




'''simple docstring'''







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm


lowercase      :   List[Any]      =       logging.get_logger(__name__)







@dataclass
class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					[
         """no_inference""",
         """no_cuda""",
         """no_tpu""",
         """no_speed""",
         """no_memory""",
         """no_env_print""",
         """no_multi_process""",
     ]







     def __init__(				self							,**__a							)      ->				Optional[int]:
          for deprecated_arg in self.deprecated_args:
               if deprecated_arg in kwargs:
                    snake_case			:      Dict        =			deprecated_arg[3:]
                    setattr(self							,__a							,not kwargs.pop(__a							)							)
                    logger.warning(
                        F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
                        F''' {positive_arg}={kwargs[positive_arg]}'''							)

          snake_case			:      Dict        =			kwargs.pop("""torchscript"""							,self.torchscript							)
          snake_case			:      List[Any]        =			kwargs.pop("""torch_xla_tpu_print_metrics"""							,self.torch_xla_tpu_print_metrics							)
          snake_case			:      Optional[int]        =			kwargs.pop("""fp16_opt_level"""							,self.fpaa_opt_level							)
          super().__init__(**__a							)

     lowerCAmelCase_  :     bool          =					field(default=a__,		metadata={"""help""": """Trace the models using torchscript"""}	)
     lowerCAmelCase_  :     bool          =					field(default=a__,		metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}	)
     lowerCAmelCase_  :     str          =					field(
         default="""O1""",		metadata={
             """help""": (
                 """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                 """See details at https://nvidia.github.io/apex/amp.html"""
             )
         },		)







     @cached_property
     def 			snake_case_      (				self							)      ->				Tuple["torch.device", int]:
          requires_backends(self							,["""torch"""]							)
          logger.info("""PyTorch: setting up devices"""							)
          if not self.cuda:
               snake_case			:      Dict        =			torch.device("""cpu"""							)
               snake_case			:      Union[str, Any]        =			0
          elif is_torch_tpu_available():
               snake_case			:      Any        =			xm.xla_device()
               snake_case			:      List[Any]        =			0
          else:
               snake_case			:      List[Any]        =			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""							)
               snake_case			:      List[str]        =			torch.cuda.device_count()
          return device, n_gpu







     @property
     def 			snake_case_      (				self							)      ->				List[Any]:
          return is_torch_tpu_available() and self.tpu







     @property
     def 			snake_case_      (				self							)      ->				int:
          requires_backends(self							,["""torch"""]							)
          # TODO(PVP): currently only single GPU is supported
          return torch.cuda.current_device()







     @property
     def 			snake_case_      (				self							)      ->				"torch.device":
          requires_backends(self							,["""torch"""]							)
          return self._setup_devices[0]







     @property
     def 			snake_case_      (				self							)      ->				Optional[int]:
          requires_backends(self							,["""torch"""]							)
          return self._setup_devices[1]







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.n_gpu > 0









'''simple docstring'''







import numpy as np

lowercase      :   str      =       [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					_a :





     '''simple docstring'''



     def __init__(				self							)      ->				None:
          snake_case			:      Dict        =			np.array(__a							)







     def 			snake_case_      (				self							,__a							)      ->				np.ndarray:
          snake_case ,		snake_case			:      Optional[Any]        =			np.where(letter == self.SQUARE							)
          snake_case			:      List[Any]        =			np.concatenate([indexa + 1, indexa + 1]							)
          return indexes







     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          snake_case			:      List[Any]        =			self.SQUARE[indexa - 1, indexa - 1]
          return letter







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			message.lower()
          snake_case			:      Dict        =			message.replace(""" """							,""""""							)
          snake_case			:      List[str]        =			message.replace("""j"""							,"""i"""							)

          snake_case			:      Optional[int]        =			np.empty((2, len(__a							))							)
          for letter_index in range(len(__a							)							):
               snake_case			:      Tuple        =			self.letter_to_numbers(message[letter_index]							)

               snake_case			:      Optional[Any]        =			numbers[0]
               snake_case			:      List[str]        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape(2 * len(__a							)							)
          snake_case			:      List[Any]        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      List[str]        =			int(second_step[numbers_index * 2]							)
               snake_case			:      List[Any]        =			int(second_step[(numbers_index * 2) + 1]							)
               snake_case			:      str        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      Any        =			encoded_message + letter

          return encoded_message







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Any        =			message.lower()
          message.replace(""" """							,""""""							)
          snake_case			:      Dict        =			np.empty(2 * len(__a							)							)
          for letter_index in range(len(__a							)							):
               snake_case			:      str        =			self.letter_to_numbers(message[letter_index]							)
               snake_case			:      Any        =			numbers[0]
               snake_case			:      int        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape((2, len(__a							))							)
          snake_case			:      Dict        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      Optional[Any]        =			int(second_step[0, numbers_index]							)
               snake_case			:      Optional[int]        =			int(second_step[1, numbers_index]							)
               snake_case			:      Tuple        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      int        =			decoded_message + letter

          return decoded_message



