
import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class       _snake_case					(     snake_case				,						snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    IFInpaintingSuperResolutionPipeline
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}				)
			UpperCamelCase__          =    PipelineTesterMixin.required_optional_params - {'latents'}







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self._get_superresolution_dummy_components()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				if str(_a		).startswith("mps"		):
					__magic_name__		:		Dict	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		Union[str, Any]	   =     torch.Generator(device=_a		).manual_seed(_a		)

				__magic_name__		:		Dict	   =     floats_tensor((1, 3, 16, 16)						,   rng=random.Random(_a		)		).to(_a		)
				__magic_name__		:		Dict	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(_a		)		).to(_a		)
				__magic_name__		:		Optional[int]	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(_a		)		).to(_a		)

				__magic_name__		:		Optional[int]	   =     {
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "original_image": original_image,
				    "mask_image": mask_image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "output_type": "numpy",
				}

				return inputs







			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()						,   reason="XFormers attention is only available with CUDA and `xformers` installed"						,   )
			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_save_load_optional_components()







			@unittest.skipIf(torch_device != "cuda"						,   reason="float16 requires CUDA"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2						,   )


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def   lowerCAmelCase_						(					_snake_case      :  str = "laptop"   )				->		DataFrame:







	'''simple docstring'''


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


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