import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation





def      __lowercase  (		lowerCamelCase      :  torch.Tensor						,						lowerCamelCase      :  int						,						lowerCamelCase      :  bool = False						,						lowerCamelCase      :  float = 1						,						lowerCamelCase      :  float = 1						,						lowerCamelCase      :  int = 10000						,						):
 assert len(timesteps.shape			) == 1, "Timesteps should be a 1d-array"

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

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

 # scale embeddings
 UpperCamelCase_      :		Union[str, Any]				=      scale * emb

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

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

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





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Optional[int]=False						,						lowerCamelCase      :  Tuple=0			):
 UpperCamelCase_      :		Any				=      np.arange(lowerCamelCase						,						dtype=np.floataa			)
 UpperCamelCase_      :		Tuple				=      np.arange(lowerCamelCase						,						dtype=np.floataa			)
 UpperCamelCase_      :		int				=      np.meshgrid(lowerCamelCase						,						lowerCamelCase			)  # here w goes first
 UpperCamelCase_      :		int				=      np.stack(lowerCamelCase						,						axis=0			)

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





def      __lowercase  (		lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Optional[Any]			):
 if embed_dim % 2 != 0:
  raise ValueError('embed_dim must be divisible by 2'			)

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

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





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  int			):
 if embed_dim % 2 != 0:
  raise ValueError('embed_dim must be divisible by 2'			)

 UpperCamelCase_      :		Union[str, Any]				=      np.arange(embed_dim // 2						,						dtype=np.floataa			)
 omega /= embed_dim / 2.0
 UpperCamelCase_      :		Optional[int]				=      1.0 / 10000**omega  # (D/2,)

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

 UpperCamelCase_      :		int				=      np.sin(lowerCamelCase			)  # (M, D/2)
 UpperCamelCase_      :		Optional[Any]				=      np.cos(lowerCamelCase			)  # (M, D/2)

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





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							str     ,   snake_case			:							str=2_2_4     ,   snake_case			:							str=2_2_4     ,   snake_case			:							Any=1_6     ,   snake_case			:							List[Any]=3     ,   snake_case			:							List[str]=7_6_8     ,   snake_case			:							List[Any]=False     ,   snake_case			:							Any=True     ,   snake_case			:							Tuple=True     ,   )     ->		List[str]:

  """simple docstring"""





  super().__init__()

  UpperCamelCase_      :		Dict				=      (height // patch_size) * (width // patch_size)
  UpperCamelCase_      :		Tuple				=      flatten
  UpperCamelCase_      :		Optional[Any]				=      layer_norm

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

  UpperCamelCase_      :		Tuple				=      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 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							str    )     ->		List[Any]:

  """simple docstring"""





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





class 	_lowercase		(      nn.Module ):

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

  """simple docstring"""





  super().__init__()

  UpperCamelCase_      :		Tuple				=      nn.Linear(snake_case     ,   snake_case    )

  if cond_proj_dim is not None:
   UpperCamelCase_      :		int				=      nn.Linear(snake_case     ,   snake_case     ,   bias=snake_case    )
  else:
   UpperCamelCase_      :		Union[str, Any]				=      None

  UpperCamelCase_      :		str				=      get_activation(snake_case    )

  if out_dim is not None:
   UpperCamelCase_      :		Optional[int]				=      out_dim
  else:
   UpperCamelCase_      :		Any				=      time_embed_dim
  UpperCamelCase_      :		int				=      nn.Linear(snake_case     ,   snake_case    )

  if post_act_fn is None:
   UpperCamelCase_      :		List[str]				=      None
  else:
   UpperCamelCase_      :		List[Any]				=      get_activation(snake_case    )


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

  """simple docstring"""





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

  if self.act is not None:
   UpperCamelCase_      :		Dict				=      self.act(snake_case    )

  UpperCamelCase_      :		Dict				=      self.linear_a(snake_case    )

  if self.post_act is not None:
   UpperCamelCase_      :		List[str]				=      self.post_act(snake_case    )
  return sample





class 	_lowercase		(      nn.Module ):

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

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		int				=      num_channels
  UpperCamelCase_      :		Optional[Any]				=      flip_sin_to_cos
  UpperCamelCase_      :		Tuple				=      downscale_freq_shift


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Union[str, Any]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		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 	_lowercase		(      nn.Module ):

 def __init__(				self			:							Optional[Any]     ,   snake_case			:							int = 2_5_6     ,   snake_case			:							float = 1.0     ,   snake_case			:							List[Any]=True     ,   snake_case			:							int=True     ,   snake_case			:							List[Any]=False    )     ->		List[Any]:

  """simple docstring"""





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

  if set_W_to_weight:
   # to delete later
   UpperCamelCase_      :		Optional[Any]				=      nn.Parameter(torch.randn(snake_case    ) * scale     ,   requires_grad=snake_case    )

   UpperCamelCase_      :		List[Any]				=      self.W


 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							int    )     ->		List[str]:

  """simple docstring"""





  if self.log:
   UpperCamelCase_      :		List[str]				=      torch.log(snake_case    )

  UpperCamelCase_      :		List[str]				=      x[:, None] * self.weight[None, :] * 2 * np.pi

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





class 	_lowercase		(      nn.Module ):

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

  """simple docstring"""





  super().__init__()

  UpperCamelCase_      :		Any				=      height
  UpperCamelCase_      :		str				=      width
  UpperCamelCase_      :		Dict				=      num_embed
  UpperCamelCase_      :		Optional[int]				=      embed_dim

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


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							Tuple    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      self.emb(snake_case    )

  UpperCamelCase_      :		Dict				=      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
  UpperCamelCase_      :		int				=      height_emb.unsqueeze(2    )

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

  UpperCamelCase_      :		Optional[Any]				=      height_emb + width_emb

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

  UpperCamelCase_      :		List[Any]				=      emb + pos_emb[:, : emb.shape[1], :]

  return emb





class 	_lowercase		(      nn.Module ):

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

  """simple docstring"""





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

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

  """simple docstring"""





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


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							torch.LongTensor     ,   snake_case			:							List[Any]=None    )     ->		int:

  """simple docstring"""





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





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							int     ,   snake_case			:							int = 1_0_2_4     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 1_0     ,   )     ->		int:

  """simple docstring"""





  super().__init__()

  UpperCamelCase_      :		str				=      num_image_text_embeds
  UpperCamelCase_      :		Tuple				=      nn.Linear(snake_case     ,   self.num_image_text_embeds * cross_attention_dim    )
  UpperCamelCase_      :		Union[str, Any]				=      nn.Linear(snake_case     ,   snake_case    )


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

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      text_embeds.shape[0]

  # image
  UpperCamelCase_      :		str				=      self.image_embeds(snake_case    )
  UpperCamelCase_      :		List[str]				=      image_text_embeds.reshape(snake_case     ,   self.num_image_text_embeds     ,   -1    )

  # text
  UpperCamelCase_      :		int				=      self.text_proj(snake_case    )

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





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							str     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 3_2     ,   )     ->		Union[str, Any]:

  """simple docstring"""





  super().__init__()

  UpperCamelCase_      :		int				=      num_image_text_embeds
  UpperCamelCase_      :		Optional[int]				=      nn.Linear(snake_case     ,   self.num_image_text_embeds * cross_attention_dim    )
  UpperCamelCase_      :		Tuple				=      nn.LayerNorm(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							torch.FloatTensor    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      image_embeds.shape[0]

  # image
  UpperCamelCase_      :		Dict				=      self.image_embeds(snake_case    )
  UpperCamelCase_      :		Tuple				=      image_embeds.reshape(snake_case     ,   self.num_image_text_embeds     ,   -1    )
  UpperCamelCase_      :		Any				=      self.norm(snake_case    )
  return image_embeds





class 	_lowercase		(      nn.Module ):

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

  """simple docstring"""





  super().__init__()

  UpperCamelCase_      :		int				=      Timesteps(num_channels=2_5_6     ,   flip_sin_to_cos=snake_case     ,   downscale_freq_shift=1    )
  UpperCamelCase_      :		List[str]				=      TimestepEmbedding(in_channels=2_5_6     ,   time_embed_dim=snake_case    )
  UpperCamelCase_      :		int				=      LabelEmbedding(snake_case     ,   snake_case     ,   snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Optional[int]     ,   snake_case			:							Optional[Any]=None    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      self.time_proj(snake_case    )
  UpperCamelCase_      :		List[Any]				=      self.timestep_embedder(timesteps_proj.to(dtype=snake_case    )    )  # (N, D)

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

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

  return conditioning





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							Union[str, Any]     ,   snake_case			:							int     ,   snake_case			:							int     ,   snake_case			:							int = 6_4    )     ->		Tuple:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		Dict				=      nn.LayerNorm(snake_case    )
  UpperCamelCase_      :		Any				=      AttentionPooling(snake_case     ,   snake_case    )
  UpperCamelCase_      :		Tuple				=      nn.Linear(snake_case     ,   snake_case    )
  UpperCamelCase_      :		str				=      nn.LayerNorm(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							Union[str, Any]    )     ->		Optional[Any]:

