from __future__ import annotations

from typing import Any



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       list[Any]	)							->					None:







  """simple docstring"""





  create_state_space_tree(__UpperCamelCase ,     [] ,     0	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       list[Any] ,     __UpperCamelCase    :       list[Any] ,     __UpperCamelCase    :       int	)							->					None:







  """simple docstring"""





  if index == len(__UpperCamelCase	):
    print(__UpperCamelCase	)
    return

  create_state_space_tree(__UpperCamelCase ,     __UpperCamelCase ,     index + 1	)
  current_subsequence.append(sequence[index]	)
  create_state_space_tree(__UpperCamelCase ,     __UpperCamelCase ,     index + 1	)
  current_subsequence.pop()


if __name__ == "__main__":
   __lowerCamelCase			: list[Any]							   =		[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['''A''', '''B''', '''C'''])
   generate_all_subsequences(seq)

# 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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






     return self.pre_processor.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )[0].strip()
