





"""simple docstring"""





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'''
    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      (       _lowercase ):


						@add_start_docstrings(__a		)
						def __call__(self   ,							__a   ,							__a   ,							**__a		) ->   bool:
								raise NotImplementedError("StoppingCriteria needs to be subclassed"		)





class        _lowerCamelCase      (       _lowercase ):


						def __init__(self   ,							__a   ,							__a = None		) ->   Any:
								UpperCamelCase   =	max_length
								UpperCamelCase   =	max_position_embeddings




						@add_start_docstrings(__a		)
						def __call__(self   ,							__a   ,							__a   ,							**__a		) ->   bool:
								UpperCamelCase   =	input_ids.shape[-1]
								UpperCamelCase   =	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   ,							__a   ,							__a		) ->   List[Any]:
								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   ,							)
								UpperCamelCase   =	start_length
								UpperCamelCase   =	max_new_tokens
								UpperCamelCase   =	start_length + max_new_tokens




						@add_start_docstrings(__a		)
						def __call__(self   ,							__a   ,							__a   ,							**__a		) ->   bool:
								return input_ids.shape[-1] >= self.max_length





class        _lowerCamelCase      (       _lowercase ):


						def __init__(self   ,							__a   ,							__a = None		) ->   Optional[Any]:
								UpperCamelCase   =	max_time
								UpperCamelCase   =	time.time() if initial_timestamp is None else initial_timestamp




						@add_start_docstrings(__a		)
						def __call__(self   ,							__a   ,							__a   ,							**__a		) ->   bool:
								return time.time() - self.initial_timestamp > self.max_time





class        _lowerCamelCase      (       _lowercase ):


						@add_start_docstrings(__a		)
						def __call__(self   ,							__a   ,							__a   ,							**__a		) ->   bool:
								return any(criteria(__a   ,							__a		) for criteria in self		)




						@property
						def     snake_case_   (self		) ->   Optional[int]:
								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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	stopping_criteria.max_length
		UpperCamelCase   =	deepcopy(_SCREAMING_SNAKE_CASE    )
		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"  ,	_SCREAMING_SNAKE_CASE    )
		elif stopping_max_length is None:
				new_stopping_criteria.append(MaxLengthCriteria(max_length=_SCREAMING_SNAKE_CASE    )    )
		return new_stopping_criteria













"""simple docstring"""





import string


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in range(len(string.ascii_uppercase    )    ):
				UpperCamelCase   =	""
				for symbol in message:
						if symbol in string.ascii_uppercase:
								UpperCamelCase   =	string.ascii_uppercase.find(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	num - key
								if num < 0:
										UpperCamelCase   =	num + len(string.ascii_uppercase    )
								UpperCamelCase   =	translated + string.ascii_uppercase[num]
						else:
								UpperCamelCase   =	translated + symbol
				print(F"Decryption using Key #{key}: {translated}"    )


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	input("Encrypted message: "    )
		UpperCamelCase   =	message.upper()
		decrypt(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






