




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 		lowercase__ (		__A:						torch.Tensor				,__A:						int				,__A:						bool = False				,__A:						float = 1				,__A:						float = 1				,__A:						int = 1_0_0_0_0				,):





					'''simple docstring'''






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

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

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

					# scale embeddings
					__magic_name__				:						Optional[int]     				=				scale * emb

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

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

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__				:						Union[str, Any]     				=				torch.nn.functional.pad(__A				,(0, 1, 0, 0)					)
					return emb







def 		lowercase__ (		__A:						Union[str, Any]				,__A:						Optional[int]				,__A:						int=False				,__A:						Any=0					):





					'''simple docstring'''






					__magic_name__				:						Optional[int]     				=				np.arange(__A				,dtype=np.floataa					)
					__magic_name__				:						str     				=				np.arange(__A				,dtype=np.floataa					)
					__magic_name__				:						Tuple     				=				np.meshgrid(__A				,__A					)  # here w goes first
					__magic_name__				:						Optional[Any]     				=				np.stack(__A				,axis=0					)

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







def 		lowercase__ (		__A:						Tuple				,__A:						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
					__magic_name__				:						str     				=				get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,grid[0]					)  # (H*W, D/2)
					__magic_name__				:						Any     				=				get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,grid[1]					)  # (H*W, D/2)

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







def 		lowercase__ (		__A:						Tuple				,__A:						List[Any]					):





					'''simple docstring'''






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

					__magic_name__				:						Any     				=				np.arange(embed_dim // 2				,dtype=np.floataa					)
					omega /= embed_dim / 2.0
					__magic_name__				:						str     				=				1.0 / 1_0_0_0_0**omega  # (D/2,)

					__magic_name__				:						Dict     				=				pos.reshape(-1					)  # (M,)
					__magic_name__				:						Optional[int]     				=				np.einsum('''m,d->md'''				,__A				,__A					)  # (M, D/2), outer product

					__magic_name__				:						Optional[int]     				=				np.sin(__A					)  # (M, D/2)
					__magic_name__				:						List[Any]     				=				np.cos(__A					)  # (M, D/2)

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


class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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

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

								__magic_name__				:						Optional[int]     				=				nn.Convad(
								    lowerCamelCase_							,    lowerCamelCase_							,    kernel_size=(patch_size, patch_size)							,    stride=lowerCamelCase_							,    bias=lowerCamelCase_	)
								if layer_norm:
													__magic_name__				:						int     				=				nn.LayerNorm(lowerCamelCase_							,    elementwise_affine=lowerCamelCase_							,    eps=1E-6	)
								else:
													__magic_name__				:						Tuple     				=				None

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






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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

								__magic_name__				:						List[Any]     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)

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

								__magic_name__				:						List[Any]     				=				get_activation(lowerCamelCase_	)

								if out_dim is not None:
													__magic_name__				:						Dict     				=				out_dim
								else:
													__magic_name__				:						List[str]     				=				time_embed_dim
								__magic_name__				:						str     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)

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


			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : Tuple							,    lowerCamelCase_      : Dict=None	)    -> List[Any]:
								if condition is not None:
													__magic_name__				:						Dict     				=				sample + self.cond_proj(lowerCamelCase_	)
								__magic_name__				:						Optional[int]     				=				self.linear_a(lowerCamelCase_	)

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

								__magic_name__				:						Optional[Any]     				=				self.linear_a(lowerCamelCase_	)

								if self.post_act is not None:
													__magic_name__				:						str     				=				self.post_act(lowerCamelCase_	)
								return sample






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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


			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : List[str]	)    -> Any:
								__magic_name__				:						Tuple     				=				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  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : int							,    lowerCamelCase_      : int = 256							,    lowerCamelCase_      : float = 1.0							,    lowerCamelCase_      : Optional[Any]=True							,    lowerCamelCase_      : Optional[int]=True							,    lowerCamelCase_      : Dict=False	)    -> List[Any]:
								super().__init__()
								__magic_name__				:						Any     				=				nn.Parameter(torch.randn(lowerCamelCase_	) * scale							,    requires_grad=lowerCamelCase_	)
								__magic_name__				:						Union[str, Any]     				=				log
								__magic_name__				:						int     				=				flip_sin_to_cos

								if set_W_to_weight:
													# to delete later
													__magic_name__				:						Union[str, Any]     				=				nn.Parameter(torch.randn(lowerCamelCase_	) * scale							,    requires_grad=lowerCamelCase_	)

