


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """table-transformer"""
     _lowerCamelCase   =      ["""past_key_values"""]
     _lowerCamelCase   =      {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }


     def __init__(	self  ,       __A=True  ,       __A=None  ,       __A=3  ,       __A=100  ,       __A=6  ,       __A=2048  ,       __A=8  ,       __A=6  ,       __A=2048  ,       __A=8  ,       __A=0.0  ,       __A=0.0  ,       __A=True  ,       __A="relu"  ,       __A=256  ,       __A=0.1  ,       __A=0.0  ,       __A=0.0  ,       __A=0.02  ,       __A=1.0  ,       __A=False  ,       __A="sine"  ,       __A="resnet50"  ,       __A=True  ,       __A=False  ,       __A=1  ,       __A=5  ,       __A=2  ,       __A=1  ,       __A=1  ,       __A=5  ,       __A=2  ,       __A=0.1  ,       **__A  ,       ):
        if backbone_config is not None and use_timm_backbone:
           raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""    )

        if not use_timm_backbone:
           if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
              __a					=  CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
           elif isinstance(__A  ,       __A    ):
              __a					=  backbone_config.get("""model_type"""    )
              __a					=  CONFIG_MAPPING[backbone_model_type]
              __a					=  config_class.from_dict(__A    )
           # set timm attributes to None
           __a	,    __a	,    __a					=  None, None, None

        __a					=  use_timm_backbone
        __a					=  backbone_config
        __a					=  num_channels
        __a					=  num_queries
        __a					=  d_model
        __a					=  encoder_ffn_dim
        __a					=  encoder_layers
        __a					=  encoder_attention_heads
        __a					=  decoder_ffn_dim
        __a					=  decoder_layers
        __a					=  decoder_attention_heads
        __a					=  dropout
        __a					=  attention_dropout
        __a					=  activation_dropout
        __a					=  activation_function
        __a					=  init_std
        __a					=  init_xavier_std
        __a					=  encoder_layerdrop
        __a					=  decoder_layerdrop
        __a					=  encoder_layers
        __a					=  auxiliary_loss
        __a					=  position_embedding_type
        __a					=  backbone
        __a					=  use_pretrained_backbone
        __a					=  dilation
        # Hungarian matcher
        __a					=  class_cost
        __a					=  bbox_cost
        __a					=  giou_cost
        # Loss coefficients
        __a					=  mask_loss_coefficient
        __a					=  dice_loss_coefficient
        __a					=  bbox_loss_coefficient
        __a					=  giou_loss_coefficient
        __a					=  eos_coefficient
        super().__init__(is_encoder_decoder=__A  ,       **__A    )


     @property
     def 	snake_case_  (	self    ):
        return self.encoder_attention_heads


     @property
     def 	snake_case_  (	self    ):
        return self.d_model







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      version.parse("""1.11"""							)


     @property
     def 	snake_case_  (	self    ):
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""pixel_mask""", {0: """batch"""}),
            ]    )


     @property
     def 	snake_case_  (	self    ):
        return 1E-5


     @property
     def 	snake_case_  (	self    ):
        return 12








from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def      __lowercase(						UpperCAmelCase__ = "laptop"			):


      """simple docstring"""



      lowerCamelCase             =		F"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase             =		{
          "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
          "Accept-Language": "en-US, en;q=0.5",
      }
      lowerCamelCase             =		BeautifulSoup(requests.get(UpperCAmelCase__ , headers=UpperCAmelCase__			).text			)
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase             =		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:
                  lowerCamelCase             =		item.ha.text
                  lowerCamelCase             =		"https://www.amazon.in/" + item.ha.a["href"]
                  lowerCamelCase             =		item.find("span" , attrs={"class": "a-offscreen"}			).text
                  try:
                        lowerCamelCase             =		item.find("span" , attrs={"class": "a-icon-alt"}			).text
                  except AttributeError:
                        lowerCamelCase             =		"Not available"
                  try:
                        lowerCamelCase             =		(
                            "₹"
                            + item.find(
                                "span" , attrs={"class": "a-price a-text-price"}			).text.split("₹"			)[1]
                        )
                  except AttributeError:
                        lowerCamelCase             =		""
                  try:
                        lowerCamelCase             =		float(
                            (
                                (
                                    float(product_mrp.strip("₹"			).replace("," , ""			)			)
                                    - float(product_price.strip("₹"			).replace("," , ""			)			)
                                )
                                / float(product_mrp.strip("₹"			).replace("," , ""			)			)
                            )
                            * 100			)
                  except ValueError:
                        lowerCamelCase             =		float("nan"			)
            except AttributeError:
                  pass
            lowerCamelCase             =		[
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase             =		" "
      lowerCamelCase             =		" "
      data_frame.index += 1
      return data_frame


if __name__ == "__main__":
     a_    :  Dict						  =		'headphones'
     get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")