




"""simple docstring"""

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings

@maybe_allow_in_graph
class        lowercase      (    nn.Module):







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					Any=0.0      ,     _lowerCamelCase   :					Optional[int] = None      ,     _lowerCamelCase   :					str = "geglu"      ,     _lowerCamelCase   :					Optional[int] = None      ,     _lowerCamelCase   :					bool = False      ,     _lowerCamelCase   :					bool = False      ,     _lowerCamelCase   :					bool = False      ,     _lowerCamelCase   :					bool = False      ,     _lowerCamelCase   :					bool = True      ,     _lowerCamelCase   :					str = "layer_norm"      ,     _lowerCamelCase   :					bool = False      ,     ):



					"""simple docstring"""

					super().__init__()
					A_  :				Optional[Any]	   =		only_cross_attention

					A_  :				int	   =		(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
					A_  :				str	   =		(num_embeds_ada_norm is not None) and norm_type == '''ada_norm'''

					if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
						raise ValueError(
						    F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
						    F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""       )

					# Define 3 blocks. Each block has its own normalization layer.
					# 1. Self-Attn
					if self.use_ada_layer_norm:
						A_  :				Tuple	   =		AdaLayerNorm(_lowerCamelCase      ,     _lowerCamelCase       )
					elif self.use_ada_layer_norm_zero:
						A_  :				Dict	   =		AdaLayerNormZero(_lowerCamelCase      ,     _lowerCamelCase       )
					else:
						A_  :				List[str]	   =		nn.LayerNorm(_lowerCamelCase      ,     elementwise_affine=_lowerCamelCase       )
					A_  :				str	   =		Attention(
					    query_dim=_lowerCamelCase      ,     heads=_lowerCamelCase      ,     dim_head=_lowerCamelCase      ,     dropout=_lowerCamelCase      ,     bias=_lowerCamelCase      ,     cross_attention_dim=cross_attention_dim if only_cross_attention else None      ,     upcast_attention=_lowerCamelCase      ,     )

					# 2. Cross-Attn
					if cross_attention_dim is not None or double_self_attention:
						# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
						# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
						# the second cross attention block.
						A_  :				List[str]	   =		(
						    AdaLayerNorm(_lowerCamelCase      ,     _lowerCamelCase       )
						    if self.use_ada_layer_norm
						    else nn.LayerNorm(_lowerCamelCase      ,     elementwise_affine=_lowerCamelCase       )
						)
						A_  :				Dict	   =		Attention(
						    query_dim=_lowerCamelCase      ,     cross_attention_dim=cross_attention_dim if not double_self_attention else None      ,     heads=_lowerCamelCase      ,     dim_head=_lowerCamelCase      ,     dropout=_lowerCamelCase      ,     bias=_lowerCamelCase      ,     upcast_attention=_lowerCamelCase      ,     )  # is self-attn if encoder_hidden_states is none
					else:
						A_  :				str	   =		None
						A_  :				List[str]	   =		None

					# 3. Feed-forward
					A_  :				Tuple	   =		nn.LayerNorm(_lowerCamelCase      ,     elementwise_affine=_lowerCamelCase       )
					A_  :				int	   =		FeedForward(_lowerCamelCase      ,     dropout=_lowerCamelCase      ,     activation_fn=_lowerCamelCase      ,     final_dropout=_lowerCamelCase       )

					# let chunk size default to None
					A_  :				List[str]	   =		None
					A_  :				str	   =		0







				def 					a_  (							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		chunk_size
					A_  :				Tuple	   =		dim







				def 					a_  (							self   :					Optional[Any]      ,     _lowerCamelCase   :					torch.FloatTensor      ,     _lowerCamelCase   :					Optional[torch.FloatTensor] = None      ,     _lowerCamelCase   :					Optional[torch.FloatTensor] = None      ,     _lowerCamelCase   :					Optional[torch.FloatTensor] = None      ,     _lowerCamelCase   :					Optional[torch.LongTensor] = None      ,     _lowerCamelCase   :					Dict[str, Any] = None      ,     _lowerCamelCase   :					Optional[torch.LongTensor] = None      ,     ):



					"""simple docstring"""

					if self.use_ada_layer_norm:
						A_  :				List[str]	   =		self.norma(_lowerCamelCase      ,     _lowerCamelCase       )
					elif self.use_ada_layer_norm_zero:
						A_   , A_   , A_   , A_   , A_  :				Dict	   =		self.norma(
						    _lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase      ,     hidden_dtype=hidden_states.dtype       )
					else:
						A_  :				int	   =		self.norma(_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		cross_attention_kwargs if cross_attention_kwargs is not None else {}

