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

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_ = False						,					lowerCamelCase_ = 1						,					lowerCamelCase_ = 1						,					lowerCamelCase_ = 1_00_00						,					)  ->      Optional[int]:


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

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

					__a:    List[str]		     =       torch.exp(_lowerCamelCase			)
					__a:    List[Any]		     =       timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__a:    Dict		     =       scale * emb

					# concat sine and cosine embeddings
					__a:    int		     =       torch.cat([torch.sin(_lowerCamelCase			), torch.cos(_lowerCamelCase			)]						,					dim=-1			)

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

					# zero pad
					if embedding_dim % 2 == 1:
										__a:    List[str]		     =       torch.nn.functional.pad(_lowerCamelCase						,					(0, 1, 0, 0)			)
					return emb


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_=False						,					lowerCamelCase_=0			)  ->      Dict:


					'''simple docstring'''
					__a:    Optional[int]		     =       np.arange(_lowerCamelCase						,					dtype=np.floataa			)
					__a:    Optional[int]		     =       np.arange(_lowerCamelCase						,					dtype=np.floataa			)
					__a:    Optional[int]		     =       np.meshgrid(_lowerCamelCase						,					_lowerCamelCase			)  # here w goes first
					__a:    Any		     =       np.stack(_lowerCamelCase						,					axis=0			)

					__a:    List[str]		     =       grid.reshape([2, 1, grid_size, grid_size]			)
					__a:    int		     =       get_ad_sincos_pos_embed_from_grid(_lowerCamelCase						,					_lowerCamelCase			)
					if cls_token and extra_tokens > 0:
										__a:    List[str]		     =       np.concatenate([np.zeros([extra_tokens, embed_dim]			), pos_embed]						,					axis=0			)
					return pos_embed


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      Optional[Any]:


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

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

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


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      int:


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

					__a:    Union[str, Any]		     =       np.arange(embed_dim // 2						,					dtype=np.floataa			)
					omega /= embed_dim / 2.0
					__a:    List[Any]		     =       1.0 / 1_00_00**omega  # (D/2,)

					__a:    Dict		     =       pos.reshape(-1			)  # (M,)
					__a:    str		     =       np.einsum("""m,d->md"""						,					_lowerCamelCase						,					_lowerCamelCase			)  # (M, D/2), outer product

					__a:    List[Any]		     =       np.sin(_lowerCamelCase			)  # (M, D/2)
					__a:    Dict		     =       np.cos(_lowerCamelCase			)  # (M, D/2)

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




class  _snake_case	(    nn.Module							):






							def __init__(       self    :      int    ,			snake_case__    :      List[str]=2_2_4    ,			snake_case__    :      Union[str, Any]=2_2_4    ,			snake_case__    :      Optional[int]=1_6    ,			snake_case__    :      str=3    ,			snake_case__    :      Union[str, Any]=7_6_8    ,			snake_case__    :      Any=False    ,			snake_case__    :      Any=True    ,			snake_case__    :      Any=True    ,			):






												"""simple docstring"""



												super().__init__()

												__a:    Any		     =       (height // patch_size) * (width // patch_size)
												__a:    Tuple		     =       flatten
												__a:    List[Any]		     =       layer_norm

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

												__a:    List[Any]		     =       get_ad_sincos_pos_embed(lowerCamelCase_    ,			int(num_patches**0.5     )     )
												self.register_buffer("""pos_embed"""    ,			torch.from_numpy(lowerCamelCase_     ).float().unsqueeze(0     )    ,			persistent=lowerCamelCase_     )






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



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






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      str    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      int = "silu"    ,			snake_case__    :      Dict = None    ,			snake_case__    :      int = None    ,			snake_case__    :      Dict=None    ,			):






												"""simple docstring"""



												super().__init__()

												__a:    int		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )

												if cond_proj_dim is not None:
																	__a:    List[str]		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_    ,			bias=lowerCamelCase_     )
												else:
																	__a:    List[str]		     =       None

