

from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





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


if __name__ == "__main__":
    __A			:					Optional[Any]						=			'headphones'
    get_amazon_product_data(product).to_csv(f'Amazon Product Data for {product}.csv')


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


__A			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(       self					:			List[Any]     , **__lowerCamelCase					:			Any			):
       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               SCREAMING_SNAKE_CASE										=   deprecated_arg[3:]
               SCREAMING_SNAKE_CASE										=   not kwargs.pop(__lowerCamelCase			)
               logger.warning(
                   f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                   f" {positive_arg}={kwargs[positive_arg]}"			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("tpu_name"     , self.tpu_name			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("device_idx"     , self.device_idx			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("eager_mode"     , self.eager_mode			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("use_xla"     , self.use_xla			)
       super().__init__(**__lowerCamelCase			)

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }					,       )





   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu			)
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

           SCREAMING_SNAKE_CASE										=   tf.distribute.TPUStrategy(self._setup_tpu			)
       else:
           # currently no multi gpu is allowed
           if self.is_gpu:
               # TODO: Currently only single GPU is supported
               tf.config.set_visible_devices(self.gpu_list[self.device_idx]     , "GPU"			)
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"			)
           else:
               tf.config.set_visible_devices([]     , "GPU"			)  # disable GPU
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"			)

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       requires_backends(self     , ["tf"]			)
       return self._setup_strategy





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.cuda:
           return len(self.gpu_list			)
       return 0





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0