


"""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


_snake_case        =				logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class 	_a   (   __snake_case			):
    a_     :					bool   							=     field(default=__snake_case		,		metadata={'help': 'Whether to use SortishSampler or not.'}			)
    a_     :					bool   							=     field(
        default=__snake_case		,		metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
    a_     :					Optional[int]   							=     field(
        default=__snake_case		,		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.'
            )
        }		,		)
    a_     :					Optional[int]   							=     field(
        default=__snake_case		,		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.'
            )
        }		,		)
    a_     :					Optional[Union[str, Path, GenerationConfig]]   							=     field(
        default=__snake_case		,		metadata={
            'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
        }		,		)
    def   _UpperCamelCase							(			self				:       str					):
          lowerCamelCase__							     =		super().to_dict()
          for k, v in d.items():
                if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__					):
                      lowerCamelCase__							     =		v.to_dict()
          return d











"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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