

"""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__ (					A__						,     A__						,     A__ = False						,     A__ = 1						,     A__ = 1						,     A__ = 1_0000						,     )   ->   Optional[Any]:




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

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

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

   # scale embeddings
   lowerCamelCase__						       =      scale * emb

   # concat sine and cosine embeddings
   lowerCamelCase__						       =      torch.cat([torch.sin(A__ ), torch.cos(A__ )]						,     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(A__						,     (0, 1, 0, 0) )
   return emb



def 						UpperCAmelCase__ (					A__						,     A__						,     A__=False						,     A__=0 )   ->   Any:




   """simple docstring"""
   lowerCamelCase__						       =      np.arange(A__						,     dtype=np.floataa )
   lowerCamelCase__						       =      np.arange(A__						,     dtype=np.floataa )
   lowerCamelCase__						       =      np.meshgrid(A__						,     A__ )  # here w goes first
   lowerCamelCase__						       =      np.stack(A__						,     axis=0 )

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



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   Optional[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
   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 						UpperCAmelCase__ (					A__						,     A__ )   ->   List[str]:




   """simple docstring"""
   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_0000**omega  # (D/2,)

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

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

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


class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__=224     ,    SCREAMING_SNAKE_CASE__=224     ,    SCREAMING_SNAKE_CASE__=16     ,    SCREAMING_SNAKE_CASE__=3     ,    SCREAMING_SNAKE_CASE__=768     ,    SCREAMING_SNAKE_CASE__=False     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=True     ,    ) ->       Tuple:
         super().__init__()

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

         lowerCamelCase__						       =      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__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__     ,    elementwise_affine=SCREAMING_SNAKE_CASE__     ,    eps=1e-6		)
         else:
            lowerCamelCase__						       =      None

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






class 		_A							(      nn.Module       ):





      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[Any]:
         super().__init__()

         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         if cond_proj_dim is not None:
            lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    bias=SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						       =      None

         lowerCamelCase__						       =      get_activation(SCREAMING_SNAKE_CASE__		)

         if out_dim is not None:
            lowerCamelCase__						       =      out_dim
         else:
            lowerCamelCase__						       =      time_embed_dim
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

         if post_act_fn is None:
            lowerCamelCase__						       =      None
         else:
            lowerCamelCase__						       =      get_activation(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       List[str]:
         if condition is not None:
            lowerCamelCase__						       =      sample + self.cond_proj(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.linear_a(SCREAMING_SNAKE_CASE__		)

         if self.act is not None:
            lowerCamelCase__						       =      self.act(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      self.linear_a(SCREAMING_SNAKE_CASE__		)

         if self.post_act is not None:
            lowerCamelCase__						       =      self.post_act(SCREAMING_SNAKE_CASE__		)
         return sample






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Optional[int]:
         super().__init__()
         lowerCamelCase__						       =      num_channels
         lowerCamelCase__						       =      flip_sin_to_cos
         lowerCamelCase__						       =      downscale_freq_shift





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         lowerCamelCase__						       =      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 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = 256     ,    SCREAMING_SNAKE_CASE__ = 1.0     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=False		) ->       str:
         super().__init__()
         lowerCamelCase__						       =      nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__		) * scale     ,    requires_grad=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      log
         lowerCamelCase__						       =      flip_sin_to_cos

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

            lowerCamelCase__						       =      self.W





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Dict:
         if self.log:
            lowerCamelCase__						       =      torch.log(SCREAMING_SNAKE_CASE__		)

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

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






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    ) ->       str:
         super().__init__()

         lowerCamelCase__						       =      height
         lowerCamelCase__						       =      width
         lowerCamelCase__						       =      num_embed
         lowerCamelCase__						       =      embed_dim

         lowerCamelCase__						       =      nn.Embedding(self.num_embed     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.Embedding(self.height     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.Embedding(self.width     ,    SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Dict:
         lowerCamelCase__						       =      self.emb(SCREAMING_SNAKE_CASE__		)

         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       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Dict:
         super().__init__()
         lowerCamelCase__						       =      dropout_prob > 0
         lowerCamelCase__						       =      nn.Embedding(num_classes + use_cfg_embedding     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      num_classes
         lowerCamelCase__						       =      dropout_prob





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       Optional[Any]:
         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(SCREAMING_SNAKE_CASE__     ,    self.num_classes     ,    SCREAMING_SNAKE_CASE__		)
         return labels





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       List[str]:
         lowerCamelCase__						       =      self.dropout_prob > 0
         if (self.training and use_dropout) or (force_drop_ids is not None):
            lowerCamelCase__						       =      self.token_drop(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.embedding_table(SCREAMING_SNAKE_CASE__		)
         return embeddings






