



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase_	       =			False






class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    List[Any]=32)->							List[str]:
						set_seed(0)
						__lowerCAmelCase	   =UNetaDModel(sample_size=snake_case_				,	in_channels=3				,	out_channels=3)
						__lowerCAmelCase	   =torch.optim.SGD(model.parameters()				,	lr=0.0_0_0_1)
						return model, optimizer



	@slow
	def 		UpperCamelCase							(						self							:    List[str])->							Optional[Any]:
						__lowerCAmelCase	   ="""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
						__lowerCAmelCase	   =DDPMScheduler(
						    num_train_timesteps=10_00				,	beta_start=0.0_0_0_1				,	beta_end=0.0_2				,	beta_schedule="""linear"""				,	clip_sample=snake_case_				,	)
						__lowerCAmelCase	   =DDIMScheduler(
						    num_train_timesteps=10_00				,	beta_start=0.0_0_0_1				,	beta_end=0.0_2				,	beta_schedule="""linear"""				,	clip_sample=snake_case_				,	)

						assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

						# shared batches for DDPM and DDIM
						set_seed(0)
						__lowerCAmelCase	   =[torch.randn((4, 3, 32, 32)).clip(-1				,	1).to(snake_case_) for _ in range(4)]
						__lowerCAmelCase	   =[torch.randn((4, 3, 32, 32)).to(snake_case_) for _ in range(4)]
						__lowerCAmelCase	   =[torch.randint(0				,	10_00				,	(4,)).long().to(snake_case_) for _ in range(4)]

						# train with a DDPM scheduler
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.get_model_optimizer(resolution=32)
						model.train().to(snake_case_)
						for i in range(4):
											optimizer.zero_grad()
											__lowerCAmelCase	   =ddpm_scheduler.add_noise(clean_images[i]				,	noise[i]				,	timesteps[i])
											__lowerCAmelCase	   =model(snake_case_				,	timesteps[i]).sample
											__lowerCAmelCase	   =torch.nn.functional.mse_loss(snake_case_				,	noise[i])
											loss.backward()
											optimizer.step()
						del model, optimizer

						# recreate the model and optimizer, and retry with DDIM
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.get_model_optimizer(resolution=32)
						model.train().to(snake_case_)
						for i in range(4):
											optimizer.zero_grad()
											__lowerCAmelCase	   =ddim_scheduler.add_noise(clean_images[i]				,	noise[i]				,	timesteps[i])
											__lowerCAmelCase	   =model(snake_case_				,	timesteps[i]).sample
											__lowerCAmelCase	   =torch.nn.functional.mse_loss(snake_case_				,	noise[i])
											loss.backward()
											optimizer.step()
						del model, optimizer

						self.assertTrue(torch.allclose(snake_case_				,	snake_case_				,	atol=1e-5))
						self.assertTrue(torch.allclose(snake_case_				,	snake_case_				,	atol=1e-5))






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str = "laptop"	)  ->						DataFrame:
					__lowerCAmelCase	   =f"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase	   ={
					    """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""",
					}
					__lowerCAmelCase	   =BeautifulSoup(requests.get(__lowerCamelCase				,   headers=__lowerCamelCase	).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__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




