






'''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_						(      lowerCamelCase    ):
  lowercase							=				"""Salesforce/blip-image-captioning-base"""
  lowercase							=				(
      """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							=				"""image_captioner"""
  lowercase							=				AutoModelForVisionaSeq

  lowercase							=				["""image"""]
  lowercase							=				["""text"""]

  def __init__(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.model.generate(**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






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








'''simple docstring'''

from timeit import timeit







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            number &= number - 1
            result += 1
      return result







def    lowercase__			(				__UpperCamelCase   )->			int:
      if number < 0:
            raise ValueError("""the value of input must not be negative"""   )
      UpperCamelCase      = 0
      while number:
            if number % 2 == 1:
                  result += 1
            number >>= 1
      return result







def    lowercase__			(				)->			None:

      def do_benchmark(__UpperCamelCase   ) -> None:
            UpperCamelCase      = """import __main__ as z"""
            print(F"Benchmark when {number = }:"   )
            print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit("""z.get_set_bits_count_using_modulo_operator(25)"""		,						setup=__UpperCamelCase   )
            print(F"timeit() runs in {timing} seconds"   )
            print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase   ) = }"   )
            UpperCamelCase      = timeit(
                """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""		,						setup=__UpperCamelCase		,						)
            print(F"timeit() runs in {timing} seconds"   )

      for number in (25, 37, 58, 0):
            do_benchmark(__UpperCamelCase   )
            print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