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = 1024     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 10     ,    ) ->       str:
         super().__init__()

         lowerCamelCase__						       =      num_image_text_embeds
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    self.num_image_text_embeds * cross_attention_dim		)
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         lowerCamelCase__						       =      text_embeds.shape[0]

         # image
         lowerCamelCase__						       =      self.image_embeds(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      image_text_embeds.reshape(SCREAMING_SNAKE_CASE__     ,    self.num_image_text_embeds     ,    -1		)

         # text
         lowerCamelCase__						       =      self.text_proj(SCREAMING_SNAKE_CASE__		)

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






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 32     ,    ) ->       Union[str, Any]:
         super().__init__()

         lowerCamelCase__						       =      num_image_text_embeds
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    self.num_image_text_embeds * cross_attention_dim		)
         lowerCamelCase__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Optional[Any]:
         lowerCamelCase__						       =      image_embeds.shape[0]

         # image
         lowerCamelCase__						       =      self.image_embeds(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      image_embeds.reshape(SCREAMING_SNAKE_CASE__     ,    self.num_image_text_embeds     ,    -1		)
         lowerCamelCase__						       =      self.norm(SCREAMING_SNAKE_CASE__		)
         return image_embeds






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=0.1		) ->       Dict:
         super().__init__()

         lowerCamelCase__						       =      Timesteps(num_channels=256     ,    flip_sin_to_cos=SCREAMING_SNAKE_CASE__     ,    downscale_freq_shift=1		)
         lowerCamelCase__						       =      TimestepEmbedding(in_channels=256     ,    time_embed_dim=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      LabelEmbedding(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       Optional[int]:
         lowerCamelCase__						       =      self.time_proj(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.timestep_embedder(timesteps_proj.to(dtype=SCREAMING_SNAKE_CASE__		)		)  # (N, D)

         lowerCamelCase__						       =      self.class_embedder(SCREAMING_SNAKE_CASE__		)  # (N, D)

         lowerCamelCase__						       =      timesteps_emb + class_labels  # (N, D)

         return conditioning






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = 64		) ->       Optional[int]:
         super().__init__()
         lowerCamelCase__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      AttentionPooling(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         lowerCamelCase__						       =      self.norma(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.pool(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.proj(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.norma(SCREAMING_SNAKE_CASE__		)
         return hidden_states






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 1536		) ->       str:
         super().__init__()
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       List[Any]:
         # text
         lowerCamelCase__						       =      self.text_proj(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.text_norm(SCREAMING_SNAKE_CASE__		)

         # image
         lowerCamelCase__						       =      self.image_proj(SCREAMING_SNAKE_CASE__		)

         return time_image_embeds + time_text_embeds






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 1536		) ->       List[str]:
         super().__init__()
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       str:
         # image
         lowerCamelCase__						       =      self.image_proj(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.image_norm(SCREAMING_SNAKE_CASE__		)
         return time_image_embeds






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__ = 768     ,    SCREAMING_SNAKE_CASE__ = 1536		) ->       int:
         super().__init__()
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      nn.LayerNorm(SCREAMING_SNAKE_CASE__		)
         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        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Tuple:
         # image
         lowerCamelCase__						       =      self.image_proj(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.image_norm(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      self.input_hint_block(SCREAMING_SNAKE_CASE__		)
         return time_image_embeds, hint






class 		_A							(      nn.Module       ):





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__=None		) ->       int:
         super().__init__()
         lowerCamelCase__						       =      dtype
         lowerCamelCase__						       =      nn.Parameter(torch.randn(1     ,    SCREAMING_SNAKE_CASE__		) / embed_dim**0.5		)
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    dtype=self.dtype		)
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    dtype=self.dtype		)
         lowerCamelCase__						       =      nn.Linear(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    dtype=self.dtype		)
         lowerCamelCase__						       =      num_heads
         lowerCamelCase__						       =      embed_dim // self.num_heads





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__		) ->       str:
         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__						       =      x.size()

         def shape(SCREAMING_SNAKE_CASE__		):
            # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
            lowerCamelCase__						       =      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__						       =      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=SCREAMING_SNAKE_CASE__		) + 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(SCREAMING_SNAKE_CASE__		)		)
         # (bs*n_heads, length+class_token_length, dim_per_head)
         lowerCamelCase__						       =      shape(self.k_proj(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						       =      shape(self.v_proj(SCREAMING_SNAKE_CASE__		)		)

         # (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"     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

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

         return a[:, 0, :]  # cls_token







"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




