






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


UpperCAmelCase__      			=						logging.get_logger(__name__)







def     a_   (__A=None				,       __A=None      )				->						str:



					"""simple docstring"""
					return field(default_factory=lambda: default				,       metadata=__UpperCAmelCase      )




@dataclass
class 							snake_case_       :

						"""simple docstring"""



						snake_case__        =      list_field(
						    default=[]		,							metadata={
						        """help""": (
						            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
						            """ of all available models"""
						        )
						    }		,							)

						snake_case__        =      list_field(
						    default=[8]		,							metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}      )

						snake_case__        =      list_field(
						    default=[8, 32, 128, 512]		,							metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}		,							)

						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}		,							)
						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}		,							)
						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}      )
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Use FP16 to accelerate inference."""}      )
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Benchmark training of model"""}      )
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Verbose memory tracing"""}      )
						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}		,							)
						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={
						        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
						    }		,							)
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Trace memory line by line"""}      )
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Save result to a CSV file"""}      )
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Save all print statements in a log file"""}      )
						snake_case__        =      field(default=_SCREAMING_SNAKE_CASE		,							metadata={"""help""": """Whether to print environment information"""}      )
						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={
						        """help""": (
						            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
						            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
						            """ for debugging / testing and on TPU."""
						        )
						    }		,							)
						snake_case__        =      field(
						    default=F'''inference_time_{round(time()      )}.csv'''		,							metadata={"""help""": """CSV filename used if saving time results to csv."""}		,							)
						snake_case__        =      field(
						    default=F'''inference_memory_{round(time()      )}.csv'''		,							metadata={"""help""": """CSV filename used if saving memory results to csv."""}		,							)
						snake_case__        =      field(
						    default=F'''train_time_{round(time()      )}.csv'''		,							metadata={"""help""": """CSV filename used if saving time results to csv for training."""}		,							)
						snake_case__        =      field(
						    default=F'''train_memory_{round(time()      )}.csv'''		,							metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}		,							)
						snake_case__        =      field(
						    default=F'''env_info_{round(time()      )}.csv'''		,							metadata={"""help""": """CSV filename used if saving environment information."""}		,							)
						snake_case__        =      field(
						    default=F'''log_{round(time()      )}.csv'''		,							metadata={"""help""": """Log filename used if print statements are saved in log."""}		,							)
						snake_case__        =      field(default=3		,							metadata={"""help""": """Times an experiment will be run."""}      )
						snake_case__        =      field(
						    default=_SCREAMING_SNAKE_CASE		,							metadata={
						        """help""": (
						            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
						            """ model weights."""
						        )
						    }		,							)



						def        UpperCAmelCase__					(self:					List[str]   )    ->     int:




											'''simple docstring'''



											warnings.warn(
											    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
											    " are deprecated in general and it is advised to use external Benchmarking libraries "
											    " to benchmark Transformer models." ,					_lowerCAmelCase ,					)



						def        UpperCAmelCase__					(self:					str   )    ->     Optional[int]:




											'''simple docstring'''



											return json.dumps(dataclasses.asdict(self   ) ,					indent=2   )



						@property
						def        UpperCAmelCase__					(self:					Dict   )    ->     Optional[int]:




											'''simple docstring'''



											if len(self.models   ) <= 0:
																raise ValueError(
																    "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
																    " bert-base-cased` or `args.models = [\'bert-base-cased\']."   )
											return self.models



						@property
						def        UpperCAmelCase__					(self:					Any   )    ->     int:




											'''simple docstring'''



											if not self.multi_process:
																return False
											elif self.is_tpu:
																logger.info("Multiprocessing is currently not possible on TPU."   )
																return False
											else:
																return True











from __future__ import annotations

from typing import TypedDict





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       42
 lowercase_							       =       42


def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					list[str]:
      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    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					BWTTransformDict:
      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.'				)

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

def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      int				)   ->					str:
      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:
            SCREAMING_SNAKE_CASE_      		=     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).'				)

      SCREAMING_SNAKE_CASE_      		=     [''] * len(__UpperCAmelCase				)
      for _ in range(len(__UpperCAmelCase				)				):
            for i in range(len(__UpperCAmelCase				)				):
                  SCREAMING_SNAKE_CASE_      		=     bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   lowerCamelCase__					:  Optional[int]      							=				'Provide a string that I will generate its BWT transform: '
   lowerCamelCase__					:  List[str]      							=				input(entry_msg).strip()
   lowerCamelCase__					:  int      							=				bwt_transform(s)
   print(
       f'''Burrows Wheeler transform for string \'{s}\' results '''
       f'''in \'{result['bwt_string']}\''''
   )
   lowerCamelCase__					:  Dict      							=				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}\''''
   )