


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


snake_case_						:			Optional[Any]													=							logging.get_logger(__name__)


snake_case_						:			List[str]													=							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__							(		UpperCamelCase__  ):




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


										"""simple docstring"""





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

class 						A__							(		UpperCamelCase__  ):




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =max_length
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =input_ids.shape[-1]
										_SCREAMING_SNAKE_CASE			  =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__							(		UpperCamelCase__  ):




						def __init__(       self					:      str				,    _a					:      int				,    _a					:      int  )							->     Dict:


										"""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				,    )
										_SCREAMING_SNAKE_CASE			  =start_length
										_SCREAMING_SNAKE_CASE			  =max_new_tokens
										_SCREAMING_SNAKE_CASE			  =start_length + max_new_tokens




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


										"""simple docstring"""





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

class 						A__							(		UpperCamelCase__  ):




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =max_time
										_SCREAMING_SNAKE_CASE			  =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					:      Optional[int]  )							->     bool:


										"""simple docstring"""





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

class 						A__							(		UpperCamelCase__  ):




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


										"""simple docstring"""





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




						@property
						def 							__UpperCamelCase				(       self					:      Dict  )							->     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 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =stopping_criteria.max_length
				_SCREAMING_SNAKE_CASE			  =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 string

import numpy







def 						lowerCamelCase(   a__	,a__):
				return b if a == 0 else greatest_common_divisor(b % a	,a__)

class 						A__							:
						UpperCAmelCase             =						string.ascii_uppercase + string.digits
						# This cipher takes alphanumerics into account
						# i.e. a total of 36 characters

						# take x and return x % len(key_string)
						UpperCAmelCase             =						numpy.vectorize(lambda UpperCamelCase__  : x % 36  )

						UpperCAmelCase             =						numpy.vectorize(UpperCamelCase__  )




						def __init__(       self					:      int				,    _a					:      numpy.ndarray  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.modulus(_a  )  # mod36 calc's on the encrypt key
										self.check_determinant()  # validate the determinant of the encryption key
										_SCREAMING_SNAKE_CASE			  =encrypt_key.shape[0]




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      str  )							->     int:


										"""simple docstring"""





										return self.key_string.index(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      int  )							->     str:


										"""simple docstring"""





										return self.key_string[round(_a  )]




						def 							__UpperCamelCase				(       self					:      int  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =round(numpy.linalg.det(self.encrypt_key  )  )

										if det < 0:
														_SCREAMING_SNAKE_CASE			  =det % len(self.key_string  )

										_SCREAMING_SNAKE_CASE			  =len(self.key_string  )
										if greatest_common_divisor(_a				,    len(self.key_string  )  ) != 1:
														_SCREAMING_SNAKE_CASE			  =(
														    f"determinant modular {req_l} of encryption key({det}) "
														    f"is not co prime w.r.t {req_l}.\nTry another key."
														)
														raise ValueError(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[char for char in text.upper() if char in self.key_string]

										_SCREAMING_SNAKE_CASE			  =chars[-1]
										while len(_a  ) % self.break_key != 0:
														chars.append(_a  )

										return "".join(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.process_text(text.upper()  )
										_SCREAMING_SNAKE_CASE			  =''''''

										for i in range(0				,    len(_a  ) - self.break_key + 1				,    self.break_key  ):
														_SCREAMING_SNAKE_CASE			  =text[i : i + self.break_key]
														_SCREAMING_SNAKE_CASE			  =[self.replace_letters(_a  ) for char in batch]
														_SCREAMING_SNAKE_CASE			  =numpy.array([vec]  ).T
														_SCREAMING_SNAKE_CASE			  =self.modulus(self.encrypt_key.dot(_a  )  ).T.tolist()[
														    0
														]
														_SCREAMING_SNAKE_CASE			  =''''''.join(
														    self.replace_digits(_a  ) for num in batch_encrypted  )
														encrypted += encrypted_batch

										return encrypted




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     numpy.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =round(numpy.linalg.det(self.encrypt_key  )  )

										if det < 0:
														_SCREAMING_SNAKE_CASE			  =det % len(self.key_string  )
										_SCREAMING_SNAKE_CASE			  =None
										for i in range(len(self.key_string  )  ):
														if (det * i) % len(self.key_string  ) == 1:
																		_SCREAMING_SNAKE_CASE			  =i
																		break

										_SCREAMING_SNAKE_CASE			  =(
										    det_inv
										    * numpy.linalg.det(self.encrypt_key  )
										    * numpy.linalg.inv(self.encrypt_key  )
										)

										return self.to_int(self.modulus(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.make_decrypt_key()
										_SCREAMING_SNAKE_CASE			  =self.process_text(text.upper()  )
										_SCREAMING_SNAKE_CASE			  =''''''

										for i in range(0				,    len(_a  ) - self.break_key + 1				,    self.break_key  ):
														_SCREAMING_SNAKE_CASE			  =text[i : i + self.break_key]
														_SCREAMING_SNAKE_CASE			  =[self.replace_letters(_a  ) for char in batch]
														_SCREAMING_SNAKE_CASE			  =numpy.array([vec]  ).T
														_SCREAMING_SNAKE_CASE			  =self.modulus(decrypt_key.dot(_a  )  ).T.tolist()[0]
														_SCREAMING_SNAKE_CASE			  =''''''.join(
														    self.replace_digits(_a  ) for num in batch_decrypted  )
														decrypted += decrypted_batch

										return decrypted







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =int(input('''Enter the order of the encryption key: '''))
				_SCREAMING_SNAKE_CASE			  =[]

				print('''Enter each row of the encryption key with space separated integers''')
				for _ in range(a__):
								_SCREAMING_SNAKE_CASE			  =[int(a__) for x in input().split()]
								hill_matrix.append(a__)

				_SCREAMING_SNAKE_CASE			  =HillCipher(numpy.array(a__))

				print('''Would you like to encrypt or decrypt some text? (1 or 2)''')
				_SCREAMING_SNAKE_CASE			  =input('''\n1. Encrypt\n2. Decrypt\n''')
				if option == "1":
								_SCREAMING_SNAKE_CASE			  =input('''What text would you like to encrypt?: ''')
								print('''Your encrypted text is:''')
								print(hc.encrypt(a__))
				elif option == "2":
								_SCREAMING_SNAKE_CASE			  =input('''What text would you like to decrypt?: ''')
								print('''Your decrypted text is:''')
								print(hc.decrypt(a__))


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()