





'''simple docstring'''




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)


class  SCREAMING_SNAKE_CASE    (nn.Module							):


     def __init__(						self):

          '''simple docstring'''






          super().__init__()
          __A						: Dict      =       nn.Linear(3						,	4)
          __A						: List[str]      =       nn.BatchNormad(4)
          __A						: Optional[Any]      =       nn.Linear(4						,	5)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          return self.lineara(self.batchnorm(self.lineara(A_)))




class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       ModelForTest()
          with TemporaryDirectory() as tmp_dir:
               offload_state_dict(A_						,	model.state_dict())
               __A						: Dict      =       os.path.join(A_						,	'index.json')
               self.assertTrue(os.path.isfile(A_))
               # TODO: add tests on what is inside the index

               for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                    __A						: Dict      =       os.path.join(A_						,	F'{key}.dat')
                    self.assertTrue(os.path.isfile(A_))
                    # TODO: add tests on the fact weights are properly loaded


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       [torch.floataa, torch.floataa, torch.bfloataa]

          for dtype in dtypes:
               __A						: Union[str, Any]      =       torch.randn(2						,	3						,	dtype=A_)
               with TemporaryDirectory() as tmp_dir:
                    __A						: int      =       offload_weight(A_						,	'weight'						,	A_						,	{})
                    __A						: List[Any]      =       os.path.join(A_						,	'weight.dat')
                    self.assertTrue(os.path.isfile(A_))
                    self.assertDictEqual(A_						,	{'weight': {'shape': [2, 3], 'dtype': str(A_).split('.')[1]}})

                    __A						: int      =       load_offloaded_weight(A_						,	index['weight'])
                    self.assertTrue(torch.equal(A_						,	A_))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       ModelForTest()
          __A						: Union[str, Any]      =       model.state_dict()
          __A						: Union[str, Any]      =       {k: v for k, v in state_dict.items() if 'linear2' not in k}
          __A						: Optional[int]      =       {k: v for k, v in state_dict.items() if 'linear2' in k}

          with TemporaryDirectory() as tmp_dir:
               offload_state_dict(A_						,	A_)
               __A						: Tuple      =       OffloadedWeightsLoader(state_dict=A_						,	save_folder=A_)

               # Every key is there with the right value
               self.assertEqual(sorted(A_)						,	sorted(state_dict.keys()))
               for key, param in state_dict.items():
                    self.assertTrue(torch.allclose(A_						,	weight_map[key]))

          __A						: str      =       {k: v for k, v in state_dict.items() if 'weight' in k}
          __A						: int      =       {k: v for k, v in state_dict.items() if 'weight' not in k}

          with TemporaryDirectory() as tmp_dir:
               offload_state_dict(A_						,	A_)
               __A						: Optional[Any]      =       OffloadedWeightsLoader(state_dict=A_						,	save_folder=A_)

               # Every key is there with the right value
               self.assertEqual(sorted(A_)						,	sorted(state_dict.keys()))
               for key, param in state_dict.items():
                    self.assertTrue(torch.allclose(A_						,	weight_map[key]))

          with TemporaryDirectory() as tmp_dir:
               offload_state_dict(A_						,	A_)
               # Duplicates are removed
               __A						: str      =       OffloadedWeightsLoader(state_dict=A_						,	save_folder=A_)

               # Every key is there with the right value
               self.assertEqual(sorted(A_)						,	sorted(state_dict.keys()))
               for key, param in state_dict.items():
                    self.assertTrue(torch.allclose(A_						,	weight_map[key]))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       {'a.1': 0, 'a.10': 1, 'a.2': 2}
          __A						: Any      =       extract_submodules_state_dict(A_						,	['a.1', 'a.2'])
          self.assertDictEqual(A_						,	{'a.1': 0, 'a.2': 2})

          __A						: Optional[int]      =       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
          __A						: Union[str, Any]      =       extract_submodules_state_dict(A_						,	['a.1', 'a.2'])
          self.assertDictEqual(A_						,	{'a.1.a': 0, 'a.2.a': 2})





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str = "laptop"     ):


      '''simple docstring'''



      lowerCamelCase_		     =    f"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase_		     =    {
          '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_		     =    BeautifulSoup(requests.get(lowercase	,							headers=lowercase     ).text     )
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase_		     =    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_		     =    item.ha.text
                  lowerCamelCase_		     =    'https://www.amazon.in/' + item.ha.a['href']
                  lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-offscreen'}     ).text
                  try:
                        lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-icon-alt'}     ).text
                  except AttributeError:
                        lowerCamelCase_		     =    'Not available'
                  try:
                        lowerCamelCase_		     =    (
                            '₹'
                            + item.find(
                                'span'	,							attrs={'class': 'a-price a-text-price'}     ).text.split('₹'     )[1]
                        )
                  except AttributeError:
                        lowerCamelCase_		     =    ''
                  try:
                        lowerCamelCase_		     =    float(
                            (
                                (
                                    float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                                    - float(product_price.strip('₹'     ).replace(','	,							''     )     )
                                )
                                / float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                            )
                            * 1_00     )
                  except ValueError:
                        lowerCamelCase_		     =    float('nan'     )
            except AttributeError:
                  pass
            lowerCamelCase_		     =    [
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase_		     =    ' '
      lowerCamelCase_		     =    ' '
      data_frame.index += 1
      return data_frame


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



