






'''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				(  snake_case__				,      snake_case__				,      snake_case__ = False				,      snake_case__ = 1				,      snake_case__ = 1				,      snake_case__ = 10_000				,      ):
      assert len(timesteps.shape				) == 1, "Timesteps should be a 1d-array"

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

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

      # scale embeddings
      __A		 =   scale * emb

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

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

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


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

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


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      if embed_dim % 2 != 0:
            raise ValueError("embed_dim must be divisible by 2"				)

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

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


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      if embed_dim % 2 != 0:
            raise ValueError("embed_dim must be divisible by 2"				)

      __A		 =   np.arange(embed_dim // 2				,      dtype=np.floataa				)
      omega /= embed_dim / 2.0
      __A		 =   1.0 / 10_000**omega  # (D/2,)

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

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

      __A		 =   np.concatenate([emb_sin, emb_cos]				,      axis=1				)  # (M, D)
      return emb






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







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

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

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

           __A		 =   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      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						int       )	->							List[Any]:
           __A		 =   self.proj(lowercase__       )
           if self.flatten:
                 __A		 =   latent.flatten(2       ).transpose(1					,					2       )  # BCHW -> BNC
           if self.layer_norm:
                 __A		 =   self.norm(lowercase__       )
           return latent + self.pos_embed







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







     def __init__(self     :						Dict					,					lowercase__     :						int					,					lowercase__     :						int					,					lowercase__     :						str = "silu"					,					lowercase__     :						int = None					,					lowercase__     :						Optional[str] = None					,					lowercase__     :						int=None					,					)	->							List[str]:
           super().__init__()

           __A		 =   nn.Linear(lowercase__					,					lowercase__       )

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

           __A		 =   get_activation(lowercase__       )

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

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






     def      _UpperCamelCase (self     :						Dict					,					lowercase__     :						str					,					lowercase__     :						int=None       )	->							Optional[Any]:
           if condition is not None:
                 __A		 =   sample + self.cond_proj(lowercase__       )
           __A		 =   self.linear_a(lowercase__       )

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

           __A		 =   self.linear_a(lowercase__       )

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







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







     def __init__(self     :						List[Any]					,					lowercase__     :						int					,					lowercase__     :						bool					,					lowercase__     :						float       )	->							str:
           super().__init__()
           __A		 =   num_channels
           __A		 =   flip_sin_to_cos
           __A		 =   downscale_freq_shift






     def      _UpperCamelCase (self     :						Dict					,					lowercase__     :						str       )	->							List[str]:
           __A		 =   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     :						Dict					,					lowercase__     :						int = 256					,					lowercase__     :						float = 1.0					,					lowercase__     :						Tuple=True					,					lowercase__     :						Tuple=True					,					lowercase__     :						List[str]=False       )	->							Dict:
           super().__init__()
           __A		 =   nn.Parameter(torch.randn(lowercase__       ) * scale					,					requires_grad=lowercase__       )
           __A		 =   log
           __A		 =   flip_sin_to_cos

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

                 __A		 =   self.W






     def      _UpperCamelCase (self     :						Any					,					lowercase__     :						Tuple       )	->							Dict:
           if self.log:
                 __A		 =   torch.log(lowercase__       )

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

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







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







     def __init__(self     :						Any					,					lowercase__     :						int					,					lowercase__     :						int					,					lowercase__     :						int					,					lowercase__     :						int					,					)	->							Optional[Any]:
           super().__init__()

           __A		 =   height
           __A		 =   width
           __A		 =   num_embed
           __A		 =   embed_dim

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






     def      _UpperCamelCase (self     :						Any					,					lowercase__     :						List[Any]       )	->							int:
           __A		 =   self.emb(lowercase__       )

           __A		 =   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
           __A		 =   height_emb.unsqueeze(2       )

           __A		 =   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
           __A		 =   width_emb.unsqueeze(1       )

           __A		 =   height_emb + width_emb

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

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

           return emb







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







     def __init__(self     :						Dict					,					lowercase__     :						List[str]					,					lowercase__     :						Union[str, Any]					,					lowercase__     :						Optional[int]       )	->							Tuple:
           super().__init__()
           __A		 =   dropout_prob > 0
           __A		 =   nn.Embedding(num_classes + use_cfg_embedding					,					lowercase__       )
           __A		 =   num_classes
           __A		 =   dropout_prob






     def      _UpperCamelCase (self     :						Dict					,					lowercase__     :						List[Any]					,					lowercase__     :						Dict=None       )	->							Any:
           if force_drop_ids is None:
                 __A		 =   torch.rand(labels.shape[0]					,					device=labels.device       ) < self.dropout_prob
           else:
                 __A		 =   torch.tensor(force_drop_ids == 1       )
           __A		 =   torch.where(lowercase__					,					self.num_classes					,					lowercase__       )
           return labels






     def      _UpperCamelCase (self     :						List[str]					,					lowercase__     :						torch.LongTensor					,					lowercase__     :						Tuple=None       )	->							str:
           __A		 =   self.dropout_prob > 0
           if (self.training and use_dropout) or (force_drop_ids is not None):
                 __A		 =   self.token_drop(lowercase__					,					lowercase__       )
           __A		 =   self.embedding_table(lowercase__       )
           return embeddings







