






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__a:     str   					=					logging.get_logger(__name__)


if is_vision_available():
  import PIL
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''pixel_values''']


  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Union[int, float] = 1 / 255       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			Optional[int]       ,				)						->	None:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    size if size is not None else {"""shortest_edge""": 224}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         _UpperCAmelCase							=    crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase       ,				param_name="""crop_size"""							)

         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    resample
         _UpperCAmelCase							=    do_center_crop
         _UpperCAmelCase							=    crop_size
         _UpperCAmelCase							=    do_rescale
         _UpperCAmelCase							=    rescale_factor
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _UpperCAmelCase							=    image_std if image_std is not None else OPENAI_CLIP_STD
         _UpperCAmelCase							=    do_convert_rgb


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         if "shortest_edge" not in size:
                raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
         _UpperCAmelCase							=    get_resize_output_image_size(lowerCamelCase       ,				size=size["""shortest_edge"""]       ,				default_to_square=lowerCamelCase							)
         return resize(lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase							)
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
         return center_crop(lowerCamelCase       ,				size=(size["""height"""], size["""width"""])       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[int, float]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	Optional[int]:


         """simple docstring"""
         return rescale(lowerCamelCase       ,				scale=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Any       ,				)						->	np.ndarray:


         """simple docstring"""
         return normalize(lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)





  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			ImageInput       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			int = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			float = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			Optional[ChannelDimension] = ChannelDimension.FIRST       ,				**lowerCamelCase		:			Any       ,				)						->	PIL.Image.Image:


         """simple docstring"""
         _UpperCAmelCase							=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							=    size if size is not None else self.size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				param_name="""size"""       ,				default_to_square=lowerCamelCase							)
         _UpperCAmelCase							=    resample if resample is not None else self.resample
         _UpperCAmelCase							=    do_center_crop if do_center_crop is not None else self.do_center_crop
         _UpperCAmelCase							=    crop_size if crop_size is not None else self.crop_size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				param_name="""crop_size"""       ,				default_to_square=lowerCamelCase							)
         _UpperCAmelCase							=    do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase							=    rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase							=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase							=    image_std if image_std is not None else self.image_std
         _UpperCAmelCase							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _UpperCAmelCase							=    make_list_of_images(lowerCamelCase							)

         if not valid_images(lowerCamelCase							):
                raise ValueError(
                    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

         if do_resize and size is None:
                raise ValueError("""Size must be specified if do_resize is True."""							)

         if do_center_crop and crop_size is None:
                raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

         if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

         if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
                _UpperCAmelCase							=    [convert_to_rgb(lowerCamelCase							) for image in images]

         # All transformations expect numpy arrays.
         _UpperCAmelCase							=    [to_numpy_array(lowerCamelCase							) for image in images]

         if do_resize:
                _UpperCAmelCase							=    [self.resize(image=lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase							) for image in images]

         if do_center_crop:
                _UpperCAmelCase							=    [self.center_crop(image=lowerCamelCase       ,				size=lowerCamelCase							) for image in images]

         if do_rescale:
                _UpperCAmelCase							=    [self.rescale(image=lowerCamelCase       ,				scale=lowerCamelCase							) for image in images]

         if do_normalize:
                _UpperCAmelCase							=    [self.normalize(image=lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    [to_channel_dimension_format(lowerCamelCase       ,				lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    {"""pixel_values""": images}
         return BatchFeature(data=lowerCamelCase       ,				tensor_type=lowerCamelCase							)




'''simple docstring'''





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def lowerCAmelCase    (__A = "laptop"):



     """simple docstring"""



     _a           =    F'''https://www.amazon.in/laptop/s?k={product}'''
     _a           =    {
         '''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
         '''Accept-Language''': '''en-US, en;q=0.5''',
     }
     _a           =    BeautifulSoup(requests.get(__A    ,      headers=__A).text)
     # Initialize a Pandas dataframe with the column titles
     _a           =    DataFrame(
         columns=[
             '''Product Title''',
             '''Product Link''',
             '''Current Price of the product''',
             '''Product Rating''',
             '''MRP of the product''',
             '''Discount''',
         ])
     # Loop through each entry and store them in the dataframe
     for item, _ in zip_longest(
         soup.find_all(
             '''div'''    ,      attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''}    ,      )    ,      soup.find_all('''div'''    ,      attrs={'''class''': '''a-row a-size-base a-color-base'''})    ,      ):
          try:
               _a           =    item.ha.text
               _a           =    '''https://www.amazon.in/''' + item.ha.a['''href''']
               _a           =    item.find('''span'''    ,      attrs={'''class''': '''a-offscreen'''}).text
               try:
                    _a           =    item.find('''span'''    ,      attrs={'''class''': '''a-icon-alt'''}).text
               except AttributeError:
                    _a           =    '''Not available'''
               try:
                    _a           =    (
                        '''₹'''
                        + item.find(
                            '''span'''    ,      attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
                    )
               except AttributeError:
                    _a           =    ''''''
               try:
                    _a           =    float(
                        (
                            (
                                float(product_mrp.strip('''₹''').replace(''','''    ,      ''''''))
                                - float(product_price.strip('''₹''').replace(''','''    ,      ''''''))
                            )
                            / float(product_mrp.strip('''₹''').replace(''','''    ,      ''''''))
                        )
                        * 100)
               except ValueError:
                    _a           =    float('''nan''')
          except AttributeError:
               pass
          _a           =    [
              product_title,
              product_link,
              product_price,
              product_rating,
              product_mrp,
              discount,
          ]
     _a           =    ''' '''
     _a           =    ''' '''
     data_frame.index += 1
     return data_frame


if __name__ == "__main__":
  lowercase_									=    "headphones"
  get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")



