
'''simple docstring'''

from __future__ import annotations

snake_case_			=     """Muhammad Umer Farooq"""
snake_case_			=     """MIT"""
snake_case_			=     """1.0.0"""
snake_case_			=     """Muhammad Umer Farooq"""
snake_case_			=     """contact@muhammadumerfarooq.me"""
snake_case_			=     """Alpha"""

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

import requests


class 	a__     (				__snake_case	):


	def __init__(self : Dict,					__UpperCAmelCase : List[Any]      )     ->			str:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						list[str]      					= []
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= domain






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Optional[Any]      )     ->			Optional[Any]:

					"""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:
																					SCREAMING_SNAKE_CASE  :						Any      					= parse.urljoin(self.domain,					_lowercase      )
																					self.urls.append(_lowercase      )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str       ):
				return ".".join(get_sub_domain_name(_lowerCamelCase       ).split('''.'''       )[-2:]       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str       ):
				return parse.urlparse(_lowerCamelCase       ).netloc






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str = "https://github.com"       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= get_domain_name(_lowerCamelCase       )

				# Initialize the parser
				SCREAMING_SNAKE_CASE  :						Tuple      					= Parser(_lowerCamelCase       )

				try:
								# Open URL
								SCREAMING_SNAKE_CASE  :						List[Any]      					= requests.get(_lowerCamelCase       )

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

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

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


if __name__ == "__main__":
	snake_case_			=     emails_from_url("""https://github.com""")
	print(f'''{len(emails)} emails found:''')
	print("""\n""".join(sorted(emails)))









'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin


class 	a__     (				_lowercase,			_lowercase	):


	@register_to_config
	def __init__(self : List[str],					__UpperCAmelCase : int = 128,					__UpperCAmelCase : int = 256,					__UpperCAmelCase : float = 2000.0,					__UpperCAmelCase : int = 768,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 12,					__UpperCAmelCase : int = 64,					__UpperCAmelCase : int = 2048,					__UpperCAmelCase : float = 0.1,					)     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()

					SCREAMING_SNAKE_CASE  :						int      					= nn.Sequential(
					    nn.Linear(__UpperCAmelCase,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					nn.Linear(d_model * 4,					d_model * 4,					bias=__UpperCAmelCase      ),					nn.SiLU(),					)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Embedding(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= False

					SCREAMING_SNAKE_CASE  :						str      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Dropout(p=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= nn.ModuleList()
					for lyr_num in range(__UpperCAmelCase      ):
									# FiLM conditional T5 decoder
									SCREAMING_SNAKE_CASE  :						List[Any]      					= DecoderLayer(d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
									self.decoders.append(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Dropout(p=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.mul(query_input.unsqueeze(-1      ),					key_input.unsqueeze(-2      )      )
					return mask.unsqueeze(-3      )






	def 				lowercase__  (self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= get_timestep_embedding(
					    decoder_noise_time * self.config.max_decoder_noise_time,					embedding_dim=self.config.d_model,					max_period=self.config.max_decoder_noise_time,					).to(dtype=self.dtype      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.conditioning_emb(__UpperCAmelCase      ).unsqueeze(1      )

					assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

					SCREAMING_SNAKE_CASE  :						Tuple      					= decoder_input_tokens.shape[1]

					# If we want to use relative positions for audio context, we can just offset
					# this sequence by the length of encodings_and_masks.
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.broadcast_to(
					    torch.arange(__UpperCAmelCase,					device=decoder_input_tokens.device      ),					(batch, seq_length),					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.position_encoding(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.continuous_inputs_projection(__UpperCAmelCase      )
					inputs += position_encodings
					SCREAMING_SNAKE_CASE  :						Dict      					= self.dropout(__UpperCAmelCase      )

					# decoder: No padding present.
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.ones(
					    decoder_input_tokens.shape[:2],					device=decoder_input_tokens.device,					dtype=inputs.dtype      )

					# Translate encoding masks to encoder-decoder masks.
					SCREAMING_SNAKE_CASE  :						Tuple      					= [(x, self.encoder_decoder_mask(__UpperCAmelCase,					__UpperCAmelCase      )) for x, y in encodings_and_masks]

