import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory






def 							UpperCAmelCase__(					):
   raise RuntimeError('CUDA out of memory.'       )
class 					__SCREAMING_SNAKE_CASE       (       nn.Module):






       """simple docstring"""

       def __init__( self				):
          super().__init__()
          __snake_case   :			int										=						nn.Linear(3			,		4				)
          __snake_case   :			str										=						nn.BatchNormad(4				)
          __snake_case   :			Optional[int]										=						nn.Linear(4			,		5				)







       def      lowercase_   ( self			,		_UpperCAmelCase				):
          return self.lineara(self.batchnorm(self.lineara(__a				)				)				)





class 					__SCREAMING_SNAKE_CASE       (       unittest.TestCase):






       """simple docstring"""

       def      lowercase_   ( self				):
          __snake_case   :			Optional[Any]										=						[]

          @find_executable_batch_size(starting_batch_size=128				)
          def mock_training_loop_function(_UpperCAmelCase				):
             nonlocal batch_sizes
             batch_sizes.append(__a				)
             if batch_size != 8:
                raise_fake_out_of_memory()

          mock_training_loop_function()
          self.assertListEqual(__a			,		[128, 64, 32, 16, 8]				)







       def      lowercase_   ( self				):
          __snake_case   :			Tuple										=						[]

          @find_executable_batch_size(starting_batch_size=128				)
          def mock_training_loop_function(_UpperCAmelCase			,		_UpperCAmelCase				):
             nonlocal batch_sizes
             batch_sizes.append(__a				)
             if batch_size != 8:
                raise_fake_out_of_memory()
             return batch_size, arga

          __snake_case						,       __snake_case   :			Union[str, Any]										=						mock_training_loop_function('hello'				)
          self.assertListEqual(__a			,		[128, 64, 32, 16, 8]				)
          self.assertListEqual([bs, arga]			,		[8, 'hello']				)







       def      lowercase_   ( self				):



          @find_executable_batch_size(starting_batch_size=0				)
          def mock_training_loop_function(_UpperCAmelCase				):
             pass

          with self.assertRaises(__a				) as cm:
             mock_training_loop_function()
             self.assertIn('No executable batch size found, reached zero.'			,		cm.exception.args[0]				)







       def      lowercase_   ( self				):



          @find_executable_batch_size(starting_batch_size=16				)
          def mock_training_loop_function(_UpperCAmelCase				):
             if batch_size > 0:
                raise_fake_out_of_memory()
             pass

          with self.assertRaises(__a				) as cm:
             mock_training_loop_function()
             self.assertIn('No executable batch size found, reached zero.'			,		cm.exception.args[0]				)







       def      lowercase_   ( self				):



          @find_executable_batch_size(starting_batch_size=128				)
          def mock_training_loop_function(_UpperCAmelCase			,		_UpperCAmelCase			,		_UpperCAmelCase				):
             if batch_size != 8:
                raise raise_fake_out_of_memory()

          with self.assertRaises(__a				) as cm:
             mock_training_loop_function(128			,		'hello'			,		'world'				)
             self.assertIn('Batch size was passed into `f`'			,		cm.exception.args[0]				)
             self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'			,		cm.exception.args[0]				)







       def      lowercase_   ( self				):



          @find_executable_batch_size(starting_batch_size=16				)
          def mock_training_loop_function(_UpperCAmelCase				):
             raise ValueError('Oops, we had an error!'				)

          with self.assertRaises(__a				) as cm:
             mock_training_loop_function()
             self.assertIn('Oops, we had an error!'			,		cm.exception.args[0]				)







       @require_cuda
       def      lowercase_   ( self				):
          __snake_case   :			Optional[int]										=						torch.cuda.memory_allocated()
          __snake_case   :			Union[str, Any]										=						ModelForTest()
          model.cuda()
          self.assertGreater(torch.cuda.memory_allocated()			,		__a				)
          __snake_case   :			Tuple										=						release_memory(__a				)
          self.assertEqual(torch.cuda.memory_allocated()			,		__a				)








from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def      __lowercase(						UpperCAmelCase__ = "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(UpperCAmelCase__ , headers=UpperCAmelCase__			).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("," , ""			)			)
                            )
                            * 100			)
                  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__":
     a_    :  Dict						  =		'headphones'
     get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")