from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def      a__ (		__UpperCamelCase = "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(__UpperCamelCase     ,      headers=__UpperCamelCase				).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(","     ,      ""				)				)
                            )
                            * 1_0_0				)
                  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     :  str					     =       "headphones"
      get_amazon_product_data(product).to_csv(f"Amazon Product Data for {product}.csv")



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import jax
      import jaxlib

A     :  Optional[Any]					     =       get_logger()

A     :  Optional[dict]					     =       None


class   lowerCamelCase					(TensorFormatter[Mapping, '''jax.Array''', Mapping]					):
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Union[str, Any]=None  ,     **__magic_name__ :       str   )  ->	Tuple:
        super().__init__(features=__magic_name__   )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(__magic_name__  ,     __magic_name__   ):
              raise ValueError(
                  F'''Expected {device} to be a `str` not {type(__magic_name__   )}, as `jaxlib.xla_extension.Device` '''
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."   )
        SCREAMING_SNAKE_CASE_          =  device if isinstance(__magic_name__  ,     __magic_name__   ) else str(jax.devices()[0]   )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()   ):
              logger.warning(
                  F'''Device with string identifier {self.device} not listed among the available '''
                  F'''devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '''
                  F'''device: {str(jax.devices()[0]   )}.'''   )
              SCREAMING_SNAKE_CASE_          =  str(jax.devices()[0]   )
        SCREAMING_SNAKE_CASE_          =  jnp_array_kwargs







  @staticmethod
  def    __A     (							)  ->	Dict[str, "jaxlib.xla_extension.Device"]:
        import jax

        return {str(__magic_name__   ): device for device in jax.devices()}







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]   )  ->	List[str]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     __magic_name__   ) and column:
              if all(
                  isinstance(__magic_name__  ,     jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
                    return jnp.stack(__magic_name__  ,     axis=0   )
        return column







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	Optional[Any]:
        import jax
        import jax.numpy as jnp

        if isinstance(__magic_name__  ,     (str, bytes, type(__magic_name__   ))   ):
              return value
        elif isinstance(__magic_name__  ,     (np.character, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.character   ):
              return value.tolist()

        SCREAMING_SNAKE_CASE_          =  {}

        if isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.integer   ):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
              else:
                    SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.intaa}
        elif isinstance(__magic_name__  ,     (np.number, np.ndarray)   ) and np.issubdtype(value.dtype  ,     np.floating   ):
              SCREAMING_SNAKE_CASE_          =  {"dtype": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__magic_name__  ,     PIL.Image.Image   ):
                    SCREAMING_SNAKE_CASE_          =  np.asarray(__magic_name__   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
              SCREAMING_SNAKE_CASE_          =  self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]   ):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__magic_name__  ,     **{**default_dtype, **self.jnp_array_kwargs}   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__magic_name__  ,     torch.Tensor   ):
                    return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
        if hasattr(__magic_name__  ,     "__array__"   ) and not isinstance(__magic_name__  ,     jax.Array   ):
              SCREAMING_SNAKE_CASE_          =  data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__magic_name__  ,     np.ndarray   ):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                    return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        elif isinstance(__magic_name__  ,     (list, tuple)   ):
              return self._consolidate([self.recursive_tensorize(__magic_name__   ) for substruct in data_struct]   )
        return self._tensorize(__magic_name__   )







  def    __A     (							self :       int  ,     __magic_name__ :       dict   )  ->	Any:
        return map_nested(self._recursive_tensorize  ,     __magic_name__  ,     map_list=__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_row(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_row(__magic_name__   )
        return self.recursive_tensorize(__magic_name__   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	"jax.Array":
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_column(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_column(__magic_name__  ,     pa_table.column_names[0]   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self._consolidate(__magic_name__   )
        return column







  def    __A     (							self :       Dict  ,     __magic_name__ :       pa.Table   )  ->	Mapping:
        SCREAMING_SNAKE_CASE_          =  self.numpy_arrow_extractor().extract_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.python_features_decoder.decode_batch(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.recursive_tensorize(__magic_name__   )
        for column_name in batch:
              SCREAMING_SNAKE_CASE_          =  self._consolidate(batch[column_name]   )
        return batch