					# cross attend style: concat encodings
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.cat([x[0] for x in encodings_and_encdec_masks],					dim=1      )
					SCREAMING_SNAKE_CASE  :						Dict      					= torch.cat([x[1] for x in encodings_and_encdec_masks],					dim=-1      )

					for lyr in self.decoders:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= lyr(
									    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					encoder_attention_mask=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						str      					= self.decoder_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= self.post_dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.spec_out(__UpperCAmelCase      )
					return spec_out



class 	a__     (				nn.Module	):


	def __init__(self : Union[str, Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Any=1e-6      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.ModuleList()

					# cond self attention: layer 0
					self.layer.append(
					    TaLayerSelfAttentionCond(d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )      )

					# cross attention: layer 1
					self.layer.append(
					    TaLayerCrossAttention(
					        d_model=__UpperCAmelCase,					d_kv=__UpperCAmelCase,					num_heads=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase,					layer_norm_epsilon=__UpperCAmelCase,					)      )

					# Film Cond MLP + dropout: last layer
					self.layer.append(
					    TaLayerFFCond(d_model=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase,					layer_norm_epsilon=__UpperCAmelCase      )      )






	def 				lowercase__  (self : int,					__UpperCAmelCase : int,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[Any]=None,					__UpperCAmelCase : Dict=None,					)     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.layer[0](
					    __UpperCAmelCase,					conditioning_emb=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					if encoder_hidden_states is not None:
									SCREAMING_SNAKE_CASE  :						Any      					= torch.where(encoder_attention_mask > 0,					0,					-1e10      ).to(
									    encoder_hidden_states.dtype      )

									SCREAMING_SNAKE_CASE  :						Any      					= self.layer[1](
									    __UpperCAmelCase,					key_value_states=__UpperCAmelCase,					attention_mask=__UpperCAmelCase,					)

					# Apply Film Conditional Feed Forward layer
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.layer[-1](__UpperCAmelCase,					__UpperCAmelCase      )

					return (hidden_states,)



class 	a__     (				nn.Module	):


	def __init__(self : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaLayerNorm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : List[str]=None,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.layer_norm(__UpperCAmelCase      )

					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.FiLMLayer(__UpperCAmelCase,					__UpperCAmelCase      )

					# Self-attention block
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.attention(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= hidden_states + self.dropout(__UpperCAmelCase      )

					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : int,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Attention(query_dim=__UpperCAmelCase,					heads=__UpperCAmelCase,					dim_head=__UpperCAmelCase,					out_bias=__UpperCAmelCase,					scale_qk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=None,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.layer_norm(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.attention(
					    __UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					attention_mask=attention_mask.squeeze(1      ),					)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return layer_output



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Dict      					= TaDenseGatedActDense(d_model=__UpperCAmelCase,					d_ff=__UpperCAmelCase,					dropout_rate=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= TaFiLMLayer(in_features=d_model * 4,					out_features=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= TaLayerNorm(__UpperCAmelCase,					eps=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= nn.Dropout(__UpperCAmelCase      )






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.layer_norm(__UpperCAmelCase      )
					if conditioning_emb is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.film(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.DenseReluDense(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states + self.dropout(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : str,					__UpperCAmelCase : str      )     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= nn.Linear(__UpperCAmelCase,					__UpperCAmelCase,					bias=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Dropout(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= NewGELUActivation()






	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.act(self.wi_a(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.wi_a(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_gelu * hidden_linear
					SCREAMING_SNAKE_CASE  :						int      					= self.dropout(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.wo(__UpperCAmelCase      )
					return hidden_states



class 	a__     (				nn.Module	):


	def __init__(self : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any=1e-6      )     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Parameter(torch.ones(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= eps






	def 				lowercase__  (self : Any,					__UpperCAmelCase : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= hidden_states.to(torch.floataa      ).pow(2      ).mean(-1,					keepdim=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

					# convert into half-precision if necessary
					if self.weight.dtype in [torch.floataa, torch.bfloataa]:
									SCREAMING_SNAKE_CASE  :						Dict      					= hidden_states.to(self.weight.dtype      )

					return self.weight * hidden_states



class 	a__     (				nn.Module	):


	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : torch.Tensor      )     ->			torch.Tensor:

					"""simple docstring"""

					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.04_4715 * torch.pow(__UpperCAmelCase,					3.0      ))      ))



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[str]      )     ->			List[Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Linear(__UpperCAmelCase,					out_features * 2,					bias=__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Dict,					__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.scale_bias(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= torch.chunk(__UpperCAmelCase,					2,					-1      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= x * (1 + scale) + shift
					return x