													__magic_name__				:						Any     				=				self.W


			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : Optional[Any]	)    -> Optional[Any]:
								if self.log:
													__magic_name__				:						str     				=				torch.log(lowerCamelCase_	)

								__magic_name__				:						Optional[Any]     				=				x[:, None] * self.weight[None, :] * 2 * np.pi

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






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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

								__magic_name__				:						List[str]     				=				height
								__magic_name__				:						int     				=				width
								__magic_name__				:						Optional[Any]     				=				num_embed
								__magic_name__				:						str     				=				embed_dim

								__magic_name__				:						Optional[int]     				=				nn.Embedding(self.num_embed							,    lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				nn.Embedding(self.height							,    lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				nn.Embedding(self.width							,    lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : Union[str, Any]	)    -> Any:
								__magic_name__				:						Dict     				=				self.emb(lowerCamelCase_	)

								__magic_name__				:						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
								__magic_name__				:						List[str]     				=				height_emb.unsqueeze(2	)

								__magic_name__				:						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
								__magic_name__				:						Tuple     				=				width_emb.unsqueeze(1	)

								__magic_name__				:						int     				=				height_emb + width_emb

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

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

								return emb






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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

			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : int							,    lowerCamelCase_      : List[str]=None	)    -> int:
								if force_drop_ids is None:
													__magic_name__				:						Optional[int]     				=				torch.rand(labels.shape[0]							,    device=labels.device	) < self.dropout_prob
								else:
													__magic_name__				:						Union[str, Any]     				=				torch.tensor(force_drop_ids == 1	)
								__magic_name__				:						List[str]     				=				torch.where(lowerCamelCase_							,    self.num_classes							,    lowerCamelCase_	)
								return labels


			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : torch.LongTensor							,    lowerCamelCase_      : Union[str, Any]=None	)    -> int:
								__magic_name__				:						Tuple     				=				self.dropout_prob > 0
								if (self.training and use_dropout) or (force_drop_ids is not None):
													__magic_name__				:						Dict     				=				self.token_drop(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						Any     				=				self.embedding_table(lowerCamelCase_	)
								return embeddings






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Optional[Any]							,    lowerCamelCase_      : int = 1024							,    lowerCamelCase_      : int = 768							,    lowerCamelCase_      : int = 768							,    lowerCamelCase_      : int = 10							,    )    -> str:
								super().__init__()

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


			def 				UpperCAmelCase__							(			self      : Optional[Any]							,    lowerCamelCase_      : torch.FloatTensor							,    lowerCamelCase_      : torch.FloatTensor	)    -> str:
								__magic_name__				:						Any     				=				text_embeds.shape[0]

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

								# text
								__magic_name__				:						Tuple     				=				self.text_proj(lowerCamelCase_	)

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






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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

								__magic_name__				:						str     				=				num_image_text_embeds
								__magic_name__				:						Union[str, Any]     				=				nn.Linear(lowerCamelCase_							,    self.num_image_text_embeds * cross_attention_dim	)
								__magic_name__				:						Tuple     				=				nn.LayerNorm(lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : torch.FloatTensor	)    -> Optional[int]:
								__magic_name__				:						str     				=				image_embeds.shape[0]

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






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : Tuple							,    lowerCamelCase_      : Any							,    lowerCamelCase_      : str=0.1	)    -> str:
								super().__init__()

								__magic_name__				:						Dict     				=				Timesteps(num_channels=256							,    flip_sin_to_cos=lowerCamelCase_							,    downscale_freq_shift=1	)
								__magic_name__				:						Union[str, Any]     				=				TimestepEmbedding(in_channels=256							,    time_embed_dim=lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				LabelEmbedding(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Optional[Any]							,    lowerCamelCase_      : List[str]=None	)    -> Union[str, Any]:
								__magic_name__				:						Optional[int]     				=				self.time_proj(lowerCamelCase_	)
								__magic_name__				:						Union[str, Any]     				=				self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase_	)	)  # (N, D)

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

								__magic_name__				:						Optional[Any]     				=				timesteps_emb + class_labels  # (N, D)

								return conditioning






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : str							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int = 64	)    -> str:
								super().__init__()
								__magic_name__				:						str     				=				nn.LayerNorm(lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				AttentionPooling(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						int     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						Dict     				=				nn.LayerNorm(lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : str							,    lowerCamelCase_      : List[Any]	)    -> Dict:
								__magic_name__				:						int     				=				self.norma(lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				self.pool(lowerCamelCase_	)
								__magic_name__				:						str     				=				self.proj(lowerCamelCase_	)
								__magic_name__				:						Tuple     				=				self.norma(lowerCamelCase_	)
								return hidden_states






