
"""simple docstring"""







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()




class  A__       (	unittest.TestCase):
		A_						:       Tuple				   =	StableDiffusionLDMaDPipeline
		A_						:       Tuple				   =	TEXT_TO_IMAGE_PARAMS
		A_						:       List[Any]				   =	TEXT_TO_IMAGE_BATCH_PARAMS
		A_						:       List[Any]				   =	TEXT_TO_IMAGE_IMAGE_PARAMS

		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						Tuple									=      UNetaDConditionModel(
			    block_out_channels=(32, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=4       ,		out_channels=4       ,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,		cross_attention_dim=32       ,		)
			__lowerCAmelCase			:						int									=      DDIMScheduler(
			    beta_start=0.0_0085       ,		beta_end=0.012       ,		beta_schedule='scaled_linear'       ,		clip_sample=_a       ,		set_alpha_to_one=_a       ,		)
			torch.manual_seed(0 )
			__lowerCAmelCase			:						int									=      AutoencoderKL(
			    block_out_channels=[32, 64]       ,		in_channels=6       ,		out_channels=6       ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,		latent_channels=4       ,		)
			torch.manual_seed(0 )
			__lowerCAmelCase			:						List[str]									=      CLIPTextConfig(
			    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=10_00       ,		)
			__lowerCAmelCase			:						Optional[Any]									=      CLIPTextModel(_a )
			__lowerCAmelCase			:						Union[str, Any]									=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

			__lowerCAmelCase			:						List[Any]									=      {
			    "unet": unet,
			    "scheduler": scheduler,
			    "vae": vae,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "safety_checker": None,
			    "feature_extractor": None,
			}
			return components

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=0 ):
			if str(_a ).startswith('mps' ):
				__lowerCAmelCase			:						Tuple									=      torch.manual_seed(_a )
			else:
				__lowerCAmelCase			:						Optional[int]									=      torch.Generator(device=_a ).manual_seed(_a )
			__lowerCAmelCase			:						Optional[Any]									=      {
			    "prompt": "A painting of a squirrel eating a burger",
			    "generator": generator,
			    "num_inference_steps": 2,
			    "guidance_scale": 6.0,
			    "output_type": "numpy",
			}
			return inputs

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      "cpu"  # ensure determinism for the device-dependent torch.Generator

			__lowerCAmelCase			:						Optional[int]									=      self.get_dummy_components()
			__lowerCAmelCase			:						List[str]									=      StableDiffusionLDMaDPipeline(**_a )
			__lowerCAmelCase			:						List[Any]									=      ldmad_pipe.to(_a )
			ldmad_pipe.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						Optional[Any]									=      self.get_dummy_inputs(_a )
			__lowerCAmelCase			:						List[str]									=      ldmad_pipe(**_a )
			__lowerCAmelCase			:						Tuple									=      output.rgb, output.depth

			__lowerCAmelCase			:						Union[str, Any]									=      rgb[0, -3:, -3:, -1]
			__lowerCAmelCase			:						List[str]									=      depth[0, -3:, -1]

			assert rgb.shape == (1, 64, 64, 3)
			assert depth.shape == (1, 64, 64)

			__lowerCAmelCase			:						str									=      np.array(
			    [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262] )
			__lowerCAmelCase			:						Optional[int]									=      np.array([1_03.4_67_27, 85.81_2004, 87.84_9236] )

			assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
			assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      self.get_dummy_components()
			__lowerCAmelCase			:						Tuple									=      StableDiffusionLDMaDPipeline(**_a )
			__lowerCAmelCase			:						Optional[Any]									=      ldmad_pipe.to(_a )
			ldmad_pipe.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						Any									=      self.get_dummy_inputs(_a )
			__lowerCAmelCase			:						List[str]									=      3 * [inputs["prompt"]]

			# forward
			__lowerCAmelCase			:						Union[str, Any]									=      ldmad_pipe(**_a )
			__lowerCAmelCase			:						int									=      output.rgb, output.depth
			__lowerCAmelCase			:						Union[str, Any]									=      rgb_slice_a[0, -3:, -3:, -1]
			__lowerCAmelCase			:						List[str]									=      depth_slice_a[0, -3:, -1]

			__lowerCAmelCase			:						List[str]									=      self.get_dummy_inputs(_a )
			__lowerCAmelCase			:						List[str]									=      3 * [inputs.pop('prompt' )]

			__lowerCAmelCase			:						List[Any]									=      ldmad_pipe.tokenizer(
			    _a       ,		padding='max_length'       ,		max_length=ldmad_pipe.tokenizer.model_max_length       ,		truncation=_a       ,		return_tensors='pt'       ,		)
			__lowerCAmelCase			:						Any									=      text_inputs["input_ids"].to(_a )

			__lowerCAmelCase			:						Optional[int]									=      ldmad_pipe.text_encoder(_a )[0]

			__lowerCAmelCase			:						List[Any]									=      prompt_embeds

			# forward
			__lowerCAmelCase			:						Dict									=      ldmad_pipe(**_a )
			__lowerCAmelCase			:						Optional[int]									=      output.rgb, output.depth
			__lowerCAmelCase			:						str									=      rgb_slice_a[0, -3:, -3:, -1]
			__lowerCAmelCase			:						Union[str, Any]									=      depth_slice_a[0, -3:, -1]

			assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
			assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      "cpu"  # ensure determinism for the device-dependent torch.Generator
			__lowerCAmelCase			:						List[Any]									=      self.get_dummy_components()
			__lowerCAmelCase			:						List[str]									=      PNDMScheduler(skip_prk_steps=_a )
			__lowerCAmelCase			:						Optional[Any]									=      StableDiffusionLDMaDPipeline(**_a )
			__lowerCAmelCase			:						Union[str, Any]									=      ldmad_pipe.to(_a )
			ldmad_pipe.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						str									=      self.get_dummy_inputs(_a )
			__lowerCAmelCase			:						Tuple									=      "french fries"
			__lowerCAmelCase			:						List[Any]									=      ldmad_pipe(**_a       ,		negative_prompt=_a )