												__a:    int		     =       get_activation(lowerCamelCase_     )

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

												if post_act_fn is None:
																	__a:    Any		     =       None
												else:
																	__a:    Optional[Any]		     =       get_activation(lowerCamelCase_     )






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Union[str, Any]=None     ):






												"""simple docstring"""



												if condition is not None:
																	__a:    str		     =       sample + self.cond_proj(lowerCamelCase_     )
												__a:    Union[str, Any]		     =       self.linear_a(lowerCamelCase_     )

												if self.act is not None:
																	__a:    List[Any]		     =       self.act(lowerCamelCase_     )

												__a:    List[Any]		     =       self.linear_a(lowerCamelCase_     )

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






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      Optional[int]    ,			snake_case__    :      List[Any]    ,			snake_case__    :      Any    ,			snake_case__    :      str     ):






												"""simple docstring"""



												super().__init__()
												__a:    List[str]		     =       num_channels
												__a:    List[Any]		     =       flip_sin_to_cos
												__a:    Tuple		     =       downscale_freq_shift






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    int		     =       get_timestep_embedding(
												    lowerCamelCase_    ,			self.num_channels    ,			flip_sin_to_cos=self.flip_sin_to_cos    ,			downscale_freq_shift=self.downscale_freq_shift    ,			)
												return t_emb






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      List[Any]    ,			snake_case__    :      List[str] = 2_5_6    ,			snake_case__    :      List[Any] = 1.0    ,			snake_case__    :      int=True    ,			snake_case__    :      List[Any]=True    ,			snake_case__    :      Optional[Any]=False     ):






												"""simple docstring"""



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

												if set_W_to_weight:
																	# to delete later
																	__a:    int		     =       nn.Parameter(torch.randn(lowerCamelCase_     ) * scale    ,			requires_grad=lowerCamelCase_     )

																	__a:    Optional[Any]		     =       self.W






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      str     ):






												"""simple docstring"""



												if self.log:
																	__a:    Dict		     =       torch.log(lowerCamelCase_     )

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

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






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      List[str]    ,			snake_case__    :      List[str]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      int    ,			snake_case__    :      Tuple    ,			):






												"""simple docstring"""



												super().__init__()

												__a:    str		     =       height
												__a:    str		     =       width
												__a:    List[Any]		     =       num_embed
												__a:    Dict		     =       embed_dim

												__a:    str		     =       nn.Embedding(self.num_embed    ,			lowerCamelCase_     )
												__a:    Tuple		     =       nn.Embedding(self.height    ,			lowerCamelCase_     )
												__a:    Optional[Any]		     =       nn.Embedding(self.width    ,			lowerCamelCase_     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    str		     =       self.emb(lowerCamelCase_     )

												__a:    int		     =       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:    List[Any]		     =       height_emb.unsqueeze(2     )

												__a:    str		     =       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:    Dict		     =       width_emb.unsqueeze(1     )

												__a:    List[Any]		     =       height_emb + width_emb

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

												__a:    Dict		     =       emb + pos_emb[:, : emb.shape[1], :]

												return emb






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      List[Any]    ,			snake_case__    :      Tuple    ,			snake_case__    :      int    ,			snake_case__    :      List[Any]     ):






												"""simple docstring"""



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






							def 				__lowercase	(       self    :      Any    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      Any=None     ):






												"""simple docstring"""



												if force_drop_ids is None:
																	__a:    List[str]		     =       torch.rand(labels.shape[0]    ,			device=labels.device     ) < self.dropout_prob
												else:
																	__a:    Union[str, Any]		     =       torch.tensor(force_drop_ids == 1     )
												__a:    Dict		     =       torch.where(lowerCamelCase_    ,			self.num_classes    ,			lowerCamelCase_     )
												return labels






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      str    ,			snake_case__    :      Optional[Any]=None     ):






												"""simple docstring"""



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






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      List[Any]    ,			snake_case__    :      Union[str, Any] = 1_0_2_4    ,			snake_case__    :      Optional[int] = 7_6_8    ,			snake_case__    :      Optional[int] = 7_6_8    ,			snake_case__    :      Optional[int] = 1_0    ,			):






