


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      (     _UpperCamelCase   ):


	@add_start_docstrings(lowercase__)
	def __call__(						self							,    lowercase__							,    lowercase__							,    **lowercase__):
							raise NotImplementedError('''StoppingCriteria needs to be subclassed''')






class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__ = None):
							__UpperCAmelCase :       Union[str, Any]  						=  max_length
							__UpperCAmelCase :       Tuple  						=  max_position_embeddings


	@add_start_docstrings(lowercase__)
	def __call__(						self							,    lowercase__							,    lowercase__							,    **lowercase__):
							__UpperCAmelCase :       Any  						=  input_ids.shape[-1]
							__UpperCAmelCase :       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        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__):
							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.'''							,    lowercase__							,    )
							__UpperCAmelCase :       str  						=  start_length
							__UpperCAmelCase :       Optional[Any]  						=  max_new_tokens
							__UpperCAmelCase :       Tuple  						=  start_length + max_new_tokens


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






class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__ = None):
							__UpperCAmelCase :       Optional[int]  						=  max_time
							__UpperCAmelCase :       Any  						=  time.time() if initial_timestamp is None else initial_timestamp


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






class        lowerCamelCase      (     _UpperCamelCase   ):


	@add_start_docstrings(lowercase__)
	def __call__(						self							,    lowercase__							,    lowercase__							,    **lowercase__):
							return any(criteria(lowercase__							,    lowercase__) for criteria in self)


	@property
	def    A(						self):
							for stopping_criterium in self:
													if isinstance(lowercase__							,    lowercase__):
																			return stopping_criterium.max_length
													elif isinstance(lowercase__							,    lowercase__):
																			return stopping_criterium.max_length
							return None






def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    StoppingCriteriaList:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  stopping_criteria.max_length
						__UpperCAmelCase :       Dict  						=  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





from string import ascii_uppercase

lowerCAmelCase							=						{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase							=						dict(enumerate(ascii_uppercase))




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  len(lowercase_			)
						__UpperCAmelCase :       int  						=  0
						while True:
												if x == i:
																		__UpperCAmelCase :       List[str]  						=  0
												if len(lowercase_			) == len(lowercase_			):
																		break
												key += key[i]
												i += 1
						return key




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       str  						=  ''''''
						__UpperCAmelCase :       List[str]  						=  0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__UpperCAmelCase :       Optional[int]  						=  (dicta[letter] - dicta[key_new[i]]) % 26
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  ''''''
						__UpperCAmelCase :       List[str]  						=  0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__UpperCAmelCase :       int  						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
																		i += 1
																		or_txt += dicta[x]
						return or_txt




def 						__SCREAMING_SNAKE_CASE       (					)		->    None:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  '''THE GERMAN ATTACK'''
						__UpperCAmelCase :       List[Any]  						=  '''SECRET'''
						__UpperCAmelCase :       Optional[int]  						=  generate_key(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       List[str]  						=  cipher_text(lowercase_			,					lowercase_			)
						print(f"Encrypted Text = {s}"			)
						print(f"Original Text = {original_text(lowercase_			,					lowercase_			)}"			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()