			__lowerCAmelCase			:						Union[str, Any]									=      output.rgb, output.depth
			__lowerCAmelCase			:						Dict									=      rgb[0, -3:, -3:, -1]
			__lowerCAmelCase			:						List[str]									=      depth[0, -3:, -1]

			assert rgb.shape == (1, 64, 64, 3)
			assert depth.shape == (1, 64, 64)

			__lowerCAmelCase			:						Dict									=      np.array(
			    [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217] )
			__lowerCAmelCase			:						Dict									=      np.array([1_07.8_47_38, 84.6_2802, 89.96_2135] )
			assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
			assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2




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

		def 							__lowerCamelCase							(      self ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE="cpu"       ,		_SCREAMING_SNAKE_CASE=torch.floataa       ,		_SCREAMING_SNAKE_CASE=0 ):
			__lowerCAmelCase			:						Tuple									=      torch.Generator(device=_a ).manual_seed(_a )
			__lowerCAmelCase			:						Union[str, Any]									=      np.random.RandomState(_a ).standard_normal((1, 4, 64, 64) )
			__lowerCAmelCase			:						Tuple									=      torch.from_numpy(_a ).to(device=_a       ,		dtype=_a )
			__lowerCAmelCase			:						Union[str, Any]									=      {
			    "prompt": "a photograph of an astronaut riding a horse",
			    "latents": latents,
			    "generator": generator,
			    "num_inference_steps": 3,
			    "guidance_scale": 7.5,
			    "output_type": "numpy",
			}
			return inputs







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' )
			__lowerCAmelCase			:						Optional[Any]									=      ldmad_pipe.to(_a )
			ldmad_pipe.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						List[Any]									=      self.get_inputs(_a )
			__lowerCAmelCase			:						List[str]									=      ldmad_pipe(**_a )
			__lowerCAmelCase			:						int									=      output.rgb, output.depth
			__lowerCAmelCase			:						Dict									=      rgb[0, -3:, -3:, -1].flatten()
			__lowerCAmelCase			:						str									=      rgb[0, -3:, -1].flatten()

			assert rgb.shape == (1, 5_12, 5_12, 3)
			assert depth.shape == (1, 5_12, 5_12)

			__lowerCAmelCase			:						Union[str, Any]									=      np.array(
			    [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706] )
			__lowerCAmelCase			:						Any									=      np.array(
			    [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706] )
			assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
			assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3




@nightly
@require_torch_gpu
class  A__       (	unittest.TestCase):

		def 							__lowerCamelCase							(      self ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE="cpu"       ,		_SCREAMING_SNAKE_CASE=torch.floataa       ,		_SCREAMING_SNAKE_CASE=0 ):
			__lowerCAmelCase			:						Union[str, Any]									=      torch.Generator(device=_a ).manual_seed(_a )
			__lowerCAmelCase			:						Tuple									=      np.random.RandomState(_a ).standard_normal((1, 4, 64, 64) )
			__lowerCAmelCase			:						List[str]									=      torch.from_numpy(_a ).to(device=_a       ,		dtype=_a )
			__lowerCAmelCase			:						Optional[Any]									=      {
			    "prompt": "a photograph of an astronaut riding a horse",
			    "latents": latents,
			    "generator": generator,
			    "num_inference_steps": 50,
			    "guidance_scale": 7.5,
			    "output_type": "numpy",
			}
			return inputs

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' ).to(_a )
			ldmad_pipe.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						Tuple									=      self.get_inputs(_a )
			__lowerCAmelCase			:						Dict									=      ldmad_pipe(**_a )
			__lowerCAmelCase			:						Tuple									=      output.rgb, output.depth

			__lowerCAmelCase			:						List[Any]									=      0.49_5586
			__lowerCAmelCase			:						Any									=      0.3379_5515
			__lowerCAmelCase			:						str									=      1_12.4_85_18
			__lowerCAmelCase			:						Tuple									=      98.48_9746
			assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
			assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
			assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
			assert np.abs(expected_depth_std - depth.std() ) < 1E-3







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						str									=      StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c' ).to(_a )
			ldmad_pipe.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						str									=      self.get_inputs(_a )
			__lowerCAmelCase			:						int									=      ldmad_pipe(**_a )
			__lowerCAmelCase			:						Any									=      output.rgb, output.depth

			__lowerCAmelCase			:						Optional[int]									=      0.419_4127
			__lowerCAmelCase			:						Any									=      0.3537_5586
			__lowerCAmelCase			:						Tuple									=      0.563_8502
			__lowerCAmelCase			:						Dict									=      0.3468_6103

			assert rgb.shape == (1, 5_12, 5_12, 3)
			assert depth.shape == (1, 5_12, 5_12, 1)
			assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
			assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
			assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
			assert np.abs(expected_depth_std - depth.std() ) < 1E-3

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")
