


from __future__ import annotations

from typing import TypedDict



class    UpperCAmelCase_	(							a__):
							'''simple docstring'''
							__UpperCamelCase      :     str
							__UpperCamelCase      :     int




def      a	(							SCREAMING_SNAKE_CASE_      :		str       ):






							"""simple docstring"""





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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		str       ):






							"""simple docstring"""





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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		int       ):






							"""simple docstring"""





							if not isinstance(_lowercase							,			_lowercase       ):
														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:
														UpperCamelCase    :	Tuple   	=		int(_lowercase       )
							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(_lowercase       ):
														raise ValueError(
														    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''       )

							UpperCamelCase    :	str   	=		[""] * len(_lowercase       )
							for _ in range(len(_lowercase       )       ):
														for i in range(len(_lowercase       )       ):
																					UpperCamelCase    :	int   	=		bwt_string[i] + ordered_rotations[i]
														ordered_rotations.sort()
							return ordered_rotations[idx_original_string]


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





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()

