"""simple docstring"""
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = False  )      ->Any:
  if device_specific:
    seed += AcceleratorState().process_index
  random.seed(_SCREAMING_SNAKE_CASE  )
  np.random.seed(_SCREAMING_SNAKE_CASE  )
  torch.manual_seed(_SCREAMING_SNAKE_CASE  )
  if is_xpu_available():
    torch.xpu.manual_seed_all(_SCREAMING_SNAKE_CASE  )
  elif is_npu_available():
    torch.npu.manual_seed_all(_SCREAMING_SNAKE_CASE  )
  else:
    torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE  )
  # ^^ safe to call this function even if cuda is not available
  if is_tpu_available():
    xm.set_rng_state(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE = None						,	_SCREAMING_SNAKE_CASE = None  )      ->Tuple:
  # Get the proper rng state
  if rng_type == RNGType.TORCH:
    a__:	int     				=      torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    a__:	int     				=      torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    a__:	Optional[Any]     				=      torch.tensor(xm.get_rng_state()  )
  elif rng_type == RNGType.NPU:
    assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
    a__:	int     				=      torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    a__:	List[Any]     				=      torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    a__:	Tuple     				=      generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  a__:	int     				=      AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    a__:	List[Any]     				=      rng_state.to(xm.xla_device()  )
    xm.collective_broadcast([rng_state]  )
    xm.mark_step()
    a__:	Any     				=      rng_state.cpu()
  elif (
      state.distributed_type in CUDA_DISTRIBUTED_TYPES
      or state.distributed_type == DistributedType.MULTI_NPU
      or state.distributed_type == DistributedType.MULTI_XPU
  ):
    a__:	Optional[Any]     				=      rng_state.to(state.device  )
    torch.distributed.broadcast(_SCREAMING_SNAKE_CASE						,	0  )
    a__:	List[str]     				=      rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(_SCREAMING_SNAKE_CASE						,	0  )

  # Set the broadcast rng state
  if rng_type == RNGType.TORCH:
    torch.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.CUDA:
    torch.cuda.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.NPU:
    torch.npu.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.XPU:
    torch.xpu.set_rng_state(_SCREAMING_SNAKE_CASE  )
  elif rng_type == RNGType.XLA:
    xm.set_rng_state(rng_state.item()  )
  elif rng_type == RNGType.GENERATOR:
    generator.set_state(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = None  )      ->Optional[Any]:
  for rng_type in rng_types:
    synchronize_rng_state(RNGType(_SCREAMING_SNAKE_CASE  )						,	generator=_SCREAMING_SNAKE_CASE  )



import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = False      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1_0_0_0_0      ,    ):
       assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"

       lowerCAmelCase__	      =   embedding_dim // 2
       lowerCAmelCase__	      =   -math.log(lowerCAmelCase__      ) * torch.arange(
           start=0      ,    end=lowerCAmelCase__      ,    dtype=torch.floataa      ,    device=timesteps.device      )
       lowerCAmelCase__	      =   exponent / (half_dim - downscale_freq_shift)

       lowerCAmelCase__	      =   torch.exp(lowerCAmelCase__      )
       lowerCAmelCase__	      =   timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       lowerCAmelCase__	      =   scale * emb

       # concat sine and cosine embeddings
       lowerCAmelCase__	      =   torch.cat([torch.sin(lowerCAmelCase__      ), torch.cos(lowerCAmelCase__      )]      ,    dim=-1      )

       # flip sine and cosine embeddings
       if flip_sin_to_cos:
              lowerCAmelCase__	      =   torch.cat([emb[:, half_dim:], emb[:, :half_dim]]      ,    dim=-1      )

       # zero pad
       if embedding_dim % 2 == 1:
              lowerCAmelCase__	      =   torch.nn.functional.pad(lowerCAmelCase__      ,    (0, 1, 0, 0)      )
       return emb






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__=False      ,    lowerCAmelCase__=0      ):
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.meshgrid(lowerCAmelCase__      ,    lowerCAmelCase__      )  # here w goes first
       lowerCAmelCase__	      =   np.stack(lowerCAmelCase__      ,    axis=0      )

       lowerCAmelCase__	      =   grid.reshape([2, 1, grid_size, grid_size]      )
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(lowerCAmelCase__      ,    lowerCAmelCase__      )
       if cls_token and extra_tokens > 0:
              lowerCAmelCase__	      =   np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]      ,    axis=0      )
       return pos_embed






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'      )

       # use half of dimensions to encode grid_h
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,    grid[0]      )  # (H*W, D/2)
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,    grid[1]      )  # (H*W, D/2)

       lowerCAmelCase__	      =   np.concatenate([emb_h, emb_w]      ,    axis=1      )  # (H*W, D)
       return emb






