
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     ,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     ,):
												super().__init__()
												lowercase					=		only_cross_attention

												lowercase					=		(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
												lowercase					=		(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:
																	lowercase					=		AdaLayerNorm(A__     ,A__)
												elif self.use_ada_layer_norm_zero:
																	lowercase					=		AdaLayerNormZero(A__     ,A__)
												else:
																	lowercase					=		nn.LayerNorm(A__     ,elementwise_affine=A__)
												lowercase					=		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.
																	lowercase					=		(
																	    AdaLayerNorm(A__     ,A__)
																	    if self.use_ada_layer_norm
																	    else nn.LayerNorm(A__     ,elementwise_affine=A__)
																	)
																	lowercase					=		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:
																	lowercase					=		None
																	lowercase					=		None

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

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




							def 				A__   (       self     ,A__     ,A__):
												# Sets chunk feed-forward
												lowercase					=		chunk_size
												lowercase					=		dim





							def 				A__   (       self     ,A__     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,):
												# Notice that normalization is always applied before the real computation in the following blocks.
												# 1. Self-Attention
												if self.use_ada_layer_norm:
																	lowercase					=		self.norma(A__     ,A__)
												elif self.use_ada_layer_norm_zero:
																	lowercase			,		lowercase			,		lowercase			,		lowercase			,		lowercase					=		self.norma(
																	    A__     ,A__     ,A__     ,hidden_dtype=hidden_states.dtype)
												else:
																	lowercase					=		self.norma(A__)

												lowercase					=		cross_attention_kwargs if cross_attention_kwargs is not None else {}

												lowercase					=		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:
																	lowercase					=		gate_msa.unsqueeze(1) * attn_output
												lowercase					=		attn_output + hidden_states

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

																	lowercase					=		self.attna(
																	    A__     ,encoder_hidden_states=A__     ,attention_mask=A__     ,**A__     ,)
																	lowercase					=		attn_output + hidden_states

												# 3. Feed-forward
												lowercase					=		self.norma(A__)

												if self.use_ada_layer_norm_zero:
																	lowercase					=		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`.')

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

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

												lowercase					=		ff_output + hidden_states

												return hidden_states







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__ = None     ,A__ = 4     ,A__ = 0.0     ,A__ = "geglu"     ,A__ = False     ,):
												super().__init__()
												lowercase					=		int(dim * mult)
												lowercase					=		dim_out if dim_out is not None else dim

												if activation_fn == "gelu":
																	lowercase					=		GELU(A__     ,A__)
												if activation_fn == "gelu-approximate":
																	lowercase					=		GELU(A__     ,A__     ,approximate='''tanh''')
												elif activation_fn == "geglu":
																	lowercase					=		GEGLU(A__     ,A__)
												elif activation_fn == "geglu-approximate":
																	lowercase					=		ApproximateGELU(A__     ,A__)

												lowercase					=		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__):
												for module in self.net:
																	lowercase					=		module(A__)
												return hidden_states







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__     ,A__ = "none"):
												super().__init__()
												lowercase					=		nn.Linear(A__     ,A__)
												lowercase					=		approximate




							def 				A__   (       self     ,A__):
												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__):
												lowercase					=		self.proj(A__)
												lowercase					=		self.gelu(A__)
												return hidden_states







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__):
												super().__init__()
												lowercase					=		nn.Linear(A__     ,dim_out * 2)




							def 				A__   (       self     ,A__):
												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__):
												lowercase			,		lowercase					=		self.proj(A__).chunk(2     ,dim=-1)
												return hidden_states * self.gelu(A__)







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__):
												super().__init__()
												lowercase					=		nn.Linear(A__     ,A__)





							def 				A__   (       self     ,A__):
												lowercase					=		self.proj(A__)
												return x * torch.sigmoid(1.702 * x)







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__):
												super().__init__()
												lowercase					=		nn.Embedding(A__     ,A__)
												lowercase					=		nn.SiLU()
												lowercase					=		nn.Linear(A__     ,embedding_dim * 2)
												lowercase					=		nn.LayerNorm(A__     ,elementwise_affine=A__)





							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		self.linear(self.silu(self.emb(A__)))
												lowercase			,		lowercase					=		torch.chunk(A__     ,2)
												lowercase					=		self.norm(A__) * (1 + scale) + shift
												return x







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__):
												super().__init__()

												lowercase					=		CombinedTimestepLabelEmbeddings(A__     ,A__)

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





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







class        lowercase	(      nn.Module	):




							def __init__(       self     ,A__     ,A__     ,A__     ,A__ = None     ,A__ = 1E-5):
												super().__init__()
												lowercase					=		num_groups
												lowercase					=		eps

												if act_fn is None:
																	lowercase					=		None
												else:
																	lowercase					=		get_activation(A__)

												lowercase					=		nn.Linear(A__     ,out_dim * 2)





							def 				A__   (       self     ,A__     ,A__):
												if self.act:
																	lowercase					=		self.act(A__)
												lowercase					=		self.linear(A__)
												lowercase					=		emb[:, :, None, None]
												lowercase			,		lowercase					=		emb.chunk(2     ,dim=1)

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






import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionInpaintPipeline` instead."
)




