


'''simple docstring'''






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def    _snake_case  (	A = "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(A					,       headers=A			).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__":
		__UpperCAmelCase       							=				'''headphones'''
		get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")



'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d