  """simple docstring"""





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





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							str     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 1_5_3_6    )     ->		List[str]:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		str				=      nn.Linear(snake_case     ,   snake_case    )
  UpperCamelCase_      :		str				=      nn.LayerNorm(snake_case    )
  UpperCamelCase_      :		Optional[int]				=      nn.Linear(snake_case     ,   snake_case    )


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

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      self.text_proj(snake_case    )
  UpperCamelCase_      :		Tuple				=      self.text_norm(snake_case    )

  # image
  UpperCamelCase_      :		Dict				=      self.image_proj(snake_case    )

  return time_image_embeds + time_text_embeds





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							Tuple     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 1_5_3_6    )     ->		str:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		List[str]				=      nn.Linear(snake_case     ,   snake_case    )
  UpperCamelCase_      :		str				=      nn.LayerNorm(snake_case    )


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

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self.image_proj(snake_case    )
  UpperCamelCase_      :		Optional[Any]				=      self.image_norm(snake_case    )
  return time_image_embeds





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							List[str]     ,   snake_case			:							int = 7_6_8     ,   snake_case			:							int = 1_5_3_6    )     ->		Optional[Any]:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		List[str]				=      nn.Linear(snake_case     ,   snake_case    )
  UpperCamelCase_      :		Tuple				=      nn.LayerNorm(snake_case    )
  UpperCamelCase_      :		str				=      nn.Sequential(
      nn.Convad(3     ,   1_6     ,   3     ,   padding=1    )     ,   nn.SiLU()     ,   nn.Convad(1_6     ,   1_6     ,   3     ,   padding=1    )     ,   nn.SiLU()     ,   nn.Convad(1_6     ,   3_2     ,   3     ,   padding=1     ,   stride=2    )     ,   nn.SiLU()     ,   nn.Convad(3_2     ,   3_2     ,   3     ,   padding=1    )     ,   nn.SiLU()     ,   nn.Convad(3_2     ,   9_6     ,   3     ,   padding=1     ,   stride=2    )     ,   nn.SiLU()     ,   nn.Convad(9_6     ,   9_6     ,   3     ,   padding=1    )     ,   nn.SiLU()     ,   nn.Convad(9_6     ,   2_5_6     ,   3     ,   padding=1     ,   stride=2    )     ,   nn.SiLU()     ,   nn.Convad(2_5_6     ,   4     ,   3     ,   padding=1    )     ,   )


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

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      self.image_proj(snake_case    )
  UpperCamelCase_      :		List[str]				=      self.image_norm(snake_case    )
  UpperCamelCase_      :		Dict				=      self.input_hint_block(snake_case    )
  return time_image_embeds, hint





class 	_lowercase		(      nn.Module ):

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

  """simple docstring"""





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


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							str    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_      :		List[Any]				=      x.size()

  def shape(snake_case			:							Any    ):
   # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
   UpperCamelCase_      :		Optional[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)
   UpperCamelCase_      :		Optional[int]				=      x.transpose(1     ,   2    )
   # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
   UpperCamelCase_      :		Optional[Any]				=      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)
   UpperCamelCase_      :		Dict				=      x.transpose(1     ,   2    )
   return x

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

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

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

  # (bs*n_heads, dim_per_head, class_token_length)
  UpperCamelCase_      :		Dict				=      torch.einsum('bts,bcs->bct'     ,   snake_case     ,   snake_case    )

  # (bs, length+1, width)
  UpperCamelCase_      :		Optional[int]				=      a.reshape(snake_case     ,   -1     ,   1    ).transpose(1     ,   2    )

  return a[:, 0, :]  # cls_token




from datetime import date

import requests

a_					      = 'https://api.carbonintensity.org.uk/intensity'





def      __lowercase  (		):
 UpperCamelCase_      :		Dict				=      requests.get(lowerCamelCase			).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Union[str, Any]			):
 return requests.get(F"{BASE_URL}/{start}/{end}"			).json()["data"]


if __name__ == "__main__":
   for entry in fetch_from_to(start=date(2_020, 10, 1), end=date(2_020, 10, 3)):
      print('from {from} to {to}: {intensity[actual]}'.format(**entry))
   print(F"""{fetch_last_half_hour() = }""")