					A_  :				int	   =		self.attna(
					    _lowerCamelCase      ,     encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None      ,     attention_mask=_lowerCamelCase      ,     **_lowerCamelCase      ,     )
					if self.use_ada_layer_norm_zero:
						A_  :				Tuple	   =		gate_msa.unsqueeze(1       ) * attn_output
					A_  :				str	   =		attn_output + hidden_states

					# 2. Cross-Attention
					if self.attna is not None:
						A_  :				Dict	   =		(
						    self.norma(_lowerCamelCase      ,     _lowerCamelCase       ) if self.use_ada_layer_norm else self.norma(_lowerCamelCase       )
						)

						A_  :				int	   =		self.attna(
						    _lowerCamelCase      ,     encoder_hidden_states=_lowerCamelCase      ,     attention_mask=_lowerCamelCase      ,     **_lowerCamelCase      ,     )
						A_  :				List[str]	   =		attn_output + hidden_states

					# 3. Feed-forward
					A_  :				str	   =		self.norma(_lowerCamelCase       )

					if self.use_ada_layer_norm_zero:
						A_  :				str	   =		norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

					if self._chunk_size is not None:
						# "feed_forward_chunk_size" can be used to save memory
						if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
							raise ValueError(
							    F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""       )

						A_  :				Dict	   =		norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
						A_  :				List[str]	   =		torch.cat(
						    [self.ff(_lowerCamelCase       ) for hid_slice in norm_hidden_states.chunk(_lowerCamelCase      ,     dim=self._chunk_dim       )]      ,     dim=self._chunk_dim      ,     )
					else:
						A_  :				str	   =		self.ff(_lowerCamelCase       )

					if self.use_ada_layer_norm_zero:
						A_  :				Optional[int]	   =		gate_mlp.unsqueeze(1       ) * ff_output

					A_  :				Dict	   =		ff_output + hidden_states

					return hidden_states

class        lowercase      (    nn.Module):







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					Optional[int] = None      ,     _lowerCamelCase   :					int = 4      ,     _lowerCamelCase   :					float = 0.0      ,     _lowerCamelCase   :					str = "geglu"      ,     _lowerCamelCase   :					bool = False      ,     ):



					"""simple docstring"""

					super().__init__()
					A_  :				List[Any]	   =		int(dim * mult       )
					A_  :				int	   =		dim_out if dim_out is not None else dim

					if activation_fn == "gelu":
						A_  :				List[Any]	   =		GELU(_lowerCamelCase      ,     _lowerCamelCase       )
					if activation_fn == "gelu-approximate":
						A_  :				Union[str, Any]	   =		GELU(_lowerCamelCase      ,     _lowerCamelCase      ,     approximate='''tanh'''       )
					elif activation_fn == "geglu":
						A_  :				Union[str, Any]	   =		GEGLU(_lowerCamelCase      ,     _lowerCamelCase       )
					elif activation_fn == "geglu-approximate":
						A_  :				Dict	   =		ApproximateGELU(_lowerCamelCase      ,     _lowerCamelCase       )

					A_  :				List[str]	   =		nn.ModuleList([]       )
					# project in
					self.net.append(_lowerCamelCase       )
					# project dropout
					self.net.append(nn.Dropout(_lowerCamelCase       )       )
					# project out
					self.net.append(nn.Linear(_lowerCamelCase      ,     _lowerCamelCase       )       )
					# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
					if final_dropout:
						self.net.append(nn.Dropout(_lowerCamelCase       )       )







				def 					a_  (							self   :					Any      ,     _lowerCamelCase   :					Optional[Any]       ):



					"""simple docstring"""

					for module in self.net:
						A_  :				Any	   =		module(_lowerCamelCase       )
					return hidden_states

class        lowercase      (    nn.Module):







				def __init__(							self   :					Optional[Any]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					str = "none"       ):



					"""simple docstring"""

					super().__init__()
					A_  :				Tuple	   =		nn.Linear(_lowerCamelCase      ,     _lowerCamelCase       )
					A_  :				Optional[int]	   =		approximate







