

"""simple docstring"""
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def       UpperCamelCase_					(     lowerCamelCase							:					torch.Tensor     , lowerCamelCase							:					int     , lowerCamelCase							:					bool = False     , lowerCamelCase							:					float = 1     , lowerCamelCase							:					float = 1     , lowerCamelCase							:					int = 1_0000     , )						->     Tuple:



       """simple docstring"""







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

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

       __magic_name__			:		str        =     torch.exp(lowerCamelCase   )
       __magic_name__			:		Optional[Any]        =     timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       __magic_name__			:		int        =     scale * emb

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

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

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







def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					int=False     , lowerCamelCase							:					int=0   )						->     List[str]:



       """simple docstring"""







       __magic_name__			:		Optional[int]        =     np.arange(lowerCamelCase     , dtype=np.floataa   )
       __magic_name__			:		str        =     np.arange(lowerCamelCase     , dtype=np.floataa   )
       __magic_name__			:		Optional[Any]        =     np.meshgrid(lowerCamelCase     , lowerCamelCase   )  # here w goes first
       __magic_name__			:		List[str]        =     np.stack(lowerCamelCase     , axis=0   )

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







def       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					Any   )						->     int:



       """simple docstring"""







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

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

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







def       UpperCamelCase_					(     lowerCamelCase							:					Dict     , lowerCamelCase							:					Union[str, Any]   )						->     Tuple:



       """simple docstring"""







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

       __magic_name__			:		Dict        =     np.arange(embed_dim // 2     , dtype=np.floataa   )
       omega /= embed_dim / 2.0
       __magic_name__			:		int        =     1.0 / 1_0000**omega  # (D/2,)

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

       __magic_name__			:		Union[str, Any]        =     np.sin(lowerCamelCase   )  # (M, D/2)
       __magic_name__			:		Optional[Any]        =     np.cos(lowerCamelCase   )  # (M, D/2)

       __magic_name__			:		Optional[int]        =     np.concatenate([emb_sin, emb_cos]     , axis=1   )  # (M, D)
       return emb

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: List[str]       ,       snake_case					: Union[str, Any]=224       ,       snake_case					: Optional[Any]=224       ,       snake_case					: Optional[Any]=16       ,       snake_case					: Any=3       ,       snake_case					: int=768       ,       snake_case					: Optional[int]=False       ,       snake_case					: Tuple=True       ,       snake_case					: Union[str, Any]=True       ,       )	->			Tuple:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		Tuple        =     (height // patch_size) * (width // patch_size)
         __magic_name__			:		List[str]        =     flatten
         __magic_name__			:		str        =     layer_norm

         __magic_name__			:		Any        =     nn.Convad(
             snake_case       ,       snake_case       ,       kernel_size=(patch_size, patch_size)       ,       stride=snake_case       ,       bias=snake_case      )
         if layer_norm:
                __magic_name__			:		int        =     nn.LayerNorm(snake_case       ,       elementwise_affine=snake_case       ,       eps=1e-6      )
         else:
                __magic_name__			:		Union[str, Any]        =     None

         __magic_name__			:		Union[str, Any]        =     get_ad_sincos_pos_embed(snake_case       ,       int(num_patches**0.5      )      )
         self.register_buffer('''pos_embed'''       ,       torch.from_numpy(snake_case      ).float().unsqueeze(0      )       ,       persistent=snake_case      )




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Tuple      )	->			Any:


         '''simple docstring'''





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

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: str = "silu"       ,       snake_case					: int = None       ,       snake_case					: Optional[str] = None       ,       snake_case					: Any=None       ,       )	->			Dict:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		Any        =     nn.Linear(snake_case       ,       snake_case      )

         if cond_proj_dim is not None:
                __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case       ,       bias=snake_case      )
         else:
                __magic_name__			:		Any        =     None

