstring"""


							return input_ids.shape[-1] >= self.max_length
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






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







							"""simple docstring"""


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






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







							"""simple docstring"""


							return time.time() - self.initial_timestamp > self.max_time
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






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







							"""simple docstring"""


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






						@property
						def UpperCAmelCase_				(      self      :      Tuple	)							->    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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Any											=	stopping_criteria.max_length
	snake_case_		:			Optional[Any]											=	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








import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return (2 / (1 + np.exp(-2 * vector   ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