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					if gate.device.type != "mps":
						return F.gelu(_lowerCamelCase      ,     approximate=self.approximate       )
					# mps: gelu is not implemented for float16
					return F.gelu(gate.to(dtype=torch.floataa       )      ,     approximate=self.approximate       ).to(dtype=gate.dtype       )







				def 					a_  (							self   :					Any      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					A_  :				str	   =		self.proj(_lowerCamelCase       )
					A_  :				List[Any]	   =		self.gelu(_lowerCamelCase       )
					return hidden_states

class        lowercase      (    nn.Module):







				def __init__(							self   :					Optional[int]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					super().__init__()
					A_  :				Union[str, Any]	   =		nn.Linear(_lowerCamelCase      ,     dim_out * 2       )







				def 					a_  (							self   :					List[Any]      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					if gate.device.type != "mps":
						return F.gelu(_lowerCamelCase       )
					# mps: gelu is not implemented for float16
					return F.gelu(gate.to(dtype=torch.floataa       )       ).to(dtype=gate.dtype       )







				def 					a_  (							self   :					str      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_   , A_  :				Union[str, Any]	   =		self.proj(_lowerCamelCase       ).chunk(2      ,     dim=-1       )
					return hidden_states * self.gelu(_lowerCamelCase       )

class        lowercase      (    nn.Module):







				def __init__(							self   :					Dict      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					super().__init__()
					A_  :				Optional[Any]	   =		nn.Linear(_lowerCamelCase      ,     _lowerCamelCase       )







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					List[Any]       ):



					"""simple docstring"""

					A_  :				str	   =		self.proj(_lowerCamelCase       )
					return x * torch.sigmoid(1.702 * x       )

class        lowercase      (    nn.Module):







				def __init__(							self   :					Tuple      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					List[str]       ):



					"""simple docstring"""

					super().__init__()
					A_  :				List[str]	   =		nn.Embedding(_lowerCamelCase      ,     _lowerCamelCase       )
					A_  :				str	   =		nn.SiLU()
					A_  :				List[str]	   =		nn.Linear(_lowerCamelCase      ,     embedding_dim * 2       )
					A_  :				str	   =		nn.LayerNorm(_lowerCamelCase      ,     elementwise_affine=_lowerCamelCase       )







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Optional[Any]      ,     _lowerCamelCase   :					Any       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.linear(self.silu(self.emb(_lowerCamelCase       )       )       )
					A_   , A_  :				List[str]	   =		torch.chunk(_lowerCamelCase      ,     2       )
					A_  :				str	   =		self.norm(_lowerCamelCase       ) * (1 + scale) + shift
					return x

class        lowercase      (    nn.Module):







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					super().__init__()

					A_  :				Optional[int]	   =		CombinedTimestepLabelEmbeddings(_lowerCamelCase      ,     _lowerCamelCase       )

					A_  :				str	   =		nn.SiLU()
					A_  :				List[Any]	   =		nn.Linear(_lowerCamelCase      ,     6 * embedding_dim      ,     bias=_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		nn.LayerNorm(_lowerCamelCase      ,     elementwise_affine=_lowerCamelCase      ,     eps=1E-6       )







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Optional[Any]=None       ):



					"""simple docstring"""

					A_  :				Any	   =		self.linear(self.silu(self.emb(_lowerCamelCase      ,     _lowerCamelCase      ,     hidden_dtype=_lowerCamelCase       )       )       )
					A_   , A_   , A_   , A_   , A_   , A_  :				Any	   =		emb.chunk(6      ,     dim=1       )
					A_  :				str	   =		self.norm(_lowerCamelCase       ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
					return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

class        lowercase      (    nn.Module):







				def __init__(							self   :					Dict      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					Optional[str] = None      ,     _lowerCamelCase   :					float = 1E-5       ):



					"""simple docstring"""

					super().__init__()
					A_  :				int	   =		num_groups
					A_  :				str	   =		eps

					if act_fn is None:
						A_  :				Optional[Any]	   =		None
					else:
						A_  :				str	   =		get_activation(_lowerCamelCase       )

					A_  :				Union[str, Any]	   =		nn.Linear(_lowerCamelCase      ,     out_dim * 2       )







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					if self.act:
						A_  :				Optional[Any]	   =		self.act(_lowerCamelCase       )
					A_  :				List[str]	   =		self.linear(_lowerCamelCase       )
					A_  :				str	   =		emb[:, :, None, None]
					A_   , A_  :				Any	   =		emb.chunk(2      ,     dim=1       )

					A_  :				List[str]	   =		F.group_norm(_lowerCamelCase      ,     self.num_groups      ,     eps=self.eps       )
					A_  :				str	   =		x * (1 + scale) + shift
					return x











"""simple docstring"""

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		len(_UpperCAmelCase	)
	for i in range(length - 1	):
		A_  :				str	   =		i
		for k in range(i + 1  ,      _UpperCAmelCase	):
			if collection[k] < collection[least]:
				A_  :				Tuple	   =		k
		if least != i:
			A_   , A_  :				Optional[int]	   =		(collection[i], collection[least])
	return collection


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       input('Enter numbers separated by a comma:\n').strip()
		_lowerCamelCase  :			List[str]        =       [int(item) for item in user_input.split(',')]
		print(selection_sort(unsorted))





