


'''simple docstring'''







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


SCREAMING_SNAKE_CASE_   =      logging.get_logger(__name__)


@dataclass
class 			lowerCAmelCase_    (							snake_case__   ):





  """simple docstring"""



  a_							:Union[str, Any]				       =[
      'no_inference',
      'no_cuda',
      'no_tpu',
      'no_speed',
      'no_memory',
      'no_env_print',
      'no_multi_process',
  ]

  def __init__(       self  :				Optional[Any]					,				**SCREAMING_SNAKE_CASE__  :				Optional[Any]    ):





       '''simple docstring'''

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

  a_							:str				       =field(
      default=snake_case__		,     metadata={"""help""": """Name of TPU"""}		,     )
  a_							:int				       =field(
      default=0		,     metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}		,     )
  a_							:bool				       =field(default=snake_case__		,     metadata={"""help""": """Benchmark models in eager model."""}   )
  a_							:bool				       =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 __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

       requires_backends(self					,				["""tf"""]    )
       __a   			= None
       if self.tpu:
            try:
                 if self.tpu_name:
                      __a   			= tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
                 else:
                      __a   			= tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                 __a   			= None
       return tpu

  @cached_property
  def __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

       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    )

            __a   			= 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"""    )
                 __a   			= tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''    )
            else:
                 tf.config.set_visible_devices([]					,				"""GPU"""    )  # disable GPU
                 __a   			= tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''    )

       return strategy

  @property
  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       requires_backends(self					,				["""tf"""]    )
       return self._setup_tpu is not None

  @property
  def __a    (       self  :				Any    ):





       '''simple docstring'''

       requires_backends(self					,				["""tf"""]    )
       return self._setup_strategy

  @property
  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       requires_backends(self					,				["""tf"""]    )
       return tf.config.list_physical_devices("""GPU"""    )

  @property
  def __a    (       self  :				Optional[int]    ):





       '''simple docstring'''

       requires_backends(self					,				["""tf"""]    )
       if self.cuda:
            return len(self.gpu_list    )
       return 0


  @property
  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

       return self.n_gpu > 0











'''simple docstring'''





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def lowerCAmelCase    (__A = "laptop"):



     """simple docstring"""



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


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