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







     def __init__(self     :						int					,					lowercase__     :						int = 1024					,					lowercase__     :						int = 768					,					lowercase__     :						int = 768					,					lowercase__     :						int = 10					,					)	->							int:
           super().__init__()

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






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						torch.FloatTensor       )	->							Optional[Any]:
           __A		 =   text_embeds.shape[0]

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

           # text
           __A		 =   self.text_proj(lowercase__       )

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







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







     def __init__(self     :						Any					,					lowercase__     :						int = 768					,					lowercase__     :						int = 768					,					lowercase__     :						int = 32					,					)	->							int:
           super().__init__()

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






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						torch.FloatTensor       )	->							Tuple:
           __A		 =   image_embeds.shape[0]

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







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







     def __init__(self     :						List[str]					,					lowercase__     :						str					,					lowercase__     :						Union[str, Any]					,					lowercase__     :						Dict=0.1       )	->							str:
           super().__init__()

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






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						Dict					,					lowercase__     :						Optional[Any]					,					lowercase__     :						Optional[int]=None       )	->							str:
           __A		 =   self.time_proj(lowercase__       )
           __A		 =   self.timestep_embedder(timesteps_proj.to(dtype=lowercase__       )       )  # (N, D)

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

           __A		 =   timesteps_emb + class_labels  # (N, D)

           return conditioning







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







     def __init__(self     :						Dict					,					lowercase__     :						int					,					lowercase__     :						int					,					lowercase__     :						int = 64       )	->							int:
           super().__init__()
           __A		 =   nn.LayerNorm(lowercase__       )
           __A		 =   AttentionPooling(lowercase__					,					lowercase__       )
           __A		 =   nn.Linear(lowercase__					,					lowercase__       )
           __A		 =   nn.LayerNorm(lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						List[Any]       )	->							List[str]:
           __A		 =   self.norma(lowercase__       )
           __A		 =   self.pool(lowercase__       )
           __A		 =   self.proj(lowercase__       )
           __A		 =   self.norma(lowercase__       )
           return hidden_states







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







     def __init__(self     :						Union[str, Any]					,					lowercase__     :						int = 768					,					lowercase__     :						int = 768					,					lowercase__     :						int = 1536       )	->							List[Any]:
           super().__init__()
           __A		 =   nn.Linear(lowercase__					,					lowercase__       )
           __A		 =   nn.LayerNorm(lowercase__       )
           __A		 =   nn.Linear(lowercase__					,					lowercase__       )






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						torch.FloatTensor       )	->							Union[str, Any]:
           # text
           __A		 =   self.text_proj(lowercase__       )
           __A		 =   self.text_norm(lowercase__       )

           # image
           __A		 =   self.image_proj(lowercase__       )

           return time_image_embeds + time_text_embeds







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







     def __init__(self     :						Dict					,					lowercase__     :						int = 768					,					lowercase__     :						int = 1536       )	->							Optional[int]:
           super().__init__()
           __A		 =   nn.Linear(lowercase__					,					lowercase__       )
           __A		 =   nn.LayerNorm(lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						torch.FloatTensor       )	->							List[str]:
           # image
           __A		 =   self.image_proj(lowercase__       )
           __A		 =   self.image_norm(lowercase__       )
           return time_image_embeds







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







     def __init__(self     :						str					,					lowercase__     :						int = 768					,					lowercase__     :						int = 1536       )	->							Optional[int]:
           super().__init__()
           __A		 =   nn.Linear(lowercase__					,					lowercase__       )
           __A		 =   nn.LayerNorm(lowercase__       )
           __A		 =   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[str]					,					lowercase__     :						torch.FloatTensor					,					lowercase__     :						torch.FloatTensor       )	->							Optional[Any]:
           # image
           __A		 =   self.image_proj(lowercase__       )
           __A		 =   self.image_norm(lowercase__       )
           __A		 =   self.input_hint_block(lowercase__       )
           return time_image_embeds, hint







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







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






     def      _UpperCamelCase (self     :						str					,					lowercase__     :						int       )	->							List[Any]:
           __A ,					__A ,					__A		 =   x.size()

           def shape(lowercase__     :						int       ):
                 # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
                 __A		 =   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)
                 __A		 =   x.transpose(1					,					2       )
                 # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
                 __A		 =   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)
                 __A		 =   x.transpose(1					,					2       )
                 return x

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

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

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

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

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

           return a[:, 0, :]  # cls_token










'''simple docstring'''







def     _UpperCamelCase				(  snake_case__				):
      if len(snake_case__				) <= 1:
            return data
      else:
            return [
                *quick_sort([e for e in data[1:] if e <= data[0]]				),
                data[0],
                *quick_sort([e for e in data[1:] if e > data[0]]				),
            ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


