


"""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    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	str      ,       A_	:	int      ,       A_	:	int      ,       A_	:	int      ,       A_	:	int=0.0      ,       A_	:	Optional[int] = None      ,       A_	:	str = "geglu"      ,       A_	:	Optional[int] = None      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = False      ,       A_	:	bool = True      ,       A_	:	str = "layer_norm"      ,       A_	:	bool = False      ,       ):






       '''simple docstring'''


       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(_UpperCamelCase      ,       _UpperCamelCase							)
       elif self.use_ada_layer_norm_zero:
        __lowercase									=	AdaLayerNormZero(_UpperCamelCase      ,       _UpperCamelCase							)
       else:
        __lowercase									=	nn.LayerNorm(_UpperCamelCase      ,       elementwise_affine=_UpperCamelCase							)
       __lowercase									=	Attention(
           query_dim=_UpperCamelCase      ,       heads=_UpperCamelCase      ,       dim_head=_UpperCamelCase      ,       dropout=_UpperCamelCase      ,       bias=_UpperCamelCase      ,       cross_attention_dim=cross_attention_dim if only_cross_attention else None      ,       upcast_attention=_UpperCamelCase      ,       )

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

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

       # let chunk size default to None
       __lowercase									=	None
       __lowercase									=	0
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Optional[int]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	chunk_size
       __lowercase									=	dim






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






       '''simple docstring'''


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

       __lowercase									=	cross_attention_kwargs if cross_attention_kwargs is not None else {}

       __lowercase									=	self.attna(
           _UpperCamelCase      ,       encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None      ,       attention_mask=_UpperCamelCase      ,       **_UpperCamelCase      ,       )
       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(_UpperCamelCase      ,       _UpperCamelCase							) if self.use_ada_layer_norm else self.norma(_UpperCamelCase							)
        )

        __lowercase									=	self.attna(
            _UpperCamelCase      ,       encoder_hidden_states=_UpperCamelCase      ,       attention_mask=_UpperCamelCase      ,       **_UpperCamelCase      ,       )
        __lowercase									=	attn_output + hidden_states

       # 3. Feed-forward
       __lowercase									=	self.norma(_UpperCamelCase							)

       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(_UpperCamelCase							) for hid_slice in norm_hidden_states.chunk(_UpperCamelCase      ,       dim=self._chunk_dim							)]      ,       dim=self._chunk_dim      ,       )
       else:
        __lowercase									=	self.ff(_UpperCamelCase							)

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

       __lowercase									=	ff_output + hidden_states

       return hidden_states






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	str      ,       A_	:	int      ,       A_	:	Optional[int] = None      ,       A_	:	int = 4      ,       A_	:	float = 0.0      ,       A_	:	str = "geglu"      ,       A_	:	bool = False      ,       ):






       '''simple docstring'''


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

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

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






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Tuple							):






       '''simple docstring'''


       for module in self.net:
        __lowercase									=	module(_UpperCamelCase							)
       return hidden_states






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	Dict      ,       A_	:	int      ,       A_	:	int      ,       A_	:	str = "none"							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	nn.Linear(_UpperCamelCase      ,       _UpperCamelCase							)
       __lowercase									=	approximate
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       if gate.device.type != "mps":
        return F.gelu(_UpperCamelCase      ,       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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.proj(_UpperCamelCase							)
       __lowercase									=	self.gelu(_UpperCamelCase							)
       return hidden_states






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	str      ,       A_	:	int      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	nn.Linear(_UpperCamelCase      ,       dim_out * 2							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	str							):






       '''simple docstring'''


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






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	Dict      ,       A_	:	int      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	nn.Linear(_UpperCamelCase      ,       _UpperCamelCase							)






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.proj(_UpperCamelCase							)
       return x * torch.sigmoid(1.7_02 * x							)






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	Optional[int]      ,       A_	:	Union[str, Any]      ,       A_	:	Dict							):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Any      ,       A_	:	List[Any]							):






       '''simple docstring'''


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






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	List[Any]      ,       A_	:	Tuple      ,       A_	:	List[str]							):






       '''simple docstring'''


       super().__init__()

       __lowercase									=	CombinedTimestepLabelEmbeddings(_UpperCamelCase      ,       _UpperCamelCase							)

       __lowercase									=	nn.SiLU()
       __lowercase									=	nn.Linear(_UpperCamelCase      ,       6 * embedding_dim      ,       bias=_UpperCamelCase							)
       __lowercase									=	nn.LayerNorm(_UpperCamelCase      ,       elementwise_affine=_UpperCamelCase      ,       eps=1e-6							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Optional[int]      ,       A_	:	List[Any]      ,       A_	:	List[str]      ,       A_	:	Dict=None							):






       '''simple docstring'''


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






class    lowerCamelCase__						(		nn.Module    ):
      def __init__(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	int      ,       A_	:	int      ,       A_	:	Optional[str] = None      ,       A_	:	float = 1e-5							):






       '''simple docstring'''


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

       if act_fn is None:
        __lowercase									=	None
       else:
        __lowercase									=	get_activation(_UpperCamelCase							)

       __lowercase									=	nn.Linear(_UpperCamelCase      ,       out_dim * 2							)






      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Tuple      ,       A_	:	Dict							):






       '''simple docstring'''


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

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




"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_      (				UpperCamelCase__	: list[float] ):






 """simple docstring"""







 if len(UpperCamelCase__ ) < 2:
  raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
 if any(i <= 0 for i in nums ):
  raise ValueError("""All values must be greater than 0""" )
 __lowercase									=	nums.copy()
 copy_nums.sort()
 return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