         __magic_name__			:		str        =     get_activation(snake_case      )

         if out_dim is not None:
                __magic_name__			:		Any        =     out_dim
         else:
                __magic_name__			:		Any        =     time_embed_dim
         __magic_name__			:		Union[str, Any]        =     nn.Linear(snake_case       ,       snake_case      )

         if post_act_fn is None:
                __magic_name__			:		Optional[Any]        =     None
         else:
                __magic_name__			:		Union[str, Any]        =     get_activation(snake_case      )




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


         '''simple docstring'''





         if condition is not None:
                __magic_name__			:		Any        =     sample + self.cond_proj(snake_case      )
         __magic_name__			:		List[Any]        =     self.linear_a(snake_case      )

         if self.act is not None:
                __magic_name__			:		Tuple        =     self.act(snake_case      )

         __magic_name__			:		int        =     self.linear_a(snake_case      )

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

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: int       ,       snake_case					: bool       ,       snake_case					: float      )	->			Union[str, Any]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Optional[int]        =     num_channels
         __magic_name__			:		str        =     flip_sin_to_cos
         __magic_name__			:		Union[str, Any]        =     downscale_freq_shift




  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: List[str]      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     get_timestep_embedding(
             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					: str       ,       snake_case					: int = 256       ,       snake_case					: float = 1.0       ,       snake_case					: List[str]=True       ,       snake_case					: Tuple=True       ,       snake_case					: Union[str, Any]=False      )	->			Tuple:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Tuple        =     nn.Parameter(torch.randn(snake_case      ) * scale       ,       requires_grad=snake_case      )
         __magic_name__			:		Optional[int]        =     log
         __magic_name__			:		List[str]        =     flip_sin_to_cos

         if set_W_to_weight:
                # to delete later
                __magic_name__			:		str        =     nn.Parameter(torch.randn(snake_case      ) * scale       ,       requires_grad=snake_case      )

                __magic_name__			:		List[Any]        =     self.W




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


         '''simple docstring'''





         if self.log:
                __magic_name__			:		Any        =     torch.log(snake_case      )

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

         if self.flip_sin_to_cos:
                __magic_name__			:		List[Any]        =     torch.cat([torch.cos(snake_case      ), torch.sin(snake_case      )]       ,       dim=-1      )
         else:
                __magic_name__			:		List[Any]        =     torch.cat([torch.sin(snake_case      ), torch.cos(snake_case      )]       ,       dim=-1      )
         return out

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: List[str]       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: int       ,       )	->			Union[str, Any]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		List[str]        =     height
         __magic_name__			:		List[Any]        =     width
         __magic_name__			:		Any        =     num_embed
         __magic_name__			:		Optional[Any]        =     embed_dim

         __magic_name__			:		Optional[int]        =     nn.Embedding(self.num_embed       ,       snake_case      )
         __magic_name__			:		List[Any]        =     nn.Embedding(self.height       ,       snake_case      )
         __magic_name__			:		Union[str, Any]        =     nn.Embedding(self.width       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Optional[Any]      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Dict        =     self.emb(snake_case      )

         __magic_name__			:		Tuple        =     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
         __magic_name__			:		Dict        =     height_emb.unsqueeze(2      )

         __magic_name__			:		int        =     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
         __magic_name__			:		int        =     width_emb.unsqueeze(1      )

         __magic_name__			:		Optional[Any]        =     height_emb + width_emb

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

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

         return emb

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: List[Any]       ,       snake_case					: Optional[int]       ,       snake_case					: Dict       ,       snake_case					: Optional[int]      )	->			Dict:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Optional[int]        =     dropout_prob > 0
         __magic_name__			:		Optional[Any]        =     nn.Embedding(num_classes + use_cfg_embedding       ,       snake_case      )
         __magic_name__			:		Any        =     num_classes
         __magic_name__			:		Optional[int]        =     dropout_prob



  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: Optional[Any]       ,       snake_case					: int=None      )	->			Dict:


         '''simple docstring'''





         if force_drop_ids is None:
                __magic_name__			:		Dict        =     torch.rand(labels.shape[0]       ,       device=labels.device      ) < self.dropout_prob
         else:
                __magic_name__			:		Optional[int]        =     torch.tensor(force_drop_ids == 1      )
         __magic_name__			:		Optional[int]        =     torch.where(snake_case       ,       self.num_classes       ,       snake_case      )
         return labels




