


'''simple docstring'''






from __future__ import annotations

from typing import TypedDict




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42






def 	_A				(lowerCAmelCase__  :str		)   ->  list[str]:




     '''simple docstring'''



     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__  :str		)   ->  BWTTransformDict:




     '''simple docstring'''



     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			      =							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			      =							{
         "bwt_string": "".join([word[-1] for word in rotations]		),
         "idx_original_string": rotations.index(lowerCAmelCase__		),
     }
     return response






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int		)   ->  str:




     '''simple docstring'''



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


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





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _lowerCAmelCase 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

