





'''simple docstring'''


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def       A							(      _UpperCAmelCase   :				torch.Tensor						,_UpperCAmelCase   :				int						,_UpperCAmelCase   :				bool = False						,_UpperCAmelCase   :				float = 1						,_UpperCAmelCase   :				float = 1						,_UpperCAmelCase   :				int = 1_0_0_0_0						,)  ->				List[Any]:


  '''simple docstring'''




  assert len(timesteps.shape					) == 1, "Timesteps should be a 1d-array"

  __lowerCAmelCase		:    Any			     =  embedding_dim // 2
  __lowerCAmelCase		:    Any			     =  -math.log(_UpperCAmelCase					) * torch.arange(
      start=0						,end=_UpperCAmelCase						,dtype=torch.floataa						,device=timesteps.device					)
  __lowerCAmelCase		:    List[Any]			     =  exponent / (half_dim - downscale_freq_shift)

  __lowerCAmelCase		:    int			     =  torch.exp(_UpperCAmelCase					)
  __lowerCAmelCase		:    Tuple			     =  timesteps[:, None].float() * emb[None, :]

  # scale embeddings
  __lowerCAmelCase		:    str			     =  scale * emb

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

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

  # zero pad
  if embedding_dim % 2 == 1:
    __lowerCAmelCase		:    List[Any]			     =  torch.nn.functional.pad(_UpperCAmelCase						,(0, 1, 0, 0)					)
  return emb




def       A							(      _UpperCAmelCase   :				Any						,_UpperCAmelCase   :				List[Any]						,_UpperCAmelCase   :				Optional[int]=False						,_UpperCAmelCase   :				Any=0					)  ->				List[Any]:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[Any]			     =  np.arange(_UpperCAmelCase						,dtype=np.floataa					)
  __lowerCAmelCase		:    Tuple			     =  np.arange(_UpperCAmelCase						,dtype=np.floataa					)
  __lowerCAmelCase		:    Any			     =  np.meshgrid(_UpperCAmelCase						,_UpperCAmelCase					)  # here w goes first
  __lowerCAmelCase		:    str			     =  np.stack(_UpperCAmelCase						,axis=0					)

  __lowerCAmelCase		:    Tuple			     =  grid.reshape([2, 1, grid_size, grid_size]					)
  __lowerCAmelCase		:    Optional[int]			     =  get_ad_sincos_pos_embed_from_grid(_UpperCAmelCase						,_UpperCAmelCase					)
  if cls_token and extra_tokens > 0:
    __lowerCAmelCase		:    Optional[Any]			     =  np.concatenate([np.zeros([extra_tokens, embed_dim]					), pos_embed]						,axis=0					)
  return pos_embed




def       A							(      _UpperCAmelCase   :				int						,_UpperCAmelCase   :				Union[str, Any]					)  ->				Optional[Any]:


  '''simple docstring'''




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

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

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




def       A							(      _UpperCAmelCase   :				Union[str, Any]						,_UpperCAmelCase   :				Optional[Any]					)  ->				Optional[Any]:


  '''simple docstring'''




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

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

  __lowerCAmelCase		:    Union[str, Any]			     =  pos.reshape(-1					)  # (M,)
  __lowerCAmelCase		:    int			     =  np.einsum('m,d->md'						,_UpperCAmelCase						,_UpperCAmelCase					)  # (M, D/2), outer product

  __lowerCAmelCase		:    str			     =  np.sin(_UpperCAmelCase					)  # (M, D/2)
  __lowerCAmelCase		:    Tuple			     =  np.cos(_UpperCAmelCase					)  # (M, D/2)

  __lowerCAmelCase		:    List[Any]			     =  np.concatenate([emb_sin, emb_cos]						,axis=1					)  # (M, D)
  return emb


class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE=2_24     , SCREAMING_SNAKE_CASE=2_24     , SCREAMING_SNAKE_CASE=16     , SCREAMING_SNAKE_CASE=3     , SCREAMING_SNAKE_CASE=7_68     , SCREAMING_SNAKE_CASE=False     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=True     , )				->  Tuple:
        super().__init__()

        __lowerCAmelCase		:    Tuple			     =  (height // patch_size) * (width // patch_size)
        __lowerCAmelCase		:    str			     =  flatten
        __lowerCAmelCase		:    Dict			     =  layer_norm

        __lowerCAmelCase		:    Optional[int]			     =  nn.Convad(
            SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , kernel_size=(patch_size, patch_size)     , stride=SCREAMING_SNAKE_CASE     , bias=SCREAMING_SNAKE_CASE )
        if layer_norm:
          __lowerCAmelCase		:    Tuple			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE     , elementwise_affine=SCREAMING_SNAKE_CASE     , eps=1e-6 )
        else:
          __lowerCAmelCase		:    Any			     =  None

        __lowerCAmelCase		:    Optional[Any]			     =  get_ad_sincos_pos_embed(SCREAMING_SNAKE_CASE     , int(num_patches**0.5 ) )
        self.register_buffer('pos_embed'     , torch.from_numpy(SCREAMING_SNAKE_CASE ).float().unsqueeze(0 )     , persistent=SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Optional[int]:
        __lowerCAmelCase		:    Optional[int]			     =  self.proj(SCREAMING_SNAKE_CASE )
        if self.flatten:
          __lowerCAmelCase		:    List[str]			     =  latent.flatten(2 ).transpose(1     , 2 )  # BCHW -> BNC
        if self.layer_norm:
          __lowerCAmelCase		:    Optional[int]			     =  self.norm(SCREAMING_SNAKE_CASE )
        return latent + self.pos_embed



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = "silu"     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE=None     , )				->  List[str]:
        super().__init__()

        __lowerCAmelCase		:    Dict			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

        if cond_proj_dim is not None:
          __lowerCAmelCase		:    List[Any]			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , bias=SCREAMING_SNAKE_CASE )
        else:
          __lowerCAmelCase		:    int			     =  None

        __lowerCAmelCase		:    List[str]			     =  get_activation(SCREAMING_SNAKE_CASE )

        if out_dim is not None:
          __lowerCAmelCase		:    Dict			     =  out_dim
        else:
          __lowerCAmelCase		:    List[Any]			     =  time_embed_dim
        __lowerCAmelCase		:    int			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

        if post_act_fn is None:
          __lowerCAmelCase		:    Optional[int]			     =  None
        else:
          __lowerCAmelCase		:    Any			     =  get_activation(SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=None )				->  List[str]:
        if condition is not None:
          __lowerCAmelCase		:    List[Any]			     =  sample + self.cond_proj(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  self.linear_a(SCREAMING_SNAKE_CASE )

        if self.act is not None:
          __lowerCAmelCase		:    Tuple			     =  self.act(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    List[str]			     =  self.linear_a(SCREAMING_SNAKE_CASE )

        if self.post_act is not None:
          __lowerCAmelCase		:    Union[str, Any]			     =  self.post_act(SCREAMING_SNAKE_CASE )
        return sample



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  List[Any]:
        super().__init__()
        __lowerCAmelCase		:    List[Any]			     =  num_channels
        __lowerCAmelCase		:    Optional[int]			     =  flip_sin_to_cos
        __lowerCAmelCase		:    Optional[Any]			     =  downscale_freq_shift







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  int:
        __lowerCAmelCase		:    Dict			     =  get_timestep_embedding(
            SCREAMING_SNAKE_CASE     , self.num_channels     , flip_sin_to_cos=self.flip_sin_to_cos     , downscale_freq_shift=self.downscale_freq_shift     , )
        return t_emb



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE = 2_56     , SCREAMING_SNAKE_CASE = 1.0     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=True     , SCREAMING_SNAKE_CASE=False )				->  Union[str, Any]:
        super().__init__()
        __lowerCAmelCase		:    Any			     =  nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE ) * scale     , requires_grad=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  log
        __lowerCAmelCase		:    Any			     =  flip_sin_to_cos

        if set_W_to_weight:
          # to delete later
          __lowerCAmelCase		:    Dict			     =  nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE ) * scale     , requires_grad=SCREAMING_SNAKE_CASE )

          __lowerCAmelCase		:    Dict			     =  self.W







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Tuple:
        if self.log:
          __lowerCAmelCase		:    Dict			     =  torch.log(SCREAMING_SNAKE_CASE )

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

        if self.flip_sin_to_cos:
          __lowerCAmelCase		:    int			     =  torch.cat([torch.cos(SCREAMING_SNAKE_CASE ), torch.sin(SCREAMING_SNAKE_CASE )]     , dim=-1 )
        else:
          __lowerCAmelCase		:    Union[str, Any]			     =  torch.cat([torch.sin(SCREAMING_SNAKE_CASE ), torch.cos(SCREAMING_SNAKE_CASE )]     , dim=-1 )
        return out



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , )				->  int:
        super().__init__()

        __lowerCAmelCase		:    Any			     =  height
        __lowerCAmelCase		:    List[Any]			     =  width
        __lowerCAmelCase		:    Dict			     =  num_embed
        __lowerCAmelCase		:    Dict			     =  embed_dim

        __lowerCAmelCase		:    List[str]			     =  nn.Embedding(self.num_embed     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Dict			     =  nn.Embedding(self.height     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Tuple			     =  nn.Embedding(self.width     , SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Optional[int]:
        __lowerCAmelCase		:    str			     =  self.emb(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    int			     =  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		:    int			     =  height_emb.unsqueeze(2 )

        __lowerCAmelCase		:    List[Any]			     =  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		:    Tuple			     =  width_emb.unsqueeze(1 )

        __lowerCAmelCase		:    Optional[Any]			     =  height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        __lowerCAmelCase		:    List[Any]			     =  pos_emb.view(1     , self.height * self.width     , -1 )

        __lowerCAmelCase		:    int			     =  emb + pos_emb[:, : emb.shape[1], :]

        return emb



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  str:
        super().__init__()
        __lowerCAmelCase		:    Any			     =  dropout_prob > 0
        __lowerCAmelCase		:    Tuple			     =  nn.Embedding(num_classes + use_cfg_embedding     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Any			     =  num_classes
        __lowerCAmelCase		:    Optional[Any]			     =  dropout_prob




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=None )				->  Union[str, Any]:
        if force_drop_ids is None:
          __lowerCAmelCase		:    List[Any]			     =  torch.rand(labels.shape[0]     , device=labels.device ) < self.dropout_prob
        else:
          __lowerCAmelCase		:    str			     =  torch.tensor(force_drop_ids == 1 )
        __lowerCAmelCase		:    str			     =  torch.where(SCREAMING_SNAKE_CASE     , self.num_classes     , SCREAMING_SNAKE_CASE )
        return labels







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=None )				->  List[Any]:
        __lowerCAmelCase		:    List[Any]			     =  self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
          __lowerCAmelCase		:    int			     =  self.token_drop(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Dict			     =  self.embedding_table(SCREAMING_SNAKE_CASE )
        return embeddings



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE = 10_24     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 10     , )				->  Optional[int]:
        super().__init__()

        __lowerCAmelCase		:    List[str]			     =  num_image_text_embeds
        __lowerCAmelCase		:    Optional[Any]			     =  nn.Linear(SCREAMING_SNAKE_CASE     , self.num_image_text_embeds * cross_attention_dim )
        __lowerCAmelCase		:    Tuple			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Dict:
        __lowerCAmelCase		:    Tuple			     =  text_embeds.shape[0]

        # image
        __lowerCAmelCase		:    List[Any]			     =  self.image_embeds(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Union[str, Any]			     =  image_text_embeds.reshape(SCREAMING_SNAKE_CASE     , self.num_image_text_embeds     , -1 )

        # text
        __lowerCAmelCase		:    List[str]			     =  self.text_proj(SCREAMING_SNAKE_CASE )

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



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 32     , )				->  List[str]:
        super().__init__()

        __lowerCAmelCase		:    int			     =  num_image_text_embeds
        __lowerCAmelCase		:    List[str]			     =  nn.Linear(SCREAMING_SNAKE_CASE     , self.num_image_text_embeds * cross_attention_dim )
        __lowerCAmelCase		:    List[Any]			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  List[str]:
        __lowerCAmelCase		:    Union[str, Any]			     =  image_embeds.shape[0]

        # image
        __lowerCAmelCase		:    Optional[int]			     =  self.image_embeds(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  image_embeds.reshape(SCREAMING_SNAKE_CASE     , self.num_image_text_embeds     , -1 )
        __lowerCAmelCase		:    Optional[Any]			     =  self.norm(SCREAMING_SNAKE_CASE )
        return image_embeds



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=0.1 )				->  Optional[Any]:
        super().__init__()

        __lowerCAmelCase		:    str			     =  Timesteps(num_channels=2_56     , flip_sin_to_cos=SCREAMING_SNAKE_CASE     , downscale_freq_shift=1 )
        __lowerCAmelCase		:    Dict			     =  TimestepEmbedding(in_channels=2_56     , time_embed_dim=SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Tuple			     =  LabelEmbedding(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=None )				->  Tuple:
        __lowerCAmelCase		:    Optional[int]			     =  self.time_proj(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[str]			     =  self.timestep_embedder(timesteps_proj.to(dtype=SCREAMING_SNAKE_CASE ) )  # (N, D)

        __lowerCAmelCase		:    str			     =  self.class_embedder(SCREAMING_SNAKE_CASE )  # (N, D)

        __lowerCAmelCase		:    List[Any]			     =  timesteps_emb + class_labels  # (N, D)

        return conditioning



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = 64 )				->  List[Any]:
        super().__init__()
        __lowerCAmelCase		:    Optional[Any]			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Union[str, Any]			     =  AttentionPooling(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Tuple			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  int:
        __lowerCAmelCase		:    Union[str, Any]			     =  self.norma(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Any			     =  self.pool(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[Any]			     =  self.proj(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[str]			     =  self.norma(SCREAMING_SNAKE_CASE )
        return hidden_states



