"""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_0000       ,   )							-> Optional[int]:




						'''simple docstring'''



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

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

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

						# scale embeddings
						lowerCamelCase__ =scale * emb

						# concat sine and cosine embeddings
						lowerCamelCase__ =torch.cat([torch.sin(__lowerCAmelCase    ), torch.cos(__lowerCAmelCase    )]       ,   dim=-1    )

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

						# zero pad
						if embedding_dim % 2 == 1:
												lowerCamelCase__ =torch.nn.functional.pad(__lowerCAmelCase       ,   (0, 1, 0, 0)    )
						return emb





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=False       ,   __lowerCAmelCase=0    )							-> Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__ =np.arange(__lowerCAmelCase       ,   dtype=np.floataa    )
						lowerCamelCase__ =np.arange(__lowerCAmelCase       ,   dtype=np.floataa    )
						lowerCamelCase__ =np.meshgrid(__lowerCAmelCase       ,   __lowerCAmelCase    )  # here w goes first
						lowerCamelCase__ =np.stack(__lowerCAmelCase       ,   axis=0    )

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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:




						'''simple docstring'''



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

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

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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



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

						lowerCamelCase__ =np.arange(embed_dim // 2       ,   dtype=np.floataa    )
						omega /= embed_dim / 2.0
						lowerCamelCase__ =1.0 / 1_0000**omega  # (D/2,)

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

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

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase=224     ,			_lowerCamelCase=224     ,			_lowerCamelCase=16     ,			_lowerCamelCase=3     ,			_lowerCamelCase=768     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			):
										super().__init__()

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

										lowerCamelCase__ =nn.Convad(
										    _lowerCamelCase     ,			_lowerCamelCase     ,			kernel_size=(patch_size, patch_size)     ,			stride=_lowerCamelCase     ,			bias=_lowerCamelCase				)
										if layer_norm:
																lowerCamelCase__ =nn.LayerNorm(_lowerCamelCase     ,			elementwise_affine=_lowerCamelCase     ,			eps=1E-6				)
										else:
																lowerCamelCase__ =None

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = "silu"     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase=None     ,			):
										super().__init__()

										lowerCamelCase__ =nn.Linear(_lowerCamelCase     ,			_lowerCamelCase				)

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

										lowerCamelCase__ =get_activation(_lowerCamelCase				)

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

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






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=None				):
										if condition is not None:
																lowerCamelCase__ =sample + self.cond_proj(_lowerCamelCase				)
										lowerCamelCase__ =self.linear_a(_lowerCamelCase				)

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

										lowerCamelCase__ =self.linear_a(_lowerCamelCase				)

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										super().__init__()
										lowerCamelCase__ =num_channels
										lowerCamelCase__ =flip_sin_to_cos
										lowerCamelCase__ =downscale_freq_shift






				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =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        __UpperCAmelCase						(     nn.Module     ):

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

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

																lowerCamelCase__ =self.W






				def 		_a     (				self     ,			_lowerCamelCase				):
										if self.log:
																lowerCamelCase__ =torch.log(_lowerCamelCase				)

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

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										super().__init__()

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

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






				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =self.emb(_lowerCamelCase				)

										lowerCamelCase__ =self.height_emb(torch.arange(self.height     ,			device=index.device				).view(1     ,			self.height				)				)

										# 1 x H x D -> 1 x H x 1 x D
										lowerCamelCase__ =height_emb.unsqueeze(2				)

										lowerCamelCase__ =self.width_emb(torch.arange(self.width     ,			device=index.device				).view(1     ,			self.width				)				)

										# 1 x W x D -> 1 x 1 x W x D
										lowerCamelCase__ =width_emb.unsqueeze(1				)

										lowerCamelCase__ =height_emb + width_emb

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

										lowerCamelCase__ =emb + pos_emb[:, : emb.shape[1], :]

										return emb






class        __UpperCAmelCase						(     nn.Module     ):

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

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=None				):
										if force_drop_ids is None:
																lowerCamelCase__ =torch.rand(labels.shape[0]     ,			device=labels.device				) < self.dropout_prob
										else:
																lowerCamelCase__ =torch.tensor(force_drop_ids == 1				)
										lowerCamelCase__ =torch.where(_lowerCamelCase     ,			self.num_classes     ,			_lowerCamelCase				)
										return labels






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=None				):
										lowerCamelCase__ =self.dropout_prob > 0
										if (self.training and use_dropout) or (force_drop_ids is not None):
																lowerCamelCase__ =self.token_drop(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =self.embedding_table(_lowerCamelCase				)
										return embeddings






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase = 1024     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 10     ,			):
										super().__init__()

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






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =text_embeds.shape[0]

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

										# text
										lowerCamelCase__ =self.text_proj(_lowerCamelCase				)

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 32     ,			):
										super().__init__()

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






				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =image_embeds.shape[0]

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=0.1				):
										super().__init__()

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






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=None				):
										lowerCamelCase__ =self.time_proj(_lowerCamelCase				)
										lowerCamelCase__ =self.timestep_embedder(timesteps_proj.to(dtype=_lowerCamelCase				)				)  # (N, D)

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

										lowerCamelCase__ =timesteps_emb + class_labels  # (N, D)

										return conditioning






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = 64				):
										super().__init__()
										lowerCamelCase__ =nn.LayerNorm(_lowerCamelCase				)
										lowerCamelCase__ =AttentionPooling(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =nn.Linear(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =nn.LayerNorm(_lowerCamelCase				)






				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =self.norma(_lowerCamelCase				)
										lowerCamelCase__ =self.pool(_lowerCamelCase				)
										lowerCamelCase__ =self.proj(_lowerCamelCase				)
										lowerCamelCase__ =self.norma(_lowerCamelCase				)
										return hidden_states






