


import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      UpperCamelCase(	unittest.TestCase ):


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''simple docstring'''

											torch.manual_seed(0    )
											__snake_case				=		UNetaDModel(
											    block_out_channels=(3_2, 6_4)  ,     layers_per_block=2  ,     sample_size=3_2  ,     in_channels=3  ,     out_channels=3  ,     down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,     up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,     )
											return model


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Any:




											'''simple docstring'''

											torch.manual_seed(0    )
											__snake_case				=		VQModel(
											    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=3  ,     )
											return model


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Dict:




											'''simple docstring'''

											torch.manual_seed(0    )
											__snake_case				=		CLIPTextConfig(
											    bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=3_2  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
											return CLIPTextModel(SCREAMING_SNAKE_CASE    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.dummy_uncond_unet
											__snake_case				=		DDIMScheduler()
											__snake_case				=		self.dummy_vq_model

											__snake_case				=		LDMPipeline(unet=SCREAMING_SNAKE_CASE  ,     vqvae=SCREAMING_SNAKE_CASE  ,     scheduler=SCREAMING_SNAKE_CASE    )
											ldm.to(SCREAMING_SNAKE_CASE    )
											ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.manual_seed(0    )
											__snake_case				=		ldm(generator=SCREAMING_SNAKE_CASE  ,     num_inference_steps=2  ,     output_type="numpy"    ).images

											__snake_case				=		torch.manual_seed(0    )
											__snake_case				=		ldm(generator=SCREAMING_SNAKE_CASE  ,     num_inference_steps=2  ,     output_type="numpy"  ,     return_dict=SCREAMING_SNAKE_CASE    )[0]

											__snake_case				=		image[0, -3:, -3:, -1]
											__snake_case				=		image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__snake_case				=		np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]    )
											__snake_case				=		1e-2 if torch_device != "mps" else 3e-2

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < tolerance
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < tolerance






@slow
@require_torch
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"    )
											ldm.to(SCREAMING_SNAKE_CASE    )
											ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.manual_seed(0    )
											__snake_case				=		ldm(generator=SCREAMING_SNAKE_CASE  ,     num_inference_steps=5  ,     output_type="numpy"    ).images

											__snake_case				=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)
											__snake_case				=		np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]    )
											__snake_case				=		1e-2 if torch_device != "mps" else 3e-2

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < tolerance









import requests
from bsa import BeautifulSoup





def 						_lowerCAmelCase							(     _lowerCAmelCase = "AAPL"			)    ->						str:


						'''simple docstring'''






						__snake_case				=		F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase			).text   ,						"html.parser"			)
						__snake_case				=		"My(6px) Pos(r) smartphone_Mt(6px)"
						return soup.find("div"   ,						class_=class_			).find("span"			).text


if __name__ == "__main__":
	for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
		print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')





