'''simple docstring'''





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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						int       ,		lowercase__					:						TransformeraDModel       ,		lowercase__					:						AutoencoderKL       ,		lowercase__					:						KarrasDiffusionSchedulers       ,		lowercase__					:						Optional[Dict[int, str]] = None       ,		)							->Tuple:


										'''simple docstring'''




										super().__init__()
										self.register_modules(transformer=lowercase__       ,		vae=lowercase__       ,		scheduler=lowercase__					)

										# create a imagenet -> id dictionary for easier use
										_UpperCamelCase			:			Optional[int]   			=						{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(","					):
																												_UpperCamelCase			:			List[Any]   			=						int(lowercase__					)
																_UpperCamelCase			:			List[str]   			=						dict(sorted(self.labels.items()					)					)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						Union[str, List[str]]					)							->List[int]:


										'''simple docstring'''




										if not isinstance(lowercase__       ,		lowercase__					):
																_UpperCamelCase			:			Dict   			=						list(lowercase__					)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''					)

										return [self.labels[l] for l in label]







				@torch.no_grad()
				def __call__(						self					:						Tuple       ,		lowercase__					:						List[int]       ,		lowercase__					:						float = 4.0       ,		lowercase__					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		lowercase__					:						int = 50       ,		lowercase__					:						Optional[str] = "pil"       ,		lowercase__					:						bool = True       ,		)							->Union[ImagePipelineOutput, Tuple]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						len(lowercase__					)
										_UpperCamelCase			:			List[Any]   			=						self.transformer.config.sample_size
										_UpperCamelCase			:			Dict   			=						self.transformer.config.in_channels

										_UpperCamelCase			:			str   			=						randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,		generator=lowercase__       ,		device=self.device       ,		dtype=self.transformer.dtype       ,		)
										_UpperCamelCase			:			Optional[int]   			=						torch.cat([latents] * 2					) if guidance_scale > 1 else latents

										_UpperCamelCase			:			Tuple   			=						torch.tensor(lowercase__       ,		device=self.device					).reshape(-1					)
										_UpperCamelCase			:			Any   			=						torch.tensor([1_000] * batch_size       ,		device=self.device					)
										_UpperCamelCase			:			str   			=						torch.cat([class_labels, class_null]       ,		0					) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(lowercase__					)

										for t in self.progress_bar(self.scheduler.timesteps					):
																if guidance_scale > 1:
																						_UpperCamelCase			:			Union[str, Any]   			=						latent_model_input[: len(lowercase__					) // 2]
																						_UpperCamelCase			:			Dict   			=						torch.cat([half, half]       ,		dim=0					)
																_UpperCamelCase			:			Any   			=						self.scheduler.scale_model_input(lowercase__       ,		lowercase__					)

																_UpperCamelCase			:			Dict   			=						t
																if not torch.is_tensor(lowercase__					):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						_UpperCamelCase			:			int   			=						latent_model_input.device.type == "mps"
																						if isinstance(lowercase__       ,		lowercase__					):
																												_UpperCamelCase			:			str   			=						torch.floataa if is_mps else torch.floataa
																						else:
																												_UpperCamelCase			:			Union[str, Any]   			=						torch.intaa if is_mps else torch.intaa
																						_UpperCamelCase			:			Optional[int]   			=						torch.tensor([timesteps]       ,		dtype=lowercase__       ,		device=latent_model_input.device					)
																elif len(timesteps.shape					) == 0:
																						_UpperCamelCase			:			Union[str, Any]   			=						timesteps[None].to(latent_model_input.device					)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																_UpperCamelCase			:			Union[str, Any]   			=						timesteps.expand(latent_model_input.shape[0]					)
																# predict noise model_output
																_UpperCamelCase			:			int   			=						self.transformer(
																    lowercase__       ,		timestep=lowercase__       ,		class_labels=lowercase__					).sample

																# perform guidance
																if guidance_scale > 1:
																						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						torch.split(lowercase__       ,		len(lowercase__					) // 2       ,		dim=0					)

																						_UpperCamelCase			:			Union[str, Any]   			=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						_UpperCamelCase			:			Union[str, Any]   			=						torch.cat([half_eps, half_eps]       ,		dim=0					)

																						_UpperCamelCase			:			str   			=						torch.cat([eps, rest]       ,		dim=1					)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						torch.split(lowercase__       ,		lowercase__       ,		dim=1					)
																else:
																						_UpperCamelCase			:			int   			=						noise_pred

