





from __future__ import annotations

from typing import TypedDict







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						str
   _UpperCamelCase :						int







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

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






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

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






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if not isinstance(lowerCamelCase							, lowerCamelCase   ):
      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:
      a__	:						str      		=  int(lowerCamelCase   )
   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(lowerCamelCase   ):
      raise ValueError(
          "The parameter idx_original_string must be lower than" " len(bwt_string)."   )

   a__	:						List[str]      		=  [""] * len(lowerCamelCase   )
   for _ in range(len(lowerCamelCase   )   ):
      for i in range(len(lowerCamelCase   )   ):
         a__	:						Union[str, Any]      		=  bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


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

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE__       :			Dict			      =						"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""







class   __lowerCAmelCase			(					unittest.TestCase		,_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Optional[int]      		=  load_tool("text-question-answering"      )
      self.tool.setup()
      a__	:						Dict      		=  load_tool("text-question-answering"					,			remote=snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  self.remote_tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Any      		=  self.tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						List[str]      		=  self.remote_tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )

