


from string import ascii_uppercase

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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  len(lowerCAmelCase__   )
   __a					=  0
   while True:
      if x == i:
         __a					=  0
      if len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ):
         break
      key += key[i]
      i += 1
   return key




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  """"""
   __a					=  0
   for letter in message:
      if letter == " ":
         cipher_text += " "
      else:
         __a					=  (dicta[letter] - dicta[key_new[i]]) % 26
         i += 1
         cipher_text += dicta[x]
   return cipher_text




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  """"""
   __a					=  0
   for letter in cipher_text:
      if letter == " ":
         or_txt += " "
      else:
         __a					=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
         i += 1
         or_txt += dicta[x]
   return or_txt




def  a   ():
   __a					=  """THE GERMAN ATTACK"""
   __a					=  """SECRET"""
   __a					=  generate_key(lowerCAmelCase__		,				lowerCAmelCase__   )
   __a					=  cipher_text(lowerCAmelCase__		,				lowerCAmelCase__   )
   print(f'''Encrypted Text = {s}'''   )
   print(f'''Original Text = {original_text(lowerCAmelCase__		,				lowerCAmelCase__   )}'''   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0  ,       """run_glue_deebert.py"""    )
           with patch.object(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )