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'      )

       lowerCAmelCase__	      =   np.arange(embed_dim // 2      ,    dtype=np.floataa      )
       omega /= embed_dim / 2.0
       lowerCAmelCase__	      =   1.0 / 1_0_0_0_0**omega  # (D/2,)

       lowerCAmelCase__	      =   pos.reshape(-1      )  # (M,)
       lowerCAmelCase__	      =   np.einsum('m,d->md'      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )  # (M, D/2), outer product

       lowerCAmelCase__	      =   np.sin(lowerCAmelCase__      )  # (M, D/2)
       lowerCAmelCase__	      =   np.cos(lowerCAmelCase__      )  # (M, D/2)

       lowerCAmelCase__	      =   np.concatenate([emb_sin, emb_cos]      ,    axis=1      )  # (M, D)
       return emb
class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    Any=224     ,       lowercase__   :    Any=224     ,       lowercase__   :    int=16     ,       lowercase__   :    List[Any]=3     ,       lowercase__   :    Dict=768     ,       lowercase__   :    Tuple=False     ,       lowercase__   :    Any=True     ,       lowercase__   :    List[str]=True     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   (height // patch_size) * (width // patch_size)
        lowerCAmelCase__	      =   flatten
        lowerCAmelCase__	      =   layer_norm

        lowerCAmelCase__	      =   nn.Convad(
            lowercase__     ,       lowercase__     ,       kernel_size=(patch_size, patch_size)     ,       stride=lowercase__     ,       bias=lowercase__)
        if layer_norm:
               lowerCAmelCase__	      =   nn.LayerNorm(lowercase__     ,       elementwise_affine=lowercase__     ,       eps=1e-6)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_ad_sincos_pos_embed(lowercase__     ,       int(num_patches**0.5))
        self.register_buffer('pos_embed'     ,       torch.from_numpy(lowercase__).float().unsqueeze(0)     ,       persistent=lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.proj(lowercase__)
        if self.flatten:
               lowerCAmelCase__	      =   latent.flatten(2).transpose(1     ,       2)  # BCHW -> BNC
        if self.layer_norm:
               lowerCAmelCase__	      =   self.norm(lowercase__)
        return latent + self.pos_embed






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    str = "silu"     ,       lowercase__   :    int = None     ,       lowercase__   :    Optional[str] = None     ,       lowercase__   :    List[str]=None     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if cond_proj_dim is not None:
               lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       bias=lowercase__)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_activation(lowercase__)

        if out_dim is not None:
               lowerCAmelCase__	      =   out_dim
        else:
               lowerCAmelCase__	      =   time_embed_dim
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if post_act_fn is None:
               lowerCAmelCase__	      =   None
        else:
               lowerCAmelCase__	      =   get_activation(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if condition is not None:
               lowerCAmelCase__	      =   sample + self.cond_proj(lowercase__)
        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.act is not None:
               lowerCAmelCase__	      =   self.act(lowercase__)

        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.post_act is not None:
               lowerCAmelCase__	      =   self.post_act(lowercase__)
        return sample






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    bool     ,       lowercase__   :    float):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   flip_sin_to_cos
        lowerCAmelCase__	      =   downscale_freq_shift




 def 						__snake_case		(     self   :    int     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   get_timestep_embedding(
            lowercase__     ,       self.num_channels     ,       flip_sin_to_cos=self.flip_sin_to_cos     ,       downscale_freq_shift=self.downscale_freq_shift     ,       )
        return t_emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 256     ,       lowercase__   :    float = 1.0     ,       lowercase__   :    str=True     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any=False):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Parameter(torch.randn(lowercase__) * scale     ,       requires_grad=lowercase__)
        lowerCAmelCase__	      =   log
        lowerCAmelCase__	      =   flip_sin_to_cos

        if set_W_to_weight:
               # to delete later
               lowerCAmelCase__	      =   nn.Parameter(torch.randn(lowercase__) * scale     ,       requires_grad=lowercase__)

               lowerCAmelCase__	      =   self.W




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        if self.log:
               lowerCAmelCase__	      =   torch.log(lowercase__)

        lowerCAmelCase__	      =   x[:, None] * self.weight[None, :] * 2 * np.pi

        if self.flip_sin_to_cos:
               lowerCAmelCase__	      =   torch.cat([torch.cos(lowercase__), torch.sin(lowercase__)]     ,       dim=-1)
        else:
               lowerCAmelCase__	      =   torch.cat([torch.sin(lowercase__), torch.cos(lowercase__)]     ,       dim=-1)
        return out






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   height
        lowerCAmelCase__	      =   width
        lowerCAmelCase__	      =   num_embed
        lowerCAmelCase__	      =   embed_dim

        lowerCAmelCase__	      =   nn.Embedding(self.num_embed     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.height     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.width     ,       lowercase__)




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.emb(lowercase__)

        lowerCAmelCase__	      =   self.height_emb(torch.arange(self.height     ,       device=index.device).view(1     ,       self.height))

        # 1 x H x D -> 1 x H x 1 x D
        lowerCAmelCase__	      =   height_emb.unsqueeze(2)

        lowerCAmelCase__	      =   self.width_emb(torch.arange(self.width     ,       device=index.device).view(1     ,       self.width))

