from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCamelCase		:       Any									=     logging.get_logger(__name__)

__UpperCamelCase		:       Optional[int]									=     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}





class 					lowercase__						(   lowercase_	,							lowercase_):
						UpperCamelCase_     =		"""convnextv2"""


						def __init__(       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Optional[int]=3    ,		UpperCamelCase__	:				Tuple=4    ,		UpperCamelCase__	:				List[str]=4    ,		UpperCamelCase__	:				Tuple=None    ,		UpperCamelCase__	:				Union[str, Any]=None    ,		UpperCamelCase__	:				Tuple="gelu"    ,		UpperCamelCase__	:				Any=0.02    ,		UpperCamelCase__	:				List[Any]=1E-12    ,		UpperCamelCase__	:				List[Any]=0.0    ,		UpperCamelCase__	:				Optional[Any]=224    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				str=None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





												super().__init__(**__UpperCamelCase						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							num_channels
												SCREAMING_SNAKE_CASE							:    List[str]							=							patch_size
												SCREAMING_SNAKE_CASE							:    List[Any]							=							num_stages
												SCREAMING_SNAKE_CASE							:    int							=							[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
												SCREAMING_SNAKE_CASE							:    str							=							[3, 3, 9, 3] if depths is None else depths
												SCREAMING_SNAKE_CASE							:    Dict							=							hidden_act
												SCREAMING_SNAKE_CASE							:    int							=							initializer_range
												SCREAMING_SNAKE_CASE							:    Dict							=							layer_norm_eps
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							drop_path_rate
												SCREAMING_SNAKE_CASE							:    Tuple							=							image_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							['''stem'''] + [f"""stage{idx}""" for idx in range(1    ,		len(self.depths						) + 1						)]
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							get_aligned_output_features_output_indices(
												    out_features=__UpperCamelCase    ,		out_indices=__UpperCamelCase    ,		stage_names=self.stage_names						)












"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					A__				(			UpperCamelCase = "laptop"       ):
							A		=  F"https://www.amazon.in/laptop/s?k={product}"
							A		=  {
							    "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",
							}
							A		=  BeautifulSoup(requests.get(UpperCamelCase					,  headers=UpperCamelCase       ).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__":
		_snake_case     :		Optional[int]       						= 'headphones'
		get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")


