

"""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 = 10000,					)			->     Any:
	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      :							Any       =   exponent / (half_dim - downscale_freq_shift)

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

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

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

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		_UpperCAmelCase      :							List[Any]       =   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  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  List[Any],					_lowerCAmelCase       :  Dict=False,					_lowerCAmelCase       :  Union[str, Any]=0	)			->     List[Any]:
	_UpperCAmelCase      :							Union[str, Any]       =   np.arange(_lowerCAmelCase,					dtype=np.floataa	)
	_UpperCAmelCase      :							List[Any]       =   np.arange(_lowerCAmelCase,					dtype=np.floataa	)
	_UpperCAmelCase      :							Tuple       =   np.meshgrid(_lowerCAmelCase,					_lowerCAmelCase	)  # here w goes first
	_UpperCAmelCase      :							List[Any]       =   np.stack(_lowerCAmelCase,					axis=0	)

	_UpperCAmelCase      :							int       =   grid.reshape([2, 1, grid_size, grid_size]	)
	_UpperCAmelCase      :							Optional[Any]       =   get_ad_sincos_pos_embed_from_grid(_lowerCAmelCase,					_lowerCAmelCase	)
	if cls_token and extra_tokens > 0:
		_UpperCAmelCase      :							Any       =   np.concatenate([np.zeros([extra_tokens, embed_dim]	), pos_embed],					axis=0	)
	return pos_embed
def  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Optional[Any]	)			->     int:
	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      :							int       =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2,					grid[1]	)  # (H*W, D/2)

	_UpperCAmelCase      :							Optional[int]       =   np.concatenate([emb_h, emb_w],					axis=1	)  # (H*W, D)
	return emb
def  UpperCamelCase   (     _lowerCAmelCase       :  List[Any],					_lowerCAmelCase       :  List[str]	)			->     int:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""	)

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

	_UpperCAmelCase      :							Optional[Any]       =   pos.reshape(-1	)  # (M,)
	_UpperCAmelCase      :							Optional[Any]       =   np.einsum("""m,d->md""",					_lowerCAmelCase,					_lowerCAmelCase	)  # (M, D/2), outer product

	_UpperCAmelCase      :							Optional[Any]       =   np.sin(_lowerCAmelCase	)  # (M, D/2)
	_UpperCAmelCase      :							Any       =   np.cos(_lowerCAmelCase	)  # (M, D/2)

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






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A=2_24 ,						_A=2_24 ,						_A=16 ,						_A=3 ,						_A=7_68 ,						_A=False ,						_A=True ,						_A=True ,						)      ->      Optional[int]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							Tuple       =   (height // patch_size) * (width // patch_size)
				_UpperCAmelCase      :							Optional[int]       =   flatten
				_UpperCAmelCase      :							Optional[int]       =   layer_norm

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

				_UpperCAmelCase      :							Any       =   get_ad_sincos_pos_embed(_A ,						int(num_patches**0.5       )       )
				self.register_buffer("""pos_embed""" ,						torch.from_numpy(_A       ).float().unsqueeze(0       ) ,						persistent=_A       )
			def 							__snake_case	(						self ,						_A       )      ->      Optional[Any]:






				'''simple docstring'''







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






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A = "silu" ,						_A = None ,						_A = None ,						_A=None ,						)      ->      Optional[int]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							str       =   nn.Linear(_A ,						_A       )

				if cond_proj_dim is not None:
					_UpperCAmelCase      :							str       =   nn.Linear(_A ,						_A ,						bias=_A       )
				else:
					_UpperCAmelCase      :							Optional[int]       =   None

				_UpperCAmelCase      :							Optional[int]       =   get_activation(_A       )

				if out_dim is not None:
					_UpperCAmelCase      :							Optional[int]       =   out_dim
				else:
					_UpperCAmelCase      :							Union[str, Any]       =   time_embed_dim
				_UpperCAmelCase      :							Any       =   nn.Linear(_A ,						_A       )

				if post_act_fn is None:
					_UpperCAmelCase      :							Union[str, Any]       =   None
				else:
					_UpperCAmelCase      :							List[str]       =   get_activation(_A       )
			def 							__snake_case	(						self ,						_A ,						_A=None       )      ->      Tuple:






				'''simple docstring'''







				if condition is not None:
					_UpperCAmelCase      :							List[Any]       =   sample + self.cond_proj(_A       )
				_UpperCAmelCase      :							str       =   self.linear_a(_A       )

				if self.act is not None:
					_UpperCAmelCase      :							Tuple       =   self.act(_A       )

				_UpperCAmelCase      :							Optional[Any]       =   self.linear_a(_A       )

				if self.post_act is not None:
					_UpperCAmelCase      :							Optional[int]       =   self.post_act(_A       )
				return sample






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A       )      ->      List[str]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Tuple       =   num_channels
				_UpperCAmelCase      :							Tuple       =   flip_sin_to_cos
				_UpperCAmelCase      :							List[Any]       =   downscale_freq_shift
			def 							__snake_case	(						self ,						_A       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   get_timestep_embedding(
				    _A ,						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):
			def __init__(						self ,						_A = 2_56 ,						_A = 1.0 ,						_A=True ,						_A=True ,						_A=False       )      ->      Optional[int]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Any       =   nn.Parameter(torch.randn(_A       ) * scale ,						requires_grad=_A       )
				_UpperCAmelCase      :							List[Any]       =   log
				_UpperCAmelCase      :							Dict       =   flip_sin_to_cos

				if set_W_to_weight:
					# to delete later
					_UpperCAmelCase      :							List[str]       =   nn.Parameter(torch.randn(_A       ) * scale ,						requires_grad=_A       )

					_UpperCAmelCase      :							Dict       =   self.W
			def 							__snake_case	(						self ,						_A       )      ->      Union[str, Any]:






				'''simple docstring'''







				if self.log:
					_UpperCAmelCase      :							Optional[Any]       =   torch.log(_A       )

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

				if self.flip_sin_to_cos:
					_UpperCAmelCase      :							Dict       =   torch.cat([torch.cos(_A       ), torch.sin(_A       )] ,						dim=-1       )
				else:
					_UpperCAmelCase      :							List[str]       =   torch.cat([torch.sin(_A       ), torch.cos(_A       )] ,						dim=-1       )
				return out






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A ,						_A ,						)      ->      Union[str, Any]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							Union[str, Any]       =   height
				_UpperCAmelCase      :							str       =   width
				_UpperCAmelCase      :							Any       =   num_embed
				_UpperCAmelCase      :							List[str]       =   embed_dim

				_UpperCAmelCase      :							str       =   nn.Embedding(self.num_embed ,						_A       )
				_UpperCAmelCase      :							List[str]       =   nn.Embedding(self.height ,						_A       )
				_UpperCAmelCase      :							Optional[int]       =   nn.Embedding(self.width ,						_A       )
			def 							__snake_case	(						self ,						_A       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   self.emb(_A       )

				_UpperCAmelCase      :							Any       =   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      :							str       =   height_emb.unsqueeze(2       )

				_UpperCAmelCase      :							Tuple       =   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      :							Optional[int]       =   width_emb.unsqueeze(1       )

				_UpperCAmelCase      :							Dict       =   height_emb + width_emb

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

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

				return emb






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A       )      ->      Union[str, Any]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Any       =   dropout_prob > 0
				_UpperCAmelCase      :							Union[str, Any]       =   nn.Embedding(num_classes + use_cfg_embedding ,						_A       )
				_UpperCAmelCase      :							List[Any]       =   num_classes
				_UpperCAmelCase      :							Union[str, Any]       =   dropout_prob
			def 							__snake_case	(						self ,						_A ,						_A=None       )      ->      List[Any]:






				'''simple docstring'''







				if force_drop_ids is None:
					_UpperCAmelCase      :							List[str]       =   torch.rand(labels.shape[0] ,						device=labels.device       ) < self.dropout_prob
				else:
					_UpperCAmelCase      :							int       =   torch.tensor(force_drop_ids == 1       )
				_UpperCAmelCase      :							int       =   torch.where(_A ,						self.num_classes ,						_A       )
				return labels
			def 							__snake_case	(						self ,						_A ,						_A=None       )      ->      str:






				'''simple docstring'''







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






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A = 10_24 ,						_A = 7_68 ,						_A = 7_68 ,						_A = 10 ,						)      ->      List[str]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							Optional[Any]       =   num_image_text_embeds
				_UpperCAmelCase      :							Union[str, Any]       =   nn.Linear(_A ,						self.num_image_text_embeds * cross_attention_dim       )
				_UpperCAmelCase      :							Optional[Any]       =   nn.Linear(_A ,						_A       )
			def 							__snake_case	(						self ,						_A ,						_A       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   text_embeds.shape[0]

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

				# text
				_UpperCAmelCase      :							Optional[Any]       =   self.text_proj(_A       )

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






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A = 7_68 ,						_A = 7_68 ,						_A = 32 ,						)      ->      Tuple:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							Union[str, Any]       =   num_image_text_embeds
				_UpperCAmelCase      :							List[str]       =   nn.Linear(_A ,						self.num_image_text_embeds * cross_attention_dim       )
				_UpperCAmelCase      :							int       =   nn.LayerNorm(_A       )
			def 							__snake_case	(						self ,						_A       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   image_embeds.shape[0]

				# image
				_UpperCAmelCase      :							Union[str, Any]       =   self.image_embeds(_A       )
				_UpperCAmelCase      :							Optional[Any]       =   image_embeds.reshape(_A ,						self.num_image_text_embeds ,						-1       )
				_UpperCAmelCase      :							str       =   self.norm(_A       )
				return image_embeds






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A=0.1       )      ->      List[str]:






				'''simple docstring'''







				super().__init__()

				_UpperCAmelCase      :							Dict       =   Timesteps(num_channels=2_56 ,						flip_sin_to_cos=_A ,						downscale_freq_shift=1       )
				_UpperCAmelCase      :							Tuple       =   TimestepEmbedding(in_channels=2_56 ,						time_embed_dim=_A       )
				_UpperCAmelCase      :							List[str]       =   LabelEmbedding(_A ,						_A ,						_A       )
			def 							__snake_case	(						self ,						_A ,						_A ,						_A=None       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   self.time_proj(_A       )
				_UpperCAmelCase      :							str       =   self.timestep_embedder(timesteps_proj.to(dtype=_A       )       )  # (N, D)

				_UpperCAmelCase      :							Union[str, Any]       =   self.class_embedder(_A       )  # (N, D)

				_UpperCAmelCase      :							Any       =   timesteps_emb + class_labels  # (N, D)

				return conditioning






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A = 64       )      ->      Tuple:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Union[str, Any]       =   nn.LayerNorm(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   AttentionPooling(_A ,						_A       )
				_UpperCAmelCase      :							List[str]       =   nn.Linear(_A ,						_A       )
				_UpperCAmelCase      :							Optional[Any]       =   nn.LayerNorm(_A       )
			def 							__snake_case	(						self ,						_A       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   self.norma(_A       )
				_UpperCAmelCase      :							str       =   self.pool(_A       )
				_UpperCAmelCase      :							Optional[int]       =   self.proj(_A       )
				_UpperCAmelCase      :							List[str]       =   self.norma(_A       )
				return hidden_states






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A = 7_68 ,						_A = 7_68 ,						_A = 15_36       )      ->      List[Any]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Optional[int]       =   nn.Linear(_A ,						_A       )
				_UpperCAmelCase      :							List[str]       =   nn.LayerNorm(_A       )
				_UpperCAmelCase      :							int       =   nn.Linear(_A ,						_A       )
			def 							__snake_case	(						self ,						_A ,						_A       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   self.text_proj(_A       )
				_UpperCAmelCase      :							Optional[int]       =   self.text_norm(_A       )

