



"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							lowerCamelCase_(   A__,				A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       int										= StableDiffusionInpaintPipeline
					lowercase__   :       Dict										= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					lowercase__   :       Optional[int]										= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowercase__   :       List[str]										= frozenset(
					    []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					lowercase__   :       str										= frozenset([]    )

					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=9				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=3_2				,       attention_head_dim=(2, 4)				,       use_linear_projection=lowerCamelCase__				,       )
									_lowerCamelCase        =				PNDMScheduler(skip_prk_steps=lowerCamelCase__   )
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       sample_size=1_2_8				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       hidden_act='''gelu'''				,       projection_dim=5_1_2				,       )
									_lowerCamelCase        =				CLIPTextModel(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

									_lowerCamelCase        =				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				image.cpu().permute(0				,       2				,       3				,       1   )[0]
									_lowerCamelCase        =				Image.fromarray(np.uinta(lowerCamelCase__   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
									_lowerCamelCase        =				Image.fromarray(np.uinta(image + 4   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
									if str(lowerCamelCase__   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': init_image,
									    '''mask_image''': mask_image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionInpaintPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)
									_lowerCamelCase        =				np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2





					def 			snake_case__      (    self   ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3   )


@slow
@require_torch_gpu
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png'''   )
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
									_lowerCamelCase        =				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
									    '''/yellow_cat_sitting_on_a_park_bench.npy'''   )

									_lowerCamelCase        =				'''stabilityai/stable-diffusion-2-inpainting'''
									_lowerCamelCase        =				StableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase__				,       safety_checker=lowerCamelCase__   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )
									pipe.enable_attention_slicing()

									_lowerCamelCase        =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       mask_image=lowerCamelCase__				,       generator=lowerCamelCase__				,       output_type='''np'''				,       )
									_lowerCamelCase        =				output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image   ).max() < 9e-3

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png'''   )
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
									_lowerCamelCase        =				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
									    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''   )

									_lowerCamelCase        =				'''stabilityai/stable-diffusion-2-inpainting'''
									_lowerCamelCase        =				StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCamelCase__				,       torch_dtype=torch.floataa				,       safety_checker=lowerCamelCase__				,       )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )
									pipe.enable_attention_slicing()

									_lowerCamelCase        =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       mask_image=lowerCamelCase__				,       generator=lowerCamelCase__				,       output_type='''np'''				,       )
									_lowerCamelCase        =				output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image   ).max() < 5e-1





					def 			snake_case__      (    self   ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png'''   )
									_lowerCamelCase        =				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )

									_lowerCamelCase        =				'''stabilityai/stable-diffusion-2-inpainting'''
									_lowerCamelCase        =				PNDMScheduler.from_pretrained(lowerCamelCase__				,       subfolder='''scheduler'''   )
									_lowerCamelCase        =				StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCamelCase__				,       safety_checker=lowerCamelCase__				,       scheduler=lowerCamelCase__				,       torch_dtype=torch.floataa				,       )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )
									pipe.enable_attention_slicing(1   )
									pipe.enable_sequential_cpu_offload()

									_lowerCamelCase        =				'''Face of a yellow cat, high resolution, sitting on a park bench'''

									_lowerCamelCase        =				torch.manual_seed(0   )
									_lowerCamelCase        =				pipe(
									    prompt=lowerCamelCase__				,       image=lowerCamelCase__				,       mask_image=lowerCamelCase__				,       generator=lowerCamelCase__				,       num_inference_steps=2				,       output_type='''np'''				,       )

									_lowerCamelCase        =				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.6_5 * 1_0**9





"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def        lowerCAmelCase_(   lowercase_     :							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(lowercase_						,   headers=lowercase_							).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(''','''						,   ''''''							)							)
																    )
																    * 1_00							)
												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__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''headphones'''
			get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
