



"""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				):







       '''simple docstring'''


       def __init__(							self      :  List[str]		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Any=0.0		,    _UpperCAmelCase      :  Any = None		,    _UpperCAmelCase      :  List[str] = "geglu"		,    _UpperCAmelCase      :  List[Any] = None		,    _UpperCAmelCase      :  Optional[Any] = False		,    _UpperCAmelCase      :  List[str] = False		,    _UpperCAmelCase      :  Tuple = False		,    _UpperCAmelCase      :  Tuple = False		,    _UpperCAmelCase      :  Dict = True		,    _UpperCAmelCase      :  Union[str, Any] = "layer_norm"		,    _UpperCAmelCase      :  Optional[Any] = False		,    )			->						int:


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_      			=							only_cross_attention

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

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

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



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Union[str, Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							chunk_size
             UpperCAmelCase_      			=							dim







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


             '''simple docstring'''




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

             UpperCAmelCase_      			=							cross_attention_kwargs if cross_attention_kwargs is not None else {}

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

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

                   UpperCAmelCase_      			=							self.attna(
                       _A		,    encoder_hidden_states=_A		,    attention_mask=_A		,    **_A		,    )
                   UpperCAmelCase_      			=							attn_output + hidden_states

             # 3. Feed-forward
             UpperCAmelCase_      			=							self.norma(_A   )

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

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

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

             UpperCAmelCase_      			=							ff_output + hidden_states

             return hidden_states



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


       def __init__(							self      :  Optional[int]		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  Tuple = None		,    _UpperCAmelCase      :  Optional[int] = 4		,    _UpperCAmelCase      :  Any = 0.0		,    _UpperCAmelCase      :  Tuple = "geglu"		,    _UpperCAmelCase      :  Any = False		,    )			->						int:


             '''simple docstring'''




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

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

             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Any   )			->						Tuple:


             '''simple docstring'''




             for module in self.net:
                   UpperCAmelCase_      			=							module(_A   )
             return hidden_states



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  str = "none"   )			->						str:


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_      			=							nn.Linear(_A		,    _A   )
             UpperCAmelCase_      			=							approximate



       def 					lowercase__    (							self      :  Union[str, Any]		,    _UpperCAmelCase      :  Tuple   )			->						Optional[int]:


             '''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 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  List[Any]   )			->						List[str]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.proj(_A   )
             UpperCAmelCase_      			=							self.gelu(_A   )
             return hidden_states



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


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


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_      			=							nn.Linear(_A		,    dim_out * 2   )



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  Tuple   )			->						int:


             '''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 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  Any   )			->						int:


             '''simple docstring'''




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



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


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


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_      			=							nn.Linear(_A		,    _A   )







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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.proj(_A   )
             return x * torch.sigmoid(1.702 * x   )



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


       def __init__(							self      :  Any		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  Tuple   )			->						Tuple:


             '''simple docstring'''




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







       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  Dict		,    _UpperCAmelCase      :  Union[str, Any]   )			->						Dict:


             '''simple docstring'''




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



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


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


             '''simple docstring'''




             super().__init__()

             UpperCAmelCase_      			=							CombinedTimestepLabelEmbeddings(_A		,    _A   )

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







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


             '''simple docstring'''




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



class 				lowercase__       (   nn.Module				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Dict		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  Any = None		,    _UpperCAmelCase      :  Union[str, Any] = 1e-5   )			->						str:


             '''simple docstring'''




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

             if act_fn is None:
                   UpperCAmelCase_      			=							None
             else:
                   UpperCAmelCase_      			=							get_activation(_A   )

             UpperCAmelCase_      			=							nn.Linear(_A		,    out_dim * 2   )







       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Union[str, Any]   )			->						List[Any]:


             '''simple docstring'''




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

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









"""simple docstring"""





def        a__     (				lowerCAmelCase__ = 2000000				):
      UpperCAmelCase_      			=							[0 for i in range(n + 1				)]
      UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							1

      for i in range(2     ,      int(n**0.5				) + 1				):
            if primality_list[i] == 0:
                  for j in range(i * i     ,      n + 1     ,      lowerCAmelCase__				):
                        UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							0
      for i in range(lowerCAmelCase__				):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


if __name__ == "__main__":
  print(F"{solution() = }")




