


'''simple docstring'''



from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather




@overload
def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      None    ):





     '''simple docstring'''






     ...




@overload
def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor    ):





     '''simple docstring'''






     ...




def       _a      (		_lowercase   :      Tuple     ,   _lowercase   :      str     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[int]    				=     aatype == rc.restype_order['''G''']
     __UpperCAmelCase      :					Optional[Any]    				=     rc.atom_order['''CA''']
     __UpperCAmelCase      :					Union[str, Any]    				=     rc.atom_order['''CB''']
     __UpperCAmelCase      :					Optional[int]    				=     torch.where(
         is_gly[..., None].expand(*((-1,) * len(is_gly.shape    ))     ,   3    )     ,   all_atom_positions[..., ca_idx, :]     ,   all_atom_positions[..., cb_idx, :]     ,   )

     if all_atom_masks is not None:
          __UpperCAmelCase      :					List[str]    				=     torch.where(
              _lowercase     ,   all_atom_masks[..., ca_idx]     ,   all_atom_masks[..., cb_idx]     ,   )
          return pseudo_beta, pseudo_beta_mask
     else:
          return pseudo_beta




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      Dict[str, torch.Tensor]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Tuple    				=     batched_gather(
         _lowercase     ,   batch['''residx_atom37_to_atom14''']     ,   dim=-2     ,   no_batch_dims=len(atomaa.shape[:-2]    )     ,   )

     __UpperCAmelCase      :					Optional[Any]    				=     atomaa_data * batch['''atom37_atom_exists'''][..., None]

     return atomaa_data




def       _a      (		_lowercase   :      Dict[str, torch.Tensor]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     template_feats['''template_aatype''']
     __UpperCAmelCase      :					Optional[int]    				=     template_feats['''template_torsion_angles_sin_cos''']
     __UpperCAmelCase      :					Dict    				=     template_feats['''template_alt_torsion_angles_sin_cos''']
     __UpperCAmelCase      :					Any    				=     template_feats['''template_torsion_angles_mask''']
     __UpperCAmelCase      :					str    				=     torch.cat(
         [
             nn.functional.one_hot(_lowercase     ,   22    ),
             torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]     ,   14    ),
             alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]     ,   14    ),
             torsion_angles_mask,
         ]     ,   dim=-1     ,   )

     return template_angle_feat




def       _a      (		_lowercase   :      Dict[str, torch.Tensor]     ,   _lowercase   :      torch.types.Number     ,   _lowercase   :      torch.types.Number     ,   _lowercase   :      int     ,   _lowercase   :      bool = False     ,   _lowercase   :      float = 1E-20     ,   _lowercase   :      float = 1E8     ,   ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[Any]    				=     batch['''template_pseudo_beta_mask''']
     __UpperCAmelCase      :					int    				=     template_mask[..., None] * template_mask[..., None, :]

     # Compute distogram (this seems to differ slightly from Alg. 5)
     __UpperCAmelCase      :					Any    				=     batch['''template_pseudo_beta''']
     __UpperCAmelCase      :					int    				=     torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2     ,   dim=-1     ,   keepdim=_lowercase    )
     __UpperCAmelCase      :					str    				=     torch.linspace(_lowercase     ,   _lowercase     ,   _lowercase     ,   device=tpb.device    ) ** 2
     __UpperCAmelCase      :					List[Any]    				=     torch.cat([lower[1:], lower.new_tensor([inf]    )]     ,   dim=-1    )
     __UpperCAmelCase      :					Tuple    				=     ((dgram > lower) * (dgram < upper)).type(dgram.dtype    )

     __UpperCAmelCase      :					Optional[Any]    				=     [dgram, template_mask_ad[..., None]]

     __UpperCAmelCase      :					torch.LongTensor    				=     nn.functional.one_hot(
         batch['''template_aatype''']     ,   rc.restype_num + 2     ,   )

     __UpperCAmelCase      :					List[Any]    				=     batch['''template_aatype'''].shape[-1]
     to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]     ,   _lowercase     ,   -1     ,   -1    )    )
     to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]     ,   -1     ,   _lowercase     ,   -1    )    )

     __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					str    				=     [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
     __UpperCAmelCase      :					int    				=     Rigid.make_transform_from_reference(
         n_xyz=batch['''template_all_atom_positions'''][..., n, :]     ,   ca_xyz=batch['''template_all_atom_positions'''][..., ca, :]     ,   c_xyz=batch['''template_all_atom_positions'''][..., c, :]     ,   eps=_lowercase     ,   )
     __UpperCAmelCase      :					Any    				=     rigids.get_trans()[..., None, :, :]
     __UpperCAmelCase      :					Dict    				=     rigids[..., None].invert_apply(_lowercase    )

     __UpperCAmelCase      :					Dict    				=     torch.rsqrt(eps + torch.sum(rigid_vec**2     ,   dim=-1    )    )

     __UpperCAmelCase      :					int    				=     batch['''template_all_atom_mask''']
     __UpperCAmelCase      :					Any    				=     t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
     __UpperCAmelCase      :					Optional[Any]    				=     template_mask[..., None] * template_mask[..., None, :]

     __UpperCAmelCase      :					Tuple    				=     inv_distance_scalar * template_mask_ad
     __UpperCAmelCase      :					Union[str, Any]    				=     rigid_vec * inv_distance_scalar[..., None]

     if not use_unit_vector:
          __UpperCAmelCase      :					int    				=     unit_vector * 0.0

     to_concat.extend(torch.unbind(unit_vector[..., None, :]     ,   dim=-1    )    )
     to_concat.append(template_mask_ad[..., None]    )

     __UpperCAmelCase      :					Dict    				=     torch.cat(_lowercase     ,   dim=-1    )
     __UpperCAmelCase      :					Union[str, Any]    				=     act * template_mask_ad[..., None]

     return act




def       _a      (		_lowercase   :      Dict[str, torch.Tensor]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					torch.LongTensor    				=     nn.functional.one_hot(batch['''extra_msa''']     ,   23    )
     __UpperCAmelCase      :					Dict    				=     [
         msa_ahot,
         batch['''extra_has_deletion'''].unsqueeze(-1    ),
         batch['''extra_deletion_value'''].unsqueeze(-1    ),
     ]
     return torch.cat(_lowercase     ,   dim=-1    )




def       _a      (		_lowercase   :      Rigid     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   ):





     '''simple docstring'''






     __UpperCAmelCase      :					Tuple    				=     rrgdf[aatype, ...]

     # [*, N, 8] transformations, i.e.
     #   One [*, N, 8, 3, 3] rotation matrix and
     #   One [*, N, 8, 3]    translation matrix
     __UpperCAmelCase      :					Dict    				=     r.from_tensor_axa(_