



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    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a				,			__a				,			__a=0.0				,			__a = None				,			__a = "geglu"				,			__a = None				,			__a = False				,			__a = False				,			__a = False				,			__a = False				,			__a = True				,			__a = "layer_norm"				,			__a = False				,			):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		only_cross_attention

           lowerCamelCase								=		(num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
           lowerCamelCase								=		(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:
                 lowerCamelCase								=		AdaLayerNorm(__a				,			__a    )
           elif self.use_ada_layer_norm_zero:
                 lowerCamelCase								=		AdaLayerNormZero(__a				,			__a    )
           else:
                 lowerCamelCase								=		nn.LayerNorm(__a				,			elementwise_affine=__a    )
           lowerCamelCase								=		Attention(
               query_dim=__a				,			heads=__a				,			dim_head=__a				,			dropout=__a				,			bias=__a				,			cross_attention_dim=cross_attention_dim if only_cross_attention else None				,			upcast_attention=__a				,			)

           # 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.
                 lowerCamelCase								=		(
                     AdaLayerNorm(__a				,			__a    )
                     if self.use_ada_layer_norm
                     else nn.LayerNorm(__a				,			elementwise_affine=__a    )
                 )
                 lowerCamelCase								=		Attention(
                     query_dim=__a				,			cross_attention_dim=cross_attention_dim if not double_self_attention else None				,			heads=__a				,			dim_head=__a				,			dropout=__a				,			bias=__a				,			upcast_attention=__a				,			)  # is self-attn if encoder_hidden_states is none
           else:
                 lowerCamelCase								=		None
                 lowerCamelCase								=		None

           # 3. Feed-forward
           lowerCamelCase								=		nn.LayerNorm(__a				,			elementwise_affine=__a    )
           lowerCamelCase								=		FeedForward(__a				,			dropout=__a				,			activation_fn=__a				,			final_dropout=__a    )

           # let chunk size default to None
           lowerCamelCase								=		None
           lowerCamelCase								=		0







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		chunk_size
           lowerCamelCase								=		dim







     def 						_a      (self				,			__a				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			):






           '''simple docstring'''



           if self.use_ada_layer_norm:
                 lowerCamelCase								=		self.norma(__a				,			__a    )
           elif self.use_ada_layer_norm_zero:
                 lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		self.norma(
                     __a				,			__a				,			__a				,			hidden_dtype=hidden_states.dtype    )
           else:
                 lowerCamelCase								=		self.norma(__a    )

           lowerCamelCase								=		cross_attention_kwargs if cross_attention_kwargs is not None else {}

           lowerCamelCase								=		self.attna(
               __a				,			encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None				,			attention_mask=__a				,			**__a				,			)
           if self.use_ada_layer_norm_zero:
                 lowerCamelCase								=		gate_msa.unsqueeze(1    ) * attn_output
           lowerCamelCase								=		attn_output + hidden_states

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

                 lowerCamelCase								=		self.attna(
                     __a				,			encoder_hidden_states=__a				,			attention_mask=__a				,			**__a				,			)
                 lowerCamelCase								=		attn_output + hidden_states

           # 3. Feed-forward
           lowerCamelCase								=		self.norma(__a    )

           if self.use_ada_layer_norm_zero:
                 lowerCamelCase								=		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`."""    )

                 lowerCamelCase								=		norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
                 lowerCamelCase								=		torch.cat(
                     [self.ff(__a    ) for hid_slice in norm_hidden_states.chunk(__a				,			dim=self._chunk_dim    )]				,			dim=self._chunk_dim				,			)
           else:
                 lowerCamelCase								=		self.ff(__a    )

           if self.use_ada_layer_norm_zero:
                 lowerCamelCase								=		gate_mlp.unsqueeze(1    ) * ff_output

           lowerCamelCase								=		ff_output + hidden_states

           return hidden_states





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a = None				,			__a = 4				,			__a = 0.0				,			__a = "geglu"				,			__a = False				,			):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		int(dim * mult    )
           lowerCamelCase								=		dim_out if dim_out is not None else dim

           if activation_fn == "gelu":
                 lowerCamelCase								=		GELU(__a				,			__a    )
           if activation_fn == "gelu-approximate":
                 lowerCamelCase								=		GELU(__a				,			__a				,			approximate="tanh"    )
           elif activation_fn == "geglu":
                 lowerCamelCase								=		GEGLU(__a				,			__a    )
           elif activation_fn == "geglu-approximate":
                 lowerCamelCase								=		ApproximateGELU(__a				,			__a    )

