

'''simple docstring'''





from __future__ import annotations

from typing import TypedDict





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Dict      =						42
 _lowercase     :					Dict      =						42







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     if not isinstance(_UpperCamelCase		,							_UpperCamelCase				):
          raise TypeError("The parameter s type must be str."				)

     return [s[i:] + s[:i] for i in range(len(_UpperCamelCase				)				)]







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     if not isinstance(_UpperCamelCase		,							_UpperCamelCase				):
          raise TypeError("The parameter s type must be str."				)
     if not s:
          raise ValueError("The parameter s must not be empty."				)

     _lowerCAmelCase   :					Any        =    all_rotations(_UpperCamelCase				)
     rotations.sort()  # sort the list of rotations in alphabetically order
     # make a string composed of the last char of each rotation
     _lowerCAmelCase   :					Dict        =    {
         "bwt_string": "".join([word[-1] for word in rotations]				),
         "idx_original_string": rotations.index(_UpperCamelCase				),
     }
     return response







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     if not isinstance(_UpperCamelCase		,							_UpperCamelCase				):
          raise TypeError("The parameter bwt_string type must be str."				)
     if not bwt_string:
          raise ValueError("The parameter bwt_string must not be empty."				)
     try:
          _lowerCAmelCase   :					Optional[Any]        =    int(_UpperCamelCase				)
     except ValueError:
          raise TypeError(
              "The parameter idx_original_string type must be int or passive"
              " of cast to int."				)
     if idx_original_string < 0:
          raise ValueError("The parameter idx_original_string must not be lower than 0."				)
     if idx_original_string >= len(_UpperCamelCase				):
          raise ValueError(
              "The parameter idx_original_string must be lower than" " len(bwt_string)."				)

     _lowerCAmelCase   :					Any        =    [""] * len(_UpperCamelCase				)
     for _ in range(len(_UpperCamelCase				)				):
          for i in range(len(_UpperCamelCase				)				):
               _lowerCAmelCase   :					Optional[Any]        =    bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
  __UpperCAmelCase    				=     "Provide a string that I will generate its BWT transform: "
  __UpperCAmelCase    				=     input(entry_msg).strip()
  __UpperCAmelCase    				=     bwt_transform(s)
  print(
      f'''Burrows Wheeler transform for string \'{s}\' results '''
      f'''in \'{result['bwt_string']}\''''
  )
  __UpperCAmelCase    				=     reverse_bwt(result['bwt_string'], result['idx_original_string'])
  print(
      f'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
      f'''we get original string \'{original_string}\''''
  )









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						[
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]

 def __init__(		self :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _lowerCAmelCase   :					str        =    deprecated_arg[3:]
                _lowerCAmelCase   :					List[Any]        =    not kwargs.pop(A_       )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''       )
      _lowerCAmelCase   :					Any        =    kwargs.pop("tpu_name"     ,						self.tpu_name       )
      _lowerCAmelCase   :					str        =    kwargs.pop("device_idx"     ,						self.device_idx       )
      _lowerCAmelCase   :					str        =    kwargs.pop("eager_mode"     ,						self.eager_mode       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("use_xla"     ,						self.use_xla       )
      super().__init__(**A_       )

 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''Name of TPU'''}  ,		)
 _lowercase     :					int      =						field(
     default=0  ,		metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}  ,		)
 _lowercase     :					bool      =						field(default=A  ,		metadata={'''help''': '''Benchmark models in eager model.'''} )
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={
         '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
     }  ,		)

 @cached_property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      _lowerCAmelCase   :					Any        =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     _lowerCAmelCase   :					Union[str, Any]        =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                else:
                     _lowerCAmelCase   :					List[str]        =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                _lowerCAmelCase   :					Optional[int]        =    None
      return tpu

 @cached_property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu       )
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

           _lowerCAmelCase   :					List[Any]        =    tf.distribute.TPUStrategy(self._setup_tpu       )
      else:
           # currently no multi gpu is allowed
           if self.is_gpu:
                # TODO: Currently only single GPU is supported
                tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,						"GPU"       )
                _lowerCAmelCase   :					Dict        =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
           else:
                tf.config.set_visible_devices([]     ,						"GPU"       )  # disable GPU
                _lowerCAmelCase   :					Optional[Any]        =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

      return strategy

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_tpu is not None

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_strategy

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return tf.config.list_physical_devices("GPU"       )

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.cuda:
           return len(self.gpu_list       )
      return 0


 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.n_gpu > 0






