



# 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       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						'''Salesforce/blip-image-captioning-base'''
       UpperCamelCase__  :    Optional[int]											=						(
           '''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.'''
       )
       UpperCamelCase__  :    Optional[int]											=						'''image_captioner'''
       UpperCamelCase__  :    str											=						AutoModelForVisionaSeq

       UpperCamelCase__  :    str											=						['''image''']
       UpperCamelCase__  :    str											=						['''text''']



       def __init__(		self							:     Any     ,		*__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           requires_backends(self     ,		['''vision'''])
           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     "Image"):


           '''simple docstring'''

           return self.pre_processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return self.model.generate(**__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

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










'''simple docstring'''





from __future__ import annotations

import math
from collections.abc import Callable

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 100       ,)      ->      float:
  __lowerCamelCase       :       Dict           = x_start
  __lowerCamelCase       :       int           = fnc(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = 0.0

  for _ in range(_lowerCAmelCase       ):
    # Approximates curve as a sequence of linear lines and sums their length
    __lowerCamelCase       :       List[str]           = (x_end - x_start) / steps + xa
    __lowerCamelCase       :       List[Any]           = fnc(_lowerCAmelCase       )
    length += math.hypot(xa - xa       ,fxa - fxa       )

    # Increment step
    __lowerCamelCase       :       Any           = xa
    __lowerCamelCase       :       Tuple           = fxa

  return length


if __name__ == "__main__":

   def        a_     (							_lowerCAmelCase       )      ->      Dict:
     return math.sin(10 * x       )

   print('f(x) = sin(10 * x)')
   print('The length of the curve from x = -10 to x = 10 is:')
   _UpperCamelCase											=						10
   while i <= 100000:
      print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
      i *= 10






