





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       int										= 'speech_to_text'
					lowercase__   :       List[Any]										= ['past_key_values']
					lowercase__   :       Optional[int]										= {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

					def __init__(    self				,       lowerCamelCase__=1_0_0_0_0				,       lowerCamelCase__=1_2				,       lowerCamelCase__=2_0_4_8				,       lowerCamelCase__=4				,       lowerCamelCase__=6				,       lowerCamelCase__=2_0_4_8				,       lowerCamelCase__=4				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__="relu"				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=2				,       lowerCamelCase__=True				,       lowerCamelCase__=1				,       lowerCamelCase__=0				,       lowerCamelCase__=2				,       lowerCamelCase__=6_0_0_0				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=2				,       lowerCamelCase__=(5, 5)				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=8_0				,       lowerCamelCase__=1				,       **lowerCamelCase__				,       ):
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				encoder_ffn_dim
									_lowerCamelCase        =				encoder_layers
									_lowerCamelCase        =				encoder_attention_heads
									_lowerCamelCase        =				decoder_ffn_dim
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				dropout
									_lowerCamelCase        =				attention_dropout
									_lowerCamelCase        =				activation_dropout
									_lowerCamelCase        =				activation_function
									_lowerCamelCase        =				init_std
									_lowerCamelCase        =				encoder_layerdrop
									_lowerCamelCase        =				decoder_layerdrop
									_lowerCamelCase        =				use_cache
									_lowerCamelCase        =				encoder_layers
									_lowerCamelCase        =				scale_embedding  # scale factor will be sqrt(d_model) if True
									_lowerCamelCase        =				max_source_positions
									_lowerCamelCase        =				max_target_positions
									_lowerCamelCase        =				num_conv_layers
									_lowerCamelCase        =				list(lowerCamelCase__   )
									_lowerCamelCase        =				conv_channels
									_lowerCamelCase        =				input_feat_per_channel
									_lowerCamelCase        =				input_channels

									if len(self.conv_kernel_sizes   ) != self.num_conv_layers:
													raise ValueError(
													    '''Configuration for convolutional module is incorrect. '''
													    '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
													    F"""but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes   )}`, """
													    F"""`config.num_conv_layers = {self.num_conv_layers}`."""   )

									super().__init__(
									    pad_token_id=lowerCamelCase__				,       bos_token_id=lowerCamelCase__				,       eos_token_id=lowerCamelCase__				,       is_encoder_decoder=lowerCamelCase__				,       decoder_start_token_id=lowerCamelCase__				,       **lowerCamelCase__				,       )





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

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def        lowerCAmelCase_(   lowercase_     :							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(lowercase_						,   headers=lowercase_							).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(''','''						,   ''''''							)							)
																    )
																    * 1_00							)
												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__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''headphones'''
			get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
