





'''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

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


if TYPE_CHECKING:
	from PIL import Image




class  _A (					__lowercase		):
		lowercase__:   Optional[int]				     =   '''Salesforce/blip-image-captioning-base'''
		lowercase__:   Optional[Any]				     =   (
		    '''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.'''
		)
		lowercase__:   int				     =   '''image_captioner'''
		lowercase__:   Optional[Any]				     =   AutoModelForVisionaSeq

		lowercase__:   int				     =   ['''image''']
		lowercase__:   List[str]				     =   ['''text''']

		def __init__(   self					:   Tuple  ,      *__magic_name__					:   List[str]  ,      **__magic_name__					:   List[Any]			)					->    List[str]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   "Image"			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   int			)					->    Optional[Any]:


					"""simple docstring"""

					return self.model.generate(**__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

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










from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