        # 1 x W x D -> 1 x 1 x W x D
        lowerCAmelCase__	      =   width_emb.unsqueeze(1)

        lowerCAmelCase__	      =   height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        lowerCAmelCase__	      =   pos_emb.view(1     ,       self.height * self.width     ,       -1)

        lowerCAmelCase__	      =   emb + pos_emb[:, : emb.shape[1], :]

        return emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Any     ,       lowercase__   :    int     ,       lowercase__   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dropout_prob > 0
        lowerCAmelCase__	      =   nn.Embedding(num_classes + use_cfg_embedding     ,       lowercase__)
        lowerCAmelCase__	      =   num_classes
        lowerCAmelCase__	      =   dropout_prob




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    List[Any]     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if force_drop_ids is None:
               lowerCAmelCase__	      =   torch.rand(labels.shape[0]     ,       device=labels.device) < self.dropout_prob
        else:
               lowerCAmelCase__	      =   torch.tensor(force_drop_ids == 1)
        lowerCAmelCase__	      =   torch.where(lowercase__     ,       self.num_classes     ,       lowercase__)
        return labels




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.LongTensor     ,       lowercase__   :    str=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
               lowerCAmelCase__	      =   self.token_drop(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   self.embedding_table(lowercase__)
        return embeddings






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int = 1_024     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 10     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   text_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_text_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)

        # text
        lowerCAmelCase__	      =   self.text_proj(lowercase__)

        return torch.cat([image_text_embeds, text_embeds]     ,       dim=1)






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 32     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   image_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)
        lowerCAmelCase__	      =   self.norm(lowercase__)
        return image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Dict     ,       lowercase__   :    Tuple=0.1):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   Timesteps(num_channels=256     ,       flip_sin_to_cos=lowercase__     ,       downscale_freq_shift=1)
        lowerCAmelCase__	      =   TimestepEmbedding(in_channels=256     ,       time_embed_dim=lowercase__)
        lowerCAmelCase__	      =   LabelEmbedding(lowercase__     ,       lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[str]     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.time_proj(lowercase__)
        lowerCAmelCase__	      =   self.timestep_embedder(timesteps_proj.to(dtype=lowercase__))  # (N, D)

        lowerCAmelCase__	      =   self.class_embedder(lowercase__)  # (N, D)

        lowerCAmelCase__	      =   timesteps_emb + class_labels  # (N, D)

        return conditioning






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int = 64):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   AttentionPooling(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.norma(lowercase__)
        lowerCAmelCase__	      =   self.pool(lowercase__)
        lowerCAmelCase__	      =   self.proj(lowercase__)
        lowerCAmelCase__	      =   self.norma(lowercase__)
        return hidden_states






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.text_proj(lowercase__)
        lowerCAmelCase__	      =   self.text_norm(lowercase__)

        # image
        lowerCAmelCase__	      =   self.image_proj(lowercase__)

        return time_image_embeds + time_text_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        return time_image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Sequential(
            nn.Convad(3     ,       16     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(16     ,       16     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(16     ,       32     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(32     ,       32     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(32     ,       96     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(96     ,       96     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(96     ,       256     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(256     ,       4     ,       3     ,       padding=1)     ,       )




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        lowerCAmelCase__	      =   self.input_hint_block(lowercase__)
        return time_image_embeds, hint






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Optional[int]=None):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dtype
        lowerCAmelCase__	      =   nn.Parameter(torch.randn(1     ,       lowercase__) / embed_dim**0.5)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   embed_dim // self.num_heads




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   x.size()

        def shape(lowercase__   :    Dict):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               lowerCAmelCase__	      =   x.view(lowercase__     ,       -1     ,       self.num_heads     ,       self.dim_per_head)
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               lowerCAmelCase__	      =   x.transpose(1     ,       2)
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               lowerCAmelCase__	      =   x.reshape(bs * self.num_heads     ,       -1     ,       self.dim_per_head)
               # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
               lowerCAmelCase__	      =   x.transpose(1     ,       2)
               return x

        lowerCAmelCase__	      =   x.mean(dim=1     ,       keepdim=lowercase__) + self.positional_embedding.to(x.dtype)
        lowerCAmelCase__	      =   torch.cat([class_token, x]     ,       dim=1)  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        lowerCAmelCase__	      =   shape(self.q_proj(lowercase__))
        # (bs*n_heads, length+class_token_length, dim_per_head)
        lowerCAmelCase__	      =   shape(self.k_proj(lowercase__))
        lowerCAmelCase__	      =   shape(self.v_proj(lowercase__))

        # (bs*n_heads, class_token_length, length+class_token_length):
        lowerCAmelCase__	      =   1 / math.sqrt(math.sqrt(self.dim_per_head))
        lowerCAmelCase__	      =   torch.einsum('bct,bcs->bts'     ,       q * scale     ,       k * scale)  # More stable with f16 than dividing afterwards
        lowerCAmelCase__	      =   torch.softmax(weight.float()     ,       dim=-1).type(weight.dtype)

        # (bs*n_heads, dim_per_head, class_token_length)
        lowerCAmelCase__	      =   torch.einsum('bts,bcs->bct'     ,       lowercase__     ,       lowercase__)

        # (bs, length+1, width)
        lowerCAmelCase__	      =   a.reshape(lowercase__     ,       -1     ,       1).transpose(1     ,       2)

        return a[:, 0, :]  # cls_token