																# compute previous image: x_t -> x_t-1
																_UpperCamelCase			:			Optional[Any]   			=						self.scheduler.step(lowercase__       ,		lowercase__       ,		lowercase__					).prev_sample

										if guidance_scale > 1:
																_UpperCamelCase			,       _UpperCamelCase			:			str   			=						latent_model_input.chunk(2       ,		dim=0					)
										else:
																_UpperCamelCase			:			List[Any]   			=						latent_model_input

										_UpperCamelCase			:			List[Any]   			=						1 / self.vae.config.scaling_factor * latents
										_UpperCamelCase			:			int   			=						self.vae.decode(lowercase__					).sample

										_UpperCamelCase			:			List[str]   			=						(samples / 2 + 0.5).clamp(0       ,		1					)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										_UpperCamelCase			:			List[str]   			=						samples.cpu().permute(0       ,		2       ,		3       ,		1					).float().numpy()

										if output_type == "pil":
																_UpperCamelCase			:			Optional[Any]   			=						self.numpy_to_pil(lowercase__					)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=lowercase__					)



'''simple docstring'''





from __future__ import annotations

lowerCAmelCase_	:		Optional[Any]			=     """Muhammad Umer Farooq"""
lowerCAmelCase_	:		str			=     """MIT"""
lowerCAmelCase_	:		Optional[Any]			=     """1.0.0"""
lowerCAmelCase_	:		Union[str, Any]			=     """Muhammad Umer Farooq"""
lowerCAmelCase_	:		Any			=     """contact@muhammadumerfarooq.me"""
lowerCAmelCase_	:		Dict			=     """Alpha"""

import re
from html.parser import HTMLParser
from urllib import parse

import requests




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						List[Any]       ,		lowercase__					:						str					)							->None:


										'''simple docstring'''




										super().__init__()
										_UpperCamelCase			:			list[str]   			=						[]
										_UpperCamelCase			:			int   			=						domain







				def  snake_case__				(						self					:						str       ,		lowercase__					:						str       ,		lowercase__					:						list[tuple[str, str | None]]					)							->None:


										'''simple docstring'''




										if tag == "a":
																# Check the list of defined attributes.
																for name, value in attrs:
																						# If href is defined, and not empty nor # print it.
																						if name == "href" and value != "#" and value != "":
																												# If not already in urls.
																												if value not in self.urls:
																																		_UpperCamelCase			:			Optional[Any]   			=						parse.urljoin(self.domain       ,		lowercase__					)
																																		self.urls.append(lowercase__					)





def 						__A							(     UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						return ".".join(get_sub_domain_name(UpperCAmelCase					).split("."					)[-2:]					)
def 						__A							(     UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						return parse.urlparse(UpperCAmelCase					).netloc
def 						__A							(     UpperCAmelCase = "https://github.com"					)     ->			list[str]:




						'''simple docstring'''
						_UpperCamelCase			:			int   			=						get_domain_name(UpperCAmelCase					)

						# Initialize the parser
						_UpperCamelCase			:			Any   			=						Parser(UpperCAmelCase					)

						try:
												# Open URL
												_UpperCamelCase			:			Union[str, Any]   			=						requests.get(UpperCAmelCase					)

												# pass the raw HTML to the parser to get links
												parser.feed(r.text					)

												# Get links and loop through
												_UpperCamelCase			:			int   			=						set()
												for link in parser.urls:
																		# open URL.
																		# read = requests.get(link)
																		try:
																								_UpperCamelCase			:			Dict   			=						requests.get(UpperCAmelCase					)
																								# Get the valid email.
																								_UpperCamelCase			:			List[str]   			=						re.findall("[a-zA-Z0-9]+@" + domain     ,read.text					)
																								# If not in list then append it.
																								for email in emails:
																														valid_emails.add(UpperCAmelCase					)
																		except ValueError:
																								pass
						except ValueError:
												raise SystemExit(1					)

						# Finally return a sorted list of email addresses with no duplicates.
						return sorted(UpperCAmelCase					)


if __name__ == "__main__":
			lowerCAmelCase_	:		List[str]			=     emails_from_url("""https://github.com""")
			print(f"""{len(emails)} emails found:""")
			print("""\n""".join(sorted(emails)))


