
'''simple docstring'''

# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase												=   logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer


   class 		_a     (							SCREAMING_SNAKE_CASE  ):







      """simple docstring"""




      def __init__(       self    ,__SCREAMING_SNAKE_CASE = 101      ):
       SCREAMING_SNAKE_CASE  :  List[Any]								=		length






      def __len__(       self      ):
       return self.length






      def __getitem__(       self    ,__SCREAMING_SNAKE_CASE      ):
       return i



   class 		_a     :







      """simple docstring"""




      def __call__(       self    ,__SCREAMING_SNAKE_CASE      ):
       return {"input_ids": torch.tensor(__SCREAMING_SNAKE_CASE      ), "labels": torch.tensor(__SCREAMING_SNAKE_CASE      )}



   class 		_a     (							nn.Module  ):







      """simple docstring"""




      def __init__(       self      ):
       super().__init__()
       # Add some (unused) params otherwise DDP will complain.
       SCREAMING_SNAKE_CASE  :  Optional[Any]								=		nn.Linear(120    ,80      )






      def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None      ):
       if labels is not None:
        return torch.tensor(0.0    ,device=input_ids.device      ), input_ids
       else:
        return input_ids



def        SCREAMING_SNAKE_CASE_  (	)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		HfArgumentParser((TrainingArguments,)							)
 sys.argv += ["--output_dir", "./examples"]
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		parser.parse_args_into_dataclasses()[0]

 logger.warning(
     f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
     f"""tpu_num_cores: {training_args.tpu_num_cores}"""   ,     )

 # Essentially, what we want to verify in the distributed case is
 # that we get all samples back, in the right order.
 # (this is crucial for prediction for instance)
 for dataset_length in [1001, 256, 15]:
  SCREAMING_SNAKE_CASE  :  str								=		DummyDataset(snake_case_							)

  def compute_metrics(snake_case_				:	EvalPrediction							) -> Dict:
   SCREAMING_SNAKE_CASE  :  Any								=		list(range(len(snake_case_							)							)							)
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
   return {"success": success}

  SCREAMING_SNAKE_CASE  :  Dict								=		Trainer(
      model=DummyModel()   ,     args=snake_case_   ,     data_collator=DummyDataCollator()   ,     eval_dataset=snake_case_   ,     compute_metrics=snake_case_   ,     )
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		trainer.evaluate()
  logger.info(snake_case_							)
  if metrics["eval_success"] is not True:
   logger.error(snake_case_							)
   exit(1							)

  SCREAMING_SNAKE_CASE  :  Optional[int]								=		trainer.predict(snake_case_							)
  logger.info(p.metrics							)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics							)
   exit(1							)

  SCREAMING_SNAKE_CASE  :  Optional[int]								=		2

  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		trainer.evaluate()
  logger.info(snake_case_							)
  if metrics["eval_success"] is not True:
   logger.error(snake_case_							)
   exit(1							)

  SCREAMING_SNAKE_CASE  :  Tuple								=		trainer.predict(snake_case_							)
  logger.info(p.metrics							)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics							)
   exit(1							)

  SCREAMING_SNAKE_CASE  :  int								=		None

 logger.info('🔥 All distributed tests successful'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Tuple							)       ->	Any:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
   main()




'''simple docstring'''

import random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

 print(f"""\n{mode.title()}ion: \n{translated}"""							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		list(snake_case_							)
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit('Error in the key or symbol set.'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		chars_a.find(symbol.upper()							)
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