												"""simple docstring"""



												super().__init__()

												__a:    Optional[int]		     =       num_image_text_embeds
												__a:    Any		     =       nn.Linear(lowerCamelCase_    ,			self.num_image_text_embeds * cross_attention_dim     )
												__a:    List[Any]		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      List[str]    ,			snake_case__    :      Any     ):






												"""simple docstring"""



												__a:    Dict		     =       text_embeds.shape[0]

												# image
												__a:    Optional[Any]		     =       self.image_embeds(lowerCamelCase_     )
												__a:    Any		     =       image_text_embeds.reshape(lowerCamelCase_    ,			self.num_image_text_embeds    ,			-1     )

												# text
												__a:    Optional[Any]		     =       self.text_proj(lowerCamelCase_     )

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






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      List[Any]    ,			snake_case__    :      Any = 7_6_8    ,			snake_case__    :      int = 7_6_8    ,			snake_case__    :      str = 3_2    ,			):






												"""simple docstring"""



												super().__init__()

												__a:    List[Any]		     =       num_image_text_embeds
												__a:    Union[str, Any]		     =       nn.Linear(lowerCamelCase_    ,			self.num_image_text_embeds * cross_attention_dim     )
												__a:    int		     =       nn.LayerNorm(lowerCamelCase_     )






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      str     ):






												"""simple docstring"""



												__a:    Any		     =       image_embeds.shape[0]

												# image
												__a:    Tuple		     =       self.image_embeds(lowerCamelCase_     )
												__a:    Optional[int]		     =       image_embeds.reshape(lowerCamelCase_    ,			self.num_image_text_embeds    ,			-1     )
												__a:    Dict		     =       self.norm(lowerCamelCase_     )
												return image_embeds






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      Union[str, Any]    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      int    ,			snake_case__    :      List[str]=0.1     ):






												"""simple docstring"""



												super().__init__()

												__a:    Tuple		     =       Timesteps(num_channels=2_5_6    ,			flip_sin_to_cos=lowerCamelCase_    ,			downscale_freq_shift=1     )
												__a:    Union[str, Any]		     =       TimestepEmbedding(in_channels=2_5_6    ,			time_embed_dim=lowerCamelCase_     )
												__a:    Union[str, Any]		     =       LabelEmbedding(lowerCamelCase_    ,			lowerCamelCase_    ,			lowerCamelCase_     )






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Any    ,			snake_case__    :      int=None     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.time_proj(lowerCamelCase_     )
												__a:    List[Any]		     =       self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase_     )     )  # (N, D)

												__a:    int		     =       self.class_embedder(lowerCamelCase_     )  # (N, D)

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

												return conditioning






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      Any    ,			snake_case__    :      Tuple    ,			snake_case__    :      Tuple    ,			snake_case__    :      Dict = 6_4     ):






												"""simple docstring"""



												super().__init__()
												__a:    int		     =       nn.LayerNorm(lowerCamelCase_     )
												__a:    Union[str, Any]		     =       AttentionPooling(lowerCamelCase_    ,			lowerCamelCase_     )
												__a:    Any		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )
												__a:    Union[str, Any]		     =       nn.LayerNorm(lowerCamelCase_     )






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    int		     =       self.norma(lowerCamelCase_     )
												__a:    Any		     =       self.pool(lowerCamelCase_     )
												__a:    Dict		     =       self.proj(lowerCamelCase_     )
												__a:    List[Any]		     =       self.norma(lowerCamelCase_     )
												return hidden_states






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      str    ,			snake_case__    :      List[str] = 7_6_8    ,			snake_case__    :      Union[str, Any] = 7_6_8    ,			snake_case__    :      Any = 1_5_3_6     ):






												"""simple docstring"""



												super().__init__()
												__a:    List[Any]		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )
												__a:    List[Any]		     =       nn.LayerNorm(lowerCamelCase_     )
												__a:    List[Any]		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      Dict    ,			snake_case__    :      Union[str, Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.text_proj(lowerCamelCase_     )
												__a:    Optional[Any]		     =       self.text_norm(lowerCamelCase_     )

