



"""simple docstring"""






import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
	from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm


def 			_lowercase				(  __snake_case	)						->   Union[str, Any]:
			if is_torch_version("<"					,"2.0.0"	) or not hasattr(__snake_case					,"_dynamo"	):
						return False
			return isinstance(__snake_case					,torch._dynamo.eval_frame.OptimizedModule	)


def 			_lowercase				(  __snake_case					,__snake_case = True	)						->   Union[str, Any]:
			__lowerCAmelCase      :  Dict												=       (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

			__lowerCAmelCase      :  Optional[int]												=       is_compiled_module(__snake_case	)
			if is_compiled:
						__lowerCAmelCase      :  str												=       model
						__lowerCAmelCase      :  Any												=       model._orig_mod

			if is_deepspeed_available():
						options += (DeepSpeedEngine,)

			while isinstance(__snake_case					,__snake_case	):
						__lowerCAmelCase      :  Any												=       model.module

			if not keep_fpaa_wrapper:
						__lowerCAmelCase      :  Dict												=       getattr(__snake_case					,"forward"	)
						__lowerCAmelCase      :  Dict												=       model.__dict__.pop("_original_forward"					,__snake_case	)
						if original_forward is not None:
									while hasattr(__snake_case					,"__wrapped__"	):
												__lowerCAmelCase      :  List[str]												=       forward.__wrapped__
												if forward == original_forward:
															break
									__lowerCAmelCase      :  Optional[int]												=       forward
						if getattr(__snake_case					,"_converted_to_transformer_engine"					,__snake_case	):
									convert_model(__snake_case					,to_transformer_engine=__snake_case	)

			if is_compiled:
						__lowerCAmelCase      :  Dict												=       model
						__lowerCAmelCase      :  Optional[Any]												=       compiled_model

			return model


def 			_lowercase				(  )						->   List[str]:
			PartialState().wait_for_everyone()


def 			_lowercase				(  __snake_case					,__snake_case	)						->   List[str]:
			if PartialState().distributed_type == DistributedType.TPU:
						xm.save(__snake_case					,__snake_case	)
			elif PartialState().local_process_index == 0:
						torch.save(__snake_case					,__snake_case	)


@contextmanager
def 			_lowercase				(  **__snake_case	)						->   Optional[int]:
			for key, value in kwargs.items():
						__lowerCAmelCase      :  List[Any]												=       str(__snake_case	)

			yield

			for key in kwargs:
						if key.upper() in os.environ:
									del os.environ[key.upper()]


def 			_lowercase				(  __snake_case	)						->   Tuple:
			if not hasattr(__snake_case					,"__qualname__"	) and not hasattr(__snake_case					,"__name__"	):
						__lowerCAmelCase      :  Union[str, Any]												=       getattr(__snake_case					,"__class__"					,__snake_case	)
			if hasattr(__snake_case					,"__qualname__"	):
						return obj.__qualname__
			if hasattr(__snake_case					,"__name__"	):
						return obj.__name__
			return str(__snake_case	)


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for key, value in source.items():
						if isinstance(__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Tuple												=       destination.setdefault(__snake_case					,{}	)
									merge_dicts(__snake_case					,__snake_case	)
						else:
									__lowerCAmelCase      :  List[Any]												=       value

			return destination





def 			_lowercase				(  __snake_case = None	)						->   bool:
			if port is None:
						__lowerCAmelCase      :  str												=       29_500
			with socket.socket(socket.AF_INET					,socket.SOCK_STREAM	) as s:
						return s.connect_ex(("localhost", port)	) == 0




"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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