
import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
						_A						:		List[str]    	=     'data2vec-audio'
						def __init__(			self		,	lowerCamelCase=32		,	lowerCamelCase=7_68		,	lowerCamelCase=12		,	lowerCamelCase=12		,	lowerCamelCase=30_72		,	lowerCamelCase="gelu"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=0.0		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=0.0_2		,	lowerCamelCase=1e-5		,	lowerCamelCase="gelu"		,	lowerCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,	lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)		,	lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)		,	lowerCamelCase=False		,	lowerCamelCase=16		,	lowerCamelCase=19		,	lowerCamelCase=5		,	lowerCamelCase=0.0_5		,	lowerCamelCase=10		,	lowerCamelCase=2		,	lowerCamelCase=0.0		,	lowerCamelCase=10		,	lowerCamelCase=0		,	lowerCamelCase="sum"		,	lowerCamelCase=False		,	lowerCamelCase=False		,	lowerCamelCase=2_56		,	lowerCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,	lowerCamelCase=(5, 3, 3, 1, 1)		,	lowerCamelCase=(1, 2, 3, 1, 1)		,	lowerCamelCase=5_12		,	lowerCamelCase=0		,	lowerCamelCase=1		,	lowerCamelCase=2		,	lowerCamelCase=False		,	lowerCamelCase=3		,	lowerCamelCase=2		,	lowerCamelCase=3		,	lowerCamelCase=None		,	**lowerCamelCase		,	):
										super().__init__(**lowerCamelCase		,	pad_token_id=lowerCamelCase		,	bos_token_id=lowerCamelCase		,	eos_token_id=lowerCamelCase )
										snake_case__						=				hidden_size
										snake_case__						=				feat_extract_activation
										snake_case__						=				list(lowerCamelCase )
										snake_case__						=				list(lowerCamelCase )
										snake_case__						=				list(lowerCamelCase )
										snake_case__						=				conv_bias
										snake_case__						=				num_conv_pos_embeddings
										snake_case__						=				num_conv_pos_embedding_groups
										snake_case__						=				conv_pos_kernel_size
										snake_case__						=				len(self.conv_dim )
										snake_case__						=				num_hidden_layers
										snake_case__						=				intermediate_size
										snake_case__						=				hidden_act
										snake_case__						=				num_attention_heads
										snake_case__						=				hidden_dropout
										snake_case__						=				attention_dropout
										snake_case__						=				activation_dropout
										snake_case__						=				feat_proj_dropout
										snake_case__						=				final_dropout
										snake_case__						=				layerdrop
										snake_case__						=				layer_norm_eps
										snake_case__						=				initializer_range
										snake_case__						=				vocab_size
										snake_case__						=				use_weighted_layer_sum

										if (
										    (len(self.conv_stride ) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
										    or (len(self.conv_dim ) != self.num_feat_extract_layers)
										):
														raise ValueError(
														    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
														    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
														    F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
														    F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										snake_case__						=				mask_time_prob
										snake_case__						=				mask_time_length
										snake_case__						=				mask_time_min_masks
										snake_case__						=				mask_feature_prob
										snake_case__						=				mask_feature_length
										snake_case__						=				mask_feature_min_masks

										# ctc loss
										snake_case__						=				ctc_loss_reduction
										snake_case__						=				ctc_zero_infinity

										# adapter
										snake_case__						=				add_adapter
										snake_case__						=				adapter_kernel_size
										snake_case__						=				adapter_stride
										snake_case__						=				num_adapter_layers
										snake_case__						=				output_hidden_size or hidden_size

										# SequenceClassification-specific parameter. Feel free to ignore for other classes.
										snake_case__						=				classifier_proj_size

										# XVector-specific parameters. Feel free to ignore for other classes.
										snake_case__						=				list(lowerCamelCase )
										snake_case__						=				list(lowerCamelCase )
										snake_case__						=				list(lowerCamelCase )
										snake_case__						=				xvector_output_dim



						@property
						def A_			(			self ):
										return math.prod(self.conv_stride )





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = "laptop"							):
    snake_case__						=				F"""https://www.amazon.in/laptop/s?k={product}"""
    snake_case__						=				{
        "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",
    }
    snake_case__						=				BeautifulSoup(requests.get(__lowerCAmelCase    ,		headers=__lowerCAmelCase							).text							)
    # Initialize a Pandas dataframe with the column titles
    snake_case__						=				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:
            snake_case__						=				item.ha.text
            snake_case__						=				"https://www.amazon.in/" + item.ha.a["href"]
            snake_case__						=				item.find("span"    ,		attrs={"class": "a-offscreen"}							).text
            try:
                snake_case__						=				item.find("span"    ,		attrs={"class": "a-icon-alt"}							).text
            except AttributeError:
                snake_case__						=				"Not available"
            try:
                snake_case__						=				(
                    "₹"
                    + item.find(
                        "span"    ,		attrs={"class": "a-price a-text-price"}							).text.split("₹"							)[1]
                )
            except AttributeError:
                snake_case__						=				""
            try:
                snake_case__						=				float(
                    (
                        (
                            float(product_mrp.strip("₹"							).replace(","    ,		""							)							)
                            - float(product_price.strip("₹"							).replace(","    ,		""							)							)
                        )
                        / float(product_mrp.strip("₹"							).replace(","    ,		""							)							)
                    )
                    * 100							)
            except ValueError:
                snake_case__						=				float("nan"							)
        except AttributeError:
            pass
        snake_case__						=				[
            product_title,
            product_link,
            product_price,
            product_rating,
            product_mrp,
            discount,
        ]
    snake_case__						=				" "
    snake_case__						=				" "
    data_frame.index += 1
    return data_frame


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