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 1536				):
										super().__init__()
										lowerCamelCase__ =nn.Linear(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =nn.LayerNorm(_lowerCamelCase				)
										lowerCamelCase__ =nn.Linear(_lowerCamelCase     ,			_lowerCamelCase				)






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										# text
										lowerCamelCase__ =self.text_proj(_lowerCamelCase				)
										lowerCamelCase__ =self.text_norm(_lowerCamelCase				)

										# image
										lowerCamelCase__ =self.image_proj(_lowerCamelCase				)

										return time_image_embeds + time_text_embeds






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 1536				):
										super().__init__()
										lowerCamelCase__ =nn.Linear(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =nn.LayerNorm(_lowerCamelCase				)






				def 		_a     (				self     ,			_lowerCamelCase				):
										# image
										lowerCamelCase__ =self.image_proj(_lowerCamelCase				)
										lowerCamelCase__ =self.image_norm(_lowerCamelCase				)
										return time_image_embeds






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase = 768     ,			_lowerCamelCase = 1536				):
										super().__init__()
										lowerCamelCase__ =nn.Linear(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =nn.LayerNorm(_lowerCamelCase				)
										lowerCamelCase__ =nn.Sequential(
										    nn.Convad(3     ,			16     ,			3     ,			padding=1				)     ,			nn.SiLU()     ,			nn.Convad(16     ,			16     ,			3     ,			padding=1				)     ,			nn.SiLU()     ,			nn.Convad(16     ,			32     ,			3     ,			padding=1     ,			stride=2				)     ,			nn.SiLU()     ,			nn.Convad(32     ,			32     ,			3     ,			padding=1				)     ,			nn.SiLU()     ,			nn.Convad(32     ,			96     ,			3     ,			padding=1     ,			stride=2				)     ,			nn.SiLU()     ,			nn.Convad(96     ,			96     ,			3     ,			padding=1				)     ,			nn.SiLU()     ,			nn.Convad(96     ,			256     ,			3     ,			padding=1     ,			stride=2				)     ,			nn.SiLU()     ,			nn.Convad(256     ,			4     ,			3     ,			padding=1				)     ,			)






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										# image
										lowerCamelCase__ =self.image_proj(_lowerCamelCase				)
										lowerCamelCase__ =self.image_norm(_lowerCamelCase				)
										lowerCamelCase__ =self.input_hint_block(_lowerCamelCase				)
										return time_image_embeds, hint






class        __UpperCAmelCase						(     nn.Module     ):

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






				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ =x.size()

										def shape(_lowerCamelCase				):
																# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																lowerCamelCase__ =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)
																lowerCamelCase__ =x.transpose(1     ,			2				)
																# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																lowerCamelCase__ =x.reshape(bs * self.num_heads     ,			-1     ,			self.dim_per_head				)
																# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
																lowerCamelCase__ =x.transpose(1     ,			2				)
																return x

										lowerCamelCase__ =x.mean(dim=1     ,			keepdim=_lowerCamelCase				) + self.positional_embedding.to(x.dtype				)
										lowerCamelCase__ =torch.cat([class_token, x]     ,			dim=1				)  # (bs, length+1, width)

										# (bs*n_heads, class_token_length, dim_per_head)
										lowerCamelCase__ =shape(self.q_proj(_lowerCamelCase				)				)
										# (bs*n_heads, length+class_token_length, dim_per_head)
										lowerCamelCase__ =shape(self.k_proj(_lowerCamelCase				)				)
										lowerCamelCase__ =shape(self.v_proj(_lowerCamelCase				)				)

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

										# (bs*n_heads, dim_per_head, class_token_length)
										lowerCamelCase__ =torch.einsum("bts,bcs->bct"     ,			_lowerCamelCase     ,			_lowerCamelCase				)

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

										return a[:, 0, :]  # cls_token



"""simple docstring"""


from __future__ import annotations

from enum import Enum






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       List[Any]  							=	2_4
				A__				:       List[str]  							=	2_1
				A__				:       List[Any]  							=	1_8
				A__				:       Optional[Any]  							=	1_5
				A__				:       List[Any]  							=	1_2
				A__				:       Optional[int]  							=	9
				A__				:       List[Any]  							=	6
				A__				:       int  							=	3
				A__				:       Optional[int]  							=	2
				A__				:       int  							=	1
				A__				:       Optional[int]  							=	-1
				A__				:       Dict  							=	-2
				A__				:       Optional[Any]  							=	-3
				A__				:       str  							=	-6
				A__				:       Union[str, Any]  							=	-9
				A__				:       Optional[Any]  							=	-1_2
				A__				:       int  							=	-1_5
				A__				:       Union[str, Any]  							=	-1_8
				A__				:       Any  							=	-2_1
				A__				:       Union[str, Any]  							=	-2_4






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Optional[int]  							=	8
				A__				:       Any  							=	7
				A__				:       Any  							=	6
				A__				:       Tuple  							=	5
				A__				:       Optional[Any]  							=	4
				A__				:       str  							=	3
				A__				:       Dict  							=	2
				A__				:       Tuple  							=	1





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> float:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =SIUnit[known_prefix.lower()]
						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =SIUnit[unknown_prefix.lower()]
						lowerCamelCase__ =known_amount * (
						    10 ** (known_prefix.value - unknown_prefix.value)
						)
						return unknown_amount





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> float:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =BinaryUnit[known_prefix.lower()]
						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =BinaryUnit[unknown_prefix.lower()]
						lowerCamelCase__ =known_amount * (
						    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
						)
						return unknown_amount


if __name__ == "__main__":
	import doctest

	doctest.testmod()