  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: torch.LongTensor       ,       snake_case					: List[Any]=None      )	->			Union[str, Any]:


         '''simple docstring'''





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

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[int]       ,       snake_case					: int = 1024       ,       snake_case					: int = 768       ,       snake_case					: int = 768       ,       snake_case					: int = 10       ,       )	->			Optional[Any]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		int        =     num_image_text_embeds
         __magic_name__			:		str        =     nn.Linear(snake_case       ,       self.num_image_text_embeds * cross_attention_dim      )
         __magic_name__			:		Optional[Any]        =     nn.Linear(snake_case       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: torch.FloatTensor       ,       snake_case					: torch.FloatTensor      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		Dict        =     text_embeds.shape[0]

         # image
         __magic_name__			:		int        =     self.image_embeds(snake_case      )
         __magic_name__			:		Dict        =     image_text_embeds.reshape(snake_case       ,       self.num_image_text_embeds       ,       -1      )

         # text
         __magic_name__			:		Optional[Any]        =     self.text_proj(snake_case      )

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

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[Any]       ,       snake_case					: int = 768       ,       snake_case					: int = 768       ,       snake_case					: int = 32       ,       )	->			int:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		List[str]        =     num_image_text_embeds
         __magic_name__			:		Union[str, Any]        =     nn.Linear(snake_case       ,       self.num_image_text_embeds * cross_attention_dim      )
         __magic_name__			:		Union[str, Any]        =     nn.LayerNorm(snake_case      )




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: torch.FloatTensor      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     image_embeds.shape[0]

         # image
         __magic_name__			:		Optional[int]        =     self.image_embeds(snake_case      )
         __magic_name__			:		int        =     image_embeds.reshape(snake_case       ,       self.num_image_text_embeds       ,       -1      )
         __magic_name__			:		List[str]        =     self.norm(snake_case      )
         return image_embeds

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Any       ,       snake_case					: Union[str, Any]       ,       snake_case					: Any       ,       snake_case					: Tuple=0.1      )	->			Optional[int]:


         '''simple docstring'''





         super().__init__()

         __magic_name__			:		Optional[int]        =     Timesteps(num_channels=256       ,       flip_sin_to_cos=snake_case       ,       downscale_freq_shift=1      )
         __magic_name__			:		Union[str, Any]        =     TimestepEmbedding(in_channels=256       ,       time_embed_dim=snake_case      )
         __magic_name__			:		Tuple        =     LabelEmbedding(snake_case       ,       snake_case       ,       snake_case      )




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


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.time_proj(snake_case      )
         __magic_name__			:		str        =     self.timestep_embedder(timesteps_proj.to(dtype=snake_case      )      )  # (N, D)

         __magic_name__			:		Optional[Any]        =     self.class_embedder(snake_case      )  # (N, D)

         __magic_name__			:		int        =     timesteps_emb + class_labels  # (N, D)

         return conditioning

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: int       ,       snake_case					: int       ,       snake_case					: int       ,       snake_case					: int = 64      )	->			Dict:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Optional[int]        =     nn.LayerNorm(snake_case      )
         __magic_name__			:		Any        =     AttentionPooling(snake_case       ,       snake_case      )
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		str        =     nn.LayerNorm(snake_case      )




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


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.norma(snake_case      )
         __magic_name__			:		Any        =     self.pool(snake_case      )
         __magic_name__			:		List[Any]        =     self.proj(snake_case      )
         __magic_name__			:		Tuple        =     self.norma(snake_case      )
         return hidden_states

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: int = 768       ,       snake_case					: int = 768       ,       snake_case					: int = 1536      )	->			Optional[Any]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		Any        =     nn.LayerNorm(snake_case      )
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: torch.FloatTensor      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     self.text_proj(snake_case      )
         __magic_name__			:		List[str]        =     self.text_norm(snake_case      )

         # image
         __magic_name__			:		Union[str, Any]        =     self.image_proj(snake_case      )

         return time_image_embeds + time_text_embeds

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Optional[Any]       ,       snake_case					: int = 768       ,       snake_case					: int = 1536      )	->			int:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		int        =     nn.LayerNorm(snake_case      )




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: torch.FloatTensor      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		str        =     self.image_proj(snake_case      )
         __magic_name__			:		List[Any]        =     self.image_norm(snake_case      )
         return time_image_embeds

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Tuple       ,       snake_case					: int = 768       ,       snake_case					: int = 1536      )	->			str:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case      )
         __magic_name__			:		Tuple        =     nn.LayerNorm(snake_case      )
         __magic_name__			:		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       ,       256       ,       3       ,       padding=1       ,       stride=2      )       ,       nn.SiLU()       ,       nn.Convad(256       ,       4       ,       3       ,       padding=1      )       ,       )




  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: torch.FloatTensor      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		str        =     self.image_proj(snake_case      )
         __magic_name__			:		Optional[int]        =     self.image_norm(snake_case      )
         __magic_name__			:		Optional[Any]        =     self.input_hint_block(snake_case      )
         return time_image_embeds, hint

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: Union[str, Any]       ,       snake_case					: str       ,       snake_case					: List[Any]=None      )	->			Union[str, Any]:


         '''simple docstring'''





         super().__init__()
         __magic_name__			:		Dict        =     dtype
         __magic_name__			:		str        =     nn.Parameter(torch.randn(1       ,       snake_case      ) / embed_dim**0.5      )
         __magic_name__			:		Tuple        =     nn.Linear(snake_case       ,       snake_case       ,       dtype=self.dtype      )
         __magic_name__			:		Optional[Any]        =     nn.Linear(snake_case       ,       snake_case       ,       dtype=self.dtype      )
         __magic_name__			:		Dict        =     nn.Linear(snake_case       ,       snake_case       ,       dtype=self.dtype      )
         __magic_name__			:		Optional[Any]        =     num_heads
         __magic_name__			:		str        =     embed_dim // self.num_heads




  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: Any      )	->			int:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__       ,							__magic_name__			:		Tuple        =     x.size()

         def shape(snake_case					: Union[str, Any]      ):
                # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                __magic_name__			:		Any        =     x.view(snake_case       ,       -1       ,       self.num_heads       ,       self.dim_per_head      )
                # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
                __magic_name__			:		Tuple        =     x.transpose(1       ,       2      )
                # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                __magic_name__			:		Tuple        =     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)
                __magic_name__			:		Any        =     x.transpose(1       ,       2      )
                return x

         __magic_name__			:		Union[str, Any]        =     x.mean(dim=1       ,       keepdim=snake_case      ) + self.positional_embedding.to(x.dtype      )
         __magic_name__			:		str        =     torch.cat([class_token, x]       ,       dim=1      )  # (bs, length+1, width)

         # (bs*n_heads, class_token_length, dim_per_head)
         __magic_name__			:		Tuple        =     shape(self.q_proj(snake_case      )      )
         # (bs*n_heads, length+class_token_length, dim_per_head)
         __magic_name__			:		List[str]        =     shape(self.k_proj(snake_case      )      )
         __magic_name__			:		Optional[Any]        =     shape(self.v_proj(snake_case      )      )

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

         # (bs*n_heads, dim_per_head, class_token_length)
         __magic_name__			:		Union[str, Any]        =     torch.einsum('''bts,bcs->bct'''       ,       snake_case       ,       snake_case      )

         # (bs, length+1, width)
         __magic_name__			:		Union[str, Any]        =     a.reshape(snake_case       ,       -1       ,       1      ).transpose(1       ,       2      )

         return a[:, 0, :]  # cls_token



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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: bool = False       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Union[int, float] = 1 / 255       ,       snake_case					: bool = False       ,       snake_case					: bool = True       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = True       ,       **snake_case					: Optional[Any]       ,       )	->			None:


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Any        =     size if size is not None else {'''height''': 346, '''width''': 346}
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		List[str]        =     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Any        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		List[Any]        =     resample
         __magic_name__			:		Tuple        =     do_center_crop
         __magic_name__			:		str        =     crop_size
         __magic_name__			:		Tuple        =     do_rescale
         __magic_name__			:		Optional[Any]        =     rescale_factor
         __magic_name__			:		Optional[Any]        =     rescale_offset
         __magic_name__			:		int        =     do_normalize
         __magic_name__			:		str        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __magic_name__			:		Optional[int]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD
         __magic_name__			:		Optional[int]        =     include_top



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Union[str, Any]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return resize(
             snake_case       ,       size=(size['''height'''], size['''width'''])       ,       resample=snake_case       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: str       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return center_crop(snake_case       ,       size=(size['''height'''], size['''width'''])       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         if offset:
                __magic_name__			:		List[str]        =     (image - 127.5) * scale
                if data_format is not None:
                       __magic_name__			:		str        =     to_channel_dimension_format(snake_case       ,       snake_case      )
                __magic_name__			:		Optional[int]        =     rescaled_image.astype(np.floataa      )
         else:
                __magic_name__			:		str        =     rescale(snake_case       ,       scale=snake_case       ,       data_format=snake_case       ,       **snake_case      )
         return rescaled_image



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: np.ndarray       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





         return normalize(snake_case       ,       mean=snake_case       ,       std=snake_case       ,       data_format=snake_case       ,       **snake_case      )




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: ImageInput       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Any=None       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: bool = None       ,       snake_case					: float = None       ,       snake_case					: bool = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: List[str]       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Any        =     do_resize if do_resize is not None else self.do_resize
         __magic_name__			:		Optional[Any]        =     resample if resample is not None else self.resample
         __magic_name__			:		Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
         __magic_name__			:		Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
         __magic_name__			:		Tuple        =     rescale_factor if rescale_factor is not None else self.rescale_factor
         __magic_name__			:		Dict        =     rescale_offset if rescale_offset is not None else self.rescale_offset
         __magic_name__			:		Union[str, Any]        =     do_normalize if do_normalize is not None else self.do_normalize
         __magic_name__			:		Tuple        =     image_mean if image_mean is not None else self.image_mean
         __magic_name__			:		Dict        =     image_std if image_std is not None else self.image_std
         __magic_name__			:		Union[str, Any]        =     include_top if include_top is not None else self.include_top

         __magic_name__			:		Any        =     size if size is not None else self.size
         __magic_name__			:		List[str]        =     get_size_dict(snake_case      )
         __magic_name__			:		List[Any]        =     crop_size if crop_size is not None else self.crop_size
         __magic_name__			:		Dict        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Optional[Any]        =     make_list_of_images(snake_case      )

         if not valid_images(snake_case      ):
                raise ValueError(
                    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

         if do_resize and size is None or resample is None:
                raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

         if do_center_crop and crop_size is None:
                raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

         if do_rescale and rescale_factor is None:
                raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

         if do_normalize and (image_mean is None or image_std is None):
                raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

         # All transformations expect numpy arrays.
         __magic_name__			:		Union[str, Any]        =     [to_numpy_array(snake_case      ) for image in images]

         if do_resize:
                __magic_name__			:		Optional[int]        =     [self.resize(image=snake_case       ,       size=snake_case       ,       resample=snake_case      ) for image in images]

         if do_center_crop:
                __magic_name__			:		Dict        =     [self.center_crop(image=snake_case       ,       size=snake_case      ) for image in images]

         if do_rescale:
                __magic_name__			:		Any        =     [self.rescale(image=snake_case       ,       scale=snake_case       ,       offset=snake_case      ) for image in images]

         if do_normalize:
                __magic_name__			:		Union[str, Any]        =     [self.normalize(image=snake_case       ,       mean=snake_case       ,       std=snake_case      ) for image in images]

         if include_top:
                __magic_name__			:		Optional[Any]        =     [self.normalize(image=snake_case       ,       mean=[0, 0, 0]       ,       std=snake_case      ) for image in images]

         __magic_name__			:		Any        =     [to_channel_dimension_format(snake_case       ,       snake_case      ) for image in images]

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )