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Any							,    lowerCamelCase_      : int = 768							,    lowerCamelCase_      : int = 768							,    lowerCamelCase_      : int = 1536	)    -> int:
								super().__init__()
								__magic_name__				:						Optional[int]     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						List[str]     				=				nn.LayerNorm(lowerCamelCase_	)
								__magic_name__				:						Optional[Any]     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : List[Any]							,    lowerCamelCase_      : torch.FloatTensor							,    lowerCamelCase_      : torch.FloatTensor	)    -> List[Any]:
								# text
								__magic_name__				:						Optional[Any]     				=				self.text_proj(lowerCamelCase_	)
								__magic_name__				:						Optional[Any]     				=				self.text_norm(lowerCamelCase_	)

								# image
								__magic_name__				:						str     				=				self.image_proj(lowerCamelCase_	)

								return time_image_embeds + time_text_embeds






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Tuple							,    lowerCamelCase_      : int = 768							,    lowerCamelCase_      : int = 1536	)    -> List[str]:
								super().__init__()
								__magic_name__				:						Dict     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						Optional[Any]     				=				nn.LayerNorm(lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : Optional[Any]							,    lowerCamelCase_      : torch.FloatTensor	)    -> Any:
								# image
								__magic_name__				:						Dict     				=				self.image_proj(lowerCamelCase_	)
								__magic_name__				:						Optional[Any]     				=				self.image_norm(lowerCamelCase_	)
								return time_image_embeds






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





			def __init__(			self      : Tuple							,    lowerCamelCase_      : int = 768							,    lowerCamelCase_      : int = 1536	)    -> Optional[int]:
								super().__init__()
								__magic_name__				:						Optional[Any]     				=				nn.Linear(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						Union[str, Any]     				=				nn.LayerNorm(lowerCamelCase_	)
								__magic_name__				:						List[Any]     				=				nn.Sequential(
								    nn.Convad(3							,    16							,    3							,    padding=1	)							,    nn.SiLU()							,    nn.Convad(16							,    16							,    3							,    padding=1	)							,    nn.SiLU()							,    nn.Convad(16							,    32							,    3							,    padding=1							,    stride=2	)							,    nn.SiLU()							,    nn.Convad(32							,    32							,    3							,    padding=1	)							,    nn.SiLU()							,    nn.Convad(32							,    96							,    3							,    padding=1							,    stride=2	)							,    nn.SiLU()							,    nn.Convad(96							,    96							,    3							,    padding=1	)							,    nn.SiLU()							,    nn.Convad(96							,    256							,    3							,    padding=1							,    stride=2	)							,    nn.SiLU()							,    nn.Convad(256							,    4							,    3							,    padding=1	)							,    )


			def 				UpperCAmelCase__							(			self      : List[Any]							,    lowerCamelCase_      : torch.FloatTensor							,    lowerCamelCase_      : torch.FloatTensor	)    -> Tuple:
								# image
								__magic_name__				:						List[str]     				=				self.image_proj(lowerCamelCase_	)
								__magic_name__				:						Dict     				=				self.image_norm(lowerCamelCase_	)
								__magic_name__				:						str     				=				self.input_hint_block(lowerCamelCase_	)
								return time_image_embeds, hint






class  lowerCamelCase     (		nn.Module    ):






			'''simple docstring'''





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


			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : Optional[Any]	)    -> int:
								__magic_name__				,     __magic_name__				,     __magic_name__				:						Union[str, Any]     				=				x.size()

								def shape(lowerCamelCase_      : Tuple	):
													# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
													__magic_name__				:						Optional[Any]     				=				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)
													__magic_name__				:						Union[str, Any]     				=				x.transpose(1							,    2	)
													# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
													__magic_name__				:						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)
													__magic_name__				:						Optional[Any]     				=				x.transpose(1							,    2	)
													return x

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

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

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

								# (bs*n_heads, dim_per_head, class_token_length)
								__magic_name__				:						Tuple     				=				torch.einsum('''bts,bcs->bct'''							,    lowerCamelCase_							,    lowerCamelCase_	)

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

								return a[:, 0, :]  # cls_token









import math


class  lowerCamelCase     :






