


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "laptop"						) ->   DataFrame:





	"""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(__UpperCamelCase							,					headers=__UpperCamelCase						).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(""","""							,					""""""						)						)
				    )
				    * 1_00						)
			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       :      str	=	'headphones'
	get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



