



import functools
import operator

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


lowercase_	       =			logging.get_logger(__name__)

lowercase_	       =			{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "sew-d"



	def __init__(						self							:    Any				,	snake_case_							:    List[Any]=32				,	snake_case_							:    List[Any]=7_68				,	snake_case_							:    int=12				,	snake_case_							:    Optional[Any]=12				,	snake_case_							:    Tuple=30_72				,	snake_case_							:    Optional[int]=2				,	snake_case_							:    Union[str, Any]=5_12				,	snake_case_							:    Optional[Any]=2_56				,	snake_case_							:    List[Any]=True				,	snake_case_							:    Dict=True				,	snake_case_							:    List[str]=("p2c", "c2p")				,	snake_case_							:    List[str]="layer_norm"				,	snake_case_							:    Optional[int]="gelu_python"				,	snake_case_							:    str=0.1				,	snake_case_							:    int=0.1				,	snake_case_							:    Any=0.1				,	snake_case_							:    Optional[int]=0.0				,	snake_case_							:    Optional[int]=0.1				,	snake_case_							:    str=0.0_2				,	snake_case_							:    Optional[Any]=1e-7				,	snake_case_							:    str=1e-5				,	snake_case_							:    Tuple="group"				,	snake_case_							:    int="gelu"				,	snake_case_							:    List[str]=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)				,	snake_case_							:    Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,	snake_case_							:    Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,	snake_case_							:    List[str]=False				,	snake_case_							:    int=1_28				,	snake_case_							:    Dict=16				,	snake_case_							:    Any=True				,	snake_case_							:    Union[str, Any]=0.0_5				,	snake_case_							:    int=10				,	snake_case_							:    Dict=2				,	snake_case_							:    Union[str, Any]=0.0				,	snake_case_							:    List[Any]=10				,	snake_case_							:    str=0				,	snake_case_							:    Optional[Any]="mean"				,	snake_case_							:    str=False				,	snake_case_							:    Tuple=False				,	snake_case_							:    Optional[Any]=2_56				,	snake_case_							:    List[str]=0				,	snake_case_							:    Optional[Any]=1				,	snake_case_							:    List[Any]=2				,	**snake_case_							:    Dict				,	)->							Optional[int]:
						super().__init__(**snake_case_				,	pad_token_id=snake_case_				,	bos_token_id=snake_case_				,	eos_token_id=snake_case_)
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =feat_extract_norm
						__lowerCAmelCase	   =feat_extract_activation
						__lowerCAmelCase	   =list(snake_case_)
						__lowerCAmelCase	   =list(snake_case_)
						__lowerCAmelCase	   =list(snake_case_)
						__lowerCAmelCase	   =conv_bias
						__lowerCAmelCase	   =num_conv_pos_embeddings
						__lowerCAmelCase	   =num_conv_pos_embedding_groups
						__lowerCAmelCase	   =len(self.conv_dim)
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =intermediate_size
						__lowerCAmelCase	   =squeeze_factor
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =position_buckets
						__lowerCAmelCase	   =share_att_key
						__lowerCAmelCase	   =relative_attention
						__lowerCAmelCase	   =norm_rel_ebd
						__lowerCAmelCase	   =list(snake_case_)
						__lowerCAmelCase	   =hidden_act
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =hidden_dropout
						__lowerCAmelCase	   =attention_dropout
						__lowerCAmelCase	   =activation_dropout
						__lowerCAmelCase	   =feat_proj_dropout
						__lowerCAmelCase	   =final_dropout
						__lowerCAmelCase	   =layer_norm_eps
						__lowerCAmelCase	   =feature_layer_norm_eps
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =vocab_size

						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)`,"""
											    F"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
											    F"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

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

						# ctc loss
						__lowerCAmelCase	   =ctc_loss_reduction
						__lowerCAmelCase	   =ctc_zero_infinity

						# sequence classification
						__lowerCAmelCase	   =use_weighted_layer_sum
						__lowerCAmelCase	   =classifier_proj_size



	@property
	def 		UpperCamelCase							(						self							:    int)->							Tuple:
						return functools.reduce(operator.mul				,	self.conv_stride				,	1)









from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__lowerCAmelCase	   =f"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase	   ={
					    """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""",
					}
					__lowerCAmelCase	   =BeautifulSoup(requests.get(__lowerCamelCase				,   headers=__lowerCamelCase	).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__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