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Union[str, Any]	)    -> Optional[Any]:
								__magic_name__				:						Union[str, Any]     				=				x
								__magic_name__				:						Optional[int]     				=				y
								__magic_name__				:						List[str]     				=				step_size
								__magic_name__				:						Union[str, Any]     				=				function_to_optimize

			def 				UpperCAmelCase__							(			self      : int	)    -> int:
								return self.function(self.x							,    self.y	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> Any:
								__magic_name__				:						Optional[int]     				=				self.step_size
								return [
								    SearchProblem(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    self.function	)
								    for x, y in (
								        (self.x - step_size, self.y - step_size),
								        (self.x - step_size, self.y),
								        (self.x - step_size, self.y + step_size),
								        (self.x, self.y - step_size),
								        (self.x, self.y + step_size),
								        (self.x + step_size, self.y - step_size),
								        (self.x + step_size, self.y),
								        (self.x + step_size, self.y + step_size),
								    )
								]

			def __hash__(			self      : Dict	)    -> Tuple:
								return hash(str(self	)	)

			def __eq__(			self      : List[str]							,    lowerCamelCase_      : Dict	)    -> Union[str, Any]:
								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													return hash(str(self	)	) == hash(str(lowerCamelCase_	)	)
								return False


			def __str__(			self      : Tuple	)    -> Tuple:
								return F'''x: {self.x} y: {self.y}'''







def 		lowercase__ (		__A:						int				,__A:						bool = True				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						bool = False				,__A:						int = 1_0_0_0_0				,):





					'''simple docstring'''






					__magic_name__				:						Union[str, Any]     				=				search_prob
					__magic_name__				:						int     				=				[]  # list to store the current score at each iteration
					__magic_name__				:						Any     				=				0
					__magic_name__				:						Optional[Any]     				=				False
					__magic_name__				:						Dict     				=				set()
					while not solution_found and iterations < max_iter:
										visited.add(__A					)
										iterations += 1
										__magic_name__				:						int     				=				current_state.score()
										scores.append(__A					)
										__magic_name__				:						Union[str, Any]     				=				current_state.get_neighbors()
										__magic_name__				:						str     				=				-math.inf
										__magic_name__				:						Optional[Any]     				=				math.inf
										__magic_name__				:						Any     				=				None  # to hold the next best neighbor
										for neighbor in neighbors:
															if neighbor in visited:
																				continue  # do not want to visit the same state again
															if (
															    neighbor.x > max_x
															    or neighbor.x < min_x
															    or neighbor.y > max_y
															    or neighbor.y < min_y
															):
																				continue  # neighbor outside our bounds
															__magic_name__				:						Tuple     				=				neighbor.score() - current_score
															if find_max:  # finding max
																				# going to direction with greatest ascent
																				if change > max_change and change > 0:
																									__magic_name__				:						List[str]     				=				change
																									__magic_name__				:						List[str]     				=				neighbor
															else:  # finding min
																				# to direction with greatest descent
																				if change < min_change and change < 0:
																									__magic_name__				:						List[Any]     				=				change
																									__magic_name__				:						Optional[int]     				=				neighbor
										if next_state is not None:
															# we found at least one neighbor which improved the current state
															__magic_name__				:						Any     				=				next_state
										else:
															# since we have no neighbor that improves the solution we stop the search
															__magic_name__				:						List[str]     				=				True

					if visualization:
										from matplotlib import pyplot as plt

										plt.plot(range(__A					)				,__A					)
										plt.xlabel('''Iterations'''					)
										plt.ylabel('''Function values'''					)
										plt.show()

					return current_state


if __name__ == "__main__":
	import doctest

	doctest.testmod()







	def 		lowercase__ (		__A:						Optional[Any]				,__A:						str					):





						'''simple docstring'''






						return (x**2) + (y**2)

	# starting the problem with initial coordinates (3, 4)
	__lowerCamelCase :    Any      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Any      						=      hill_climbing(prob, find_max=False)
	print(
	    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
	    F"""{local_min.score()}"""
	)

	# starting the problem with initial coordinates (12, 47)
	__lowerCamelCase :    Optional[int]      						=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Union[str, Any]      						=      hill_climbing(
	    prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
	)
	print(
	    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
	    F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
	)







	def 		lowercase__ (		__A:						List[Any]				,__A:						int					):





						'''simple docstring'''






						return (3 * x**2) - (6 * y)

	__lowerCamelCase :    List[str]      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Optional[int]      						=      hill_climbing(prob, find_max=True)
	print(
	    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
	    F"""{local_min.score()}"""
	)