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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           for module in self.net:
                 lowerCamelCase								=		module(__a    )
           return hidden_states





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a				,			__a = "none"    ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		nn.Linear(__a				,			__a    )
           lowerCamelCase								=		approximate







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           if gate.device.type != "mps":
                 return F.gelu(__a				,			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				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.proj(__a    )
           lowerCamelCase								=		self.gelu(__a    )
           return hidden_states





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a    ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		nn.Linear(__a				,			dim_out * 2    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase			,							lowerCamelCase								=		self.proj(__a    ).chunk(2				,			dim=-1    )
           return hidden_states * self.gelu(__a    )





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a    ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		nn.Linear(__a				,			__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.proj(__a    )
           return x * torch.sigmoid(1.702 * x    )





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a    ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		nn.Embedding(__a				,			__a    )
           lowerCamelCase								=		nn.SiLU()
           lowerCamelCase								=		nn.Linear(__a				,			embedding_dim * 2    )
           lowerCamelCase								=		nn.LayerNorm(__a				,			elementwise_affine=__a    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.linear(self.silu(self.emb(__a    )    )    )
           lowerCamelCase			,							lowerCamelCase								=		torch.chunk(__a				,			2    )
           lowerCamelCase								=		self.norm(__a    ) * (1 + scale) + shift
           return x





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a    ):






           '''simple docstring'''



           super().__init__()

           lowerCamelCase								=		CombinedTimestepLabelEmbeddings(__a				,			__a    )

           lowerCamelCase								=		nn.SiLU()
           lowerCamelCase								=		nn.Linear(__a				,			6 * embedding_dim				,			bias=__a    )
           lowerCamelCase								=		nn.LayerNorm(__a				,			elementwise_affine=__a				,			eps=1E-6    )







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a=None    ):






           '''simple docstring'''



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





class    lowerCamelCase__  (						nn.Module):
     """simple docstring"""



     def __init__(self				,			__a				,			__a				,			__a				,			__a = None				,			__a = 1E-5    ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase								=		num_groups
           lowerCamelCase								=		eps

           if act_fn is None:
                 lowerCamelCase								=		None
           else:
                 lowerCamelCase								=		get_activation(__a    )

           lowerCamelCase								=		nn.Linear(__a				,			out_dim * 2    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           if self.act:
                 lowerCamelCase								=		self.act(__a    )
           lowerCamelCase								=		self.linear(__a    )
           lowerCamelCase								=		emb[:, :, None, None]
           lowerCamelCase			,							lowerCamelCase								=		emb.chunk(2				,			dim=1    )

           lowerCamelCase								=		F.group_norm(__a				,			self.num_groups				,			eps=self.eps    )
           lowerCamelCase								=		x * (1 + scale) + shift
           return x




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

a_						:     Optional[int]	     =		(3, 9, -1_1, 0, 7, 5, 1, -1)
a_						:     str	     =		(4, 6, 2, 0, 8, 1_0, 3, -2)





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		None
           for i in sorted(__a				,			reverse=__a    ):
                 lowerCamelCase								=		Node(__a				,			self.head    )







     def __iter__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.head
           while node:
                 yield node.data
                 lowerCamelCase								=		node.next_node







     def __len__(self    ):






           '''simple docstring'''



           return sum(1 for _ in self    )







     def __str__(self    ):






           '''simple docstring'''



           return " -> ".join([str(__a    ) for node in self]    )



def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     a_						:     Any	     =		SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))