




import string

import numpy

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	int					):


      '''simple docstring'''



      return b if a == 0 else greatest_common_divisor(b % a     ,							lowercase					)







class   A:



 '''simple docstring'''


 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 :  Optional[int]						,							A_ :  numpy.ndarray  )       ->     None:

       """simple docstring"""






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

 def     a__			(    self :  Dict						,							A_ :  str  )       ->     int:

       """simple docstring"""






       return self.key_string.index(A_  )

 def     a__			(    self :  str						,							A_ :  int  )       ->     str:

       """simple docstring"""






       return self.key_string[round(A_  )]

 def     a__			(    self :  List[Any]  )       ->     None:

       """simple docstring"""






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

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

       lowerCamelCase_							=   len(self.key_string  )
       if greatest_common_divisor(A_						,							len(self.key_string  )  ) != 1:
             lowerCamelCase_							=   (
                 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     a__			(    self :  str						,							A_ :  str  )       ->     str:

       """simple docstring"""






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

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

       return "".join(A_  )

 def     a__			(    self :  Any						,							A_ :  str  )       ->     str:

       """simple docstring"""






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

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

       return encrypted

 def     a__			(    self :  Any  )       ->     numpy.ndarray:

       """simple docstring"""






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

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

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

       return self.to_int(self.modulus(A_  )  )





 def     a__			(    self :  Union[str, Any]						,							A_ :  str  )       ->     str:

       """simple docstring"""






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

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

       return decrypted

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   int(input('Enter the order of the encryption key: '					)					)
      lowerCamelCase_							=   []

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

      lowerCamelCase_							=   HillCipher(numpy.array(lowercase					)					)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()









import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase    :		int			   =       logging.getLogger()

lowerCamelCase    :		Tuple			   =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  List[str]						,							A_ :  Optional[Any]  )       ->     int:

       """simple docstring"""






       os.makedirs(A_						,							exist_ok=A_  )
       lowerCamelCase_							=   {'source': 'What is love ?', 'target': 'life'}
       lowerCamelCase_							=   {'train': 12, 'val': 2, 'test': 2}
       for split in ["train", "test", "val"]:
             for field in ["source", "target"]:
                   lowerCamelCase_							=   '\n'.join([contents[field]] * n_lines[split]  )
                   with open(os.path.join(A_						,							f"""{split}.{field}"""  )						,							'w'  ) as f:
                         f.write(A_  )

 def     a__			(    self :  Optional[Any]						,							A_ :  int						,							A_ :  str = "pytorch"  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.get_auto_remove_tmp_dir()
       lowerCamelCase_							=   os.path.join(A_						,							'output'  )
       lowerCamelCase_							=   os.path.join(A_						,							'data'  )
       self._create_dummy_data(data_dir=A_  )

       lowerCamelCase_							=   f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

       if gpus > 0:
             testargs.append(f"""--gpus={gpus}"""  )
             if is_apex_available():
                   testargs.append('--fp16'  )
       else:
             testargs.append('--gpus=0'  )
             testargs.append('--distributed_backend=ddp_cpu'  )
             testargs.append('--num_processes=2'  )

       lowerCamelCase_							=   [sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
       execute_subprocess_async(A_						,							env=self.get_env()  )

       lowerCamelCase_							=   os.path.join(A_						,							'metrics.json'  )
       with open(A_  ) as f:
             lowerCamelCase_							=   json.load(A_  )
       return result

 @require_torch_gpu
 def     a__			(    self :  List[Any]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self._run_finetune(gpus=1  )
       self.assertGreaterEqual(result['test'][0]['test_avg_em']						,							0.2  )

 @require_torch_multi_gpu
 def     a__			(    self :  List[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   self._run_finetune(gpus=2  )
       self.assertGreaterEqual(result['test'][0]['test_avg_em']						,							0.2  )

 @require_torch_gpu
 @require_ray
 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self._run_finetune(gpus=1						,							distributed_retriever='ray'  )
       self.assertGreaterEqual(result['test'][0]['test_avg_em']						,							0.2  )





 @require_torch_multi_gpu
 @require_ray
 def     a__			(    self :  Dict  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self._run_finetune(gpus=1						,							distributed_retriever='ray'  )
       self.assertGreaterEqual(result['test'][0]['test_avg_em']						,							0.2  )