				# image
				_UpperCAmelCase      :							Dict       =   self.image_proj(_A       )

				return time_image_embeds + time_text_embeds






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A = 7_68 ,						_A = 15_36       )      ->      Tuple:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Optional[Any]       =   nn.Linear(_A ,						_A       )
				_UpperCAmelCase      :							Tuple       =   nn.LayerNorm(_A       )
			def 							__snake_case	(						self ,						_A       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   self.image_proj(_A       )
				_UpperCAmelCase      :							int       =   self.image_norm(_A       )
				return time_image_embeds






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A = 7_68 ,						_A = 15_36       )      ->      Optional[int]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							List[Any]       =   nn.Linear(_A ,						_A       )
				_UpperCAmelCase      :							str       =   nn.LayerNorm(_A       )
				_UpperCAmelCase      :							int       =   nn.Sequential(
				    nn.Convad(3 ,						16 ,						3 ,						padding=1       ) ,						nn.SiLU() ,						nn.Convad(16 ,						16 ,						3 ,						padding=1       ) ,						nn.SiLU() ,						nn.Convad(16 ,						32 ,						3 ,						padding=1 ,						stride=2       ) ,						nn.SiLU() ,						nn.Convad(32 ,						32 ,						3 ,						padding=1       ) ,						nn.SiLU() ,						nn.Convad(32 ,						96 ,						3 ,						padding=1 ,						stride=2       ) ,						nn.SiLU() ,						nn.Convad(96 ,						96 ,						3 ,						padding=1       ) ,						nn.SiLU() ,						nn.Convad(96 ,						2_56 ,						3 ,						padding=1 ,						stride=2       ) ,						nn.SiLU() ,						nn.Convad(2_56 ,						4 ,						3 ,						padding=1       ) ,						)
			def 							__snake_case	(						self ,						_A ,						_A       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   self.image_proj(_A       )
				_UpperCAmelCase      :							Tuple       =   self.image_norm(_A       )
				_UpperCAmelCase      :							int       =   self.input_hint_block(_A       )
				return time_image_embeds, hint






class       _UpperCAmelCase     (						nn.Module):
			def __init__(						self ,						_A ,						_A ,						_A=None       )      ->      int:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase      :							Dict       =   dtype
				_UpperCAmelCase      :							Tuple       =   nn.Parameter(torch.randn(1 ,						_A       ) / embed_dim**0.5       )
				_UpperCAmelCase      :							List[Any]       =   nn.Linear(_A ,						_A ,						dtype=self.dtype       )
				_UpperCAmelCase      :							Optional[int]       =   nn.Linear(_A ,						_A ,						dtype=self.dtype       )
				_UpperCAmelCase      :							int       =   nn.Linear(_A ,						_A ,						dtype=self.dtype       )
				_UpperCAmelCase      :							Optional[int]       =   num_heads
				_UpperCAmelCase      :							List[str]       =   embed_dim // self.num_heads
			def 							__snake_case	(						self ,						_A       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase  ,				_UpperCAmelCase  ,				_UpperCAmelCase      :							int       =   x.size()

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

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

				# (bs*n_heads, class_token_length, dim_per_head)
				_UpperCAmelCase      :							Dict       =   shape(self.q_proj(_A       )       )
				# (bs*n_heads, length+class_token_length, dim_per_head)
				_UpperCAmelCase      :							Any       =   shape(self.k_proj(_A       )       )
				_UpperCAmelCase      :							Dict       =   shape(self.v_proj(_A       )       )

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

				# (bs*n_heads, dim_per_head, class_token_length)
				_UpperCAmelCase      :							int       =   torch.einsum("""bts,bcs->bct""" ,						_A ,						_A       )

				# (bs, length+1, width)
				_UpperCAmelCase      :							int       =   a.reshape(_A ,						-1 ,						1       ).transpose(1 ,						2       )

				return a[:, 0, :]  # cls_token








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			:					int							  =					{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					Optional[int]							  =					[
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						lowerCamelCase__			:					Optional[Any]							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