												# image
												__a:    Any		     =       self.image_proj(lowerCamelCase_     )

												return time_image_embeds + time_text_embeds






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      Dict    ,			snake_case__    :      List[str] = 7_6_8    ,			snake_case__    :      Union[str, Any] = 1_5_3_6     ):






												"""simple docstring"""



												super().__init__()
												__a:    int		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )
												__a:    List[Any]		     =       nn.LayerNorm(lowerCamelCase_     )






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Dict     ):






												"""simple docstring"""



												__a:    int		     =       self.image_proj(lowerCamelCase_     )
												__a:    Dict		     =       self.image_norm(lowerCamelCase_     )
												return time_image_embeds






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      str    ,			snake_case__    :      Union[str, Any] = 7_6_8    ,			snake_case__    :      Union[str, Any] = 1_5_3_6     ):






												"""simple docstring"""



												super().__init__()
												__a:    Dict		     =       nn.Linear(lowerCamelCase_    ,			lowerCamelCase_     )
												__a:    Union[str, Any]		     =       nn.LayerNorm(lowerCamelCase_     )
												__a:    List[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 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.image_proj(lowerCamelCase_     )
												__a:    Any		     =       self.image_norm(lowerCamelCase_     )
												__a:    str		     =       self.input_hint_block(lowerCamelCase_     )
												return time_image_embeds, hint






class  _snake_case	(    nn.Module							):






							def __init__(       self    :      Any    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Any    ,			snake_case__    :      Union[str, Any]=None     ):






												"""simple docstring"""



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






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      Tuple     ):






												"""simple docstring"""



												__a:    List[str]		     =       x.size()

												def shape(snake_case__    :      Any     ):
																	# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																	__a:    Tuple		     =       x.view(lowerCamelCase_    ,			-1    ,			self.num_heads    ,			self.dim_per_head     )
																	# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
																	__a:    str		     =       x.transpose(1    ,			2     )
																	# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																	__a:    Optional[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)
																	__a:    Optional[int]		     =       x.transpose(1    ,			2     )
																	return x

												__a:    Tuple		     =       x.mean(dim=1    ,			keepdim=lowerCamelCase_     ) + self.positional_embedding.to(x.dtype     )
												__a:    str		     =       torch.cat([class_token, x]    ,			dim=1     )  # (bs, length+1, width)

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

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

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

												# (bs, length+1, width)
												__a:    List[Any]		     =       a.reshape(lowerCamelCase_    ,			-1    ,			1     ).transpose(1    ,			2     )

												return a[:, 0, :]  # cls_token








"""simple docstring"""
class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		data
       lowerCamelCase__	:     BinaryTreeNode | None								=		None
       lowerCamelCase__	:     BinaryTreeNode | None								=		None






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if node is None:
  lowerCamelCase__	:     List[str]								=		BinaryTreeNode(_lowerCamelCase							)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase__	:     str								=		insert(node.left_child     ,  _lowerCamelCase							)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase__	:     str								=		insert(node.right_child     ,  _lowerCamelCase							)
 return node






def 			lowerCamelCase_    (		_lowerCamelCase							):  # if node is None,return
 if node:
  lowerCamelCase__	:     Any								=		inorder(node.left_child							)
  lowerCamelCase__	:     Tuple								=		[*inorder_array, node.data]
  lowerCamelCase__	:     List[Any]								=		inorder_array + inorder(node.right_child							)
 else:
  lowerCamelCase__	:     Any								=		[]
 return inorder_array






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		insert(_lowerCamelCase     ,  15							)
 insert(_lowerCamelCase     ,  10							)
 insert(_lowerCamelCase     ,  25							)
 insert(_lowerCamelCase     ,  6							)
 insert(_lowerCamelCase     ,  14							)
 insert(_lowerCamelCase     ,  20							)
 insert(_lowerCamelCase     ,  60							)
 return root






def 			lowerCamelCase_    (		):
 # main function
 lowerCamelCase__	:     List[Any]								=		make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'							)
 inorder(_lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()

