






"""simple docstring"""







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase            =	logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCamelCase       ( _A )     ->       Dict:
				warnings.warn(
				    """The preprocess method is deprecated and will be removed in a future version. Please"""
				    """ use VaeImageProcessor.preprocess instead"""  ,				_A  ,				)
				if isinstance(_A  ,				torch.Tensor ):
								return image
				elif isinstance(_A  ,				PIL.Image.Image ):
								lowercase    :      int       = [image]

				if isinstance(image[0]  ,				PIL.Image.Image ):
								lowercase      ,      lowercase    :      Any       = image[0].size
								lowercase      ,      lowercase    :      List[Any]       = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

								lowercase    :      Tuple       = [np.array(i.resize((w, h)  ,				resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
								lowercase    :      List[str]       = np.concatenate(_A  ,				axis=0 )
								lowercase    :      List[Any]       = np.array(_A ).astype(np.floataa ) / 255.0
								lowercase    :      List[str]       = image.transpose(0  ,				3  ,				1  ,				2 )
								lowercase    :      Optional[Any]       = 2.0 * image - 1.0
								lowercase    :      Optional[int]       = torch.from_numpy(_A )
				elif isinstance(image[0]  ,				torch.Tensor ):
								lowercase    :      List[str]       = torch.cat(_A  ,				dim=0 )
				return image


def      UpperCamelCase       ( _A )     ->       int:
				if isinstance(_A  ,				torch.Tensor ):
								return mask
				elif isinstance(_A  ,				PIL.Image.Image ):
								lowercase    :      int       = [mask]

				if isinstance(mask[0]  ,				PIL.Image.Image ):
								lowercase      ,      lowercase    :      Union[str, Any]       = mask[0].size
								lowercase      ,      lowercase    :      str       = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
								lowercase    :      Any       = [np.array(m.convert("""L""" ).resize((w, h)  ,				resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
								lowercase    :      Dict       = np.concatenate(_A  ,				axis=0 )
								lowercase    :      Optional[int]       = mask.astype(np.floataa ) / 255.0
								lowercase    :      Union[str, Any]       = 0
								lowercase    :      int       = 1
								lowercase    :      str       = torch.from_numpy(_A )
				elif isinstance(mask[0]  ,				torch.Tensor ):
								lowercase    :      Optional[Any]       = torch.cat(_A  ,				dim=0 )
				return mask


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : UNetaDModel
						_SCREAMING_SNAKE_CASE  : RePaintScheduler






						def __init__(		self  :List[Any]			, __magic_name__  :int			, __magic_name__  :str				)      ->Dict:
										super().__init__()
										self.register_modules(unet=__magic_name__			, scheduler=__magic_name__				)






						@torch.no_grad()
						def __call__(		self  :List[str]			, __magic_name__  :Union[torch.Tensor, PIL.Image.Image]			, __magic_name__  :Union[torch.Tensor, PIL.Image.Image]			, __magic_name__  :int = 250			, __magic_name__  :float = 0.0			, __magic_name__  :int = 10			, __magic_name__  :int = 10			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:
										lowercase    :      str       = image

										lowercase    :      Union[str, Any]       = _preprocess_image(__magic_name__				)
										lowercase    :      Optional[int]       = original_image.to(device=self.device			, dtype=self.unet.dtype				)
										lowercase    :      str       = _preprocess_mask(__magic_name__				)
										lowercase    :      Optional[int]       = mask_image.to(device=self.device			, dtype=self.unet.dtype				)

										lowercase    :      Union[str, Any]       = original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Any       = original_image.shape
										lowercase    :      str       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__			, __magic_name__			, __magic_name__			, self.device				)
										lowercase    :      Dict       = eta

										lowercase    :      str       = self.scheduler.timesteps[0] + 1
										lowercase    :      Any       = generator[0] if isinstance(__magic_name__			, __magic_name__				) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
														if t < t_last:
																		# predict the noise residual
																		lowercase    :      List[str]       = self.unet(__magic_name__			, __magic_name__				).sample
																		# compute previous image: x_t -> x_t-1
																		lowercase    :      List[Any]       = self.scheduler.step(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				).prev_sample

														else:
																		# compute the reverse: x_t-1 -> x_t
																		lowercase    :      List[str]       = self.scheduler.undo_step(__magic_name__			, __magic_name__			, __magic_name__				)
														lowercase    :      Union[str, Any]       = t

										lowercase    :      Union[str, Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      List[str]       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Union[str, Any]       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)








"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def      UpperCamelCase       ( _A = "laptop" )     ->       DataFrame:
				lowercase    :      List[str]       = F"""https://www.amazon.in/laptop/s?k={product}"""
				lowercase    :      str       = {
				    """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""",
				}
				lowercase    :      str       = BeautifulSoup(requests.get(_A  ,				headers=_A ).text )
				# Initialize a Pandas dataframe with the column titles
				lowercase    :      Optional[Any]       = 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:
												lowercase    :      str       = item.ha.text
												lowercase    :      Tuple       = """https://www.amazon.in/""" + item.ha.a["""href"""]
												lowercase    :      Tuple       = item.find("""span"""  ,				attrs={"""class""": """a-offscreen"""} ).text
												try:
																lowercase    :      Any       = item.find("""span"""  ,				attrs={"""class""": """a-icon-alt"""} ).text
												except AttributeError:
																lowercase    :      Tuple       = """Not available"""
												try:
																lowercase    :      Any       = (
																    """₹"""
																    + item.find(
																        """span"""  ,				attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
																)
												except AttributeError:
																lowercase    :      Any       = """"""
												try:
																lowercase    :      List[str]       = float(
																    (
																        (
																            float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																            - float(product_price.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																        )
																        / float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																    )
																    * 100 )
												except ValueError:
																lowercase    :      Tuple       = float("""nan""" )
								except AttributeError:
												pass
								lowercase    :      Union[str, Any]       = [
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				lowercase    :      Tuple       = """ """
				lowercase    :      List[str]       = """ """
				data_frame.index += 1
				return data_frame


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