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 15_36 )				->  Optional[Any]:
        super().__init__()
        __lowerCAmelCase		:    str			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[Any]			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Any			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  int:
        # text
        __lowerCAmelCase		:    str			     =  self.text_proj(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  self.text_norm(SCREAMING_SNAKE_CASE )

        # image
        __lowerCAmelCase		:    Dict			     =  self.image_proj(SCREAMING_SNAKE_CASE )

        return time_image_embeds + time_text_embeds



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 15_36 )				->  List[str]:
        super().__init__()
        __lowerCAmelCase		:    Any			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[str]			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  Optional[Any]:
        # image
        __lowerCAmelCase		:    int			     =  self.image_proj(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    str			     =  self.image_norm(SCREAMING_SNAKE_CASE )
        return time_image_embeds



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE = 7_68     , SCREAMING_SNAKE_CASE = 15_36 )				->  List[str]:
        super().__init__()
        __lowerCAmelCase		:    Tuple			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    str			     =  nn.LayerNorm(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  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     , 2_56     , 3     , padding=1     , stride=2 )     , nn.SiLU()     , nn.Convad(2_56     , 4     , 3     , padding=1 )     , )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )				->  Tuple:
        # image
        __lowerCAmelCase		:    Tuple			     =  self.image_proj(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Dict			     =  self.image_norm(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[Any]			     =  self.input_hint_block(SCREAMING_SNAKE_CASE )
        return time_image_embeds, hint



class 		UpperCamelCase__     (       nn.Module							):
      '''simple docstring'''






      def __init__(     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE=None )				->  List[Any]:
        super().__init__()
        __lowerCAmelCase		:    Tuple			     =  dtype
        __lowerCAmelCase		:    int			     =  nn.Parameter(torch.randn(1     , SCREAMING_SNAKE_CASE ) / embed_dim**0.5 )
        __lowerCAmelCase		:    Dict			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , dtype=self.dtype )
        __lowerCAmelCase		:    str			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , dtype=self.dtype )
        __lowerCAmelCase		:    Optional[int]			     =  nn.Linear(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , dtype=self.dtype )
        __lowerCAmelCase		:    Any			     =  num_heads
        __lowerCAmelCase		:    Optional[int]			     =  embed_dim // self.num_heads







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE )				->  str:
        __lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		:    Optional[Any]			     =  x.size()

        def shape(SCREAMING_SNAKE_CASE ):
          # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
          __lowerCAmelCase		:    Any			     =  x.view(SCREAMING_SNAKE_CASE     , -1     , self.num_heads     , self.dim_per_head )
          # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
          __lowerCAmelCase		:    Union[str, Any]			     =  x.transpose(1     , 2 )
          # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
          __lowerCAmelCase		:    Optional[int]			     =  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		:    Dict			     =  x.transpose(1     , 2 )
          return x

        __lowerCAmelCase		:    List[str]			     =  x.mean(dim=1     , keepdim=SCREAMING_SNAKE_CASE ) + self.positional_embedding.to(x.dtype )
        __lowerCAmelCase		:    Optional[int]			     =  torch.cat([class_token, x]     , dim=1 )  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        __lowerCAmelCase		:    Optional[int]			     =  shape(self.q_proj(SCREAMING_SNAKE_CASE ) )
        # (bs*n_heads, length+class_token_length, dim_per_head)
        __lowerCAmelCase		:    List[Any]			     =  shape(self.k_proj(SCREAMING_SNAKE_CASE ) )
        __lowerCAmelCase		:    Optional[Any]			     =  shape(self.v_proj(SCREAMING_SNAKE_CASE ) )

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

        # (bs*n_heads, dim_per_head, class_token_length)
        __lowerCAmelCase		:    Any			     =  torch.einsum('bts,bcs->bct'     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )

        # (bs, length+1, width)
        __lowerCAmelCase		:    Any			     =  a.reshape(SCREAMING_SNAKE_CASE     , -1     , 1 ).transpose(1     , 2 )

        return a[:, 0, :]  # cls_token












'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A_				     =							{
    "configuration_sam": [
        "SAM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SamConfig",
        "SamMaskDecoderConfig",
        "SamPromptEncoderConfig",
        "SamVisionConfig",
    ],
    "processing_sam": ["SamProcessor"],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							[
        "SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SamModel",
        "SamPreTrainedModel",
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							[
        "TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSamModel",
        "TFSamPreTrainedModel",
    ]
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_				     =							["SamImageProcessor"]


if TYPE_CHECKING:
    from .configuration_sam import (
        SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SamConfig,
        SamMaskDecoderConfig,
        SamPromptEncoderConfig,
        SamVisionConfig,
    )
    from .processing_sam import SamProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_sam import SamImageProcessor

else:
    import sys

    A_				     =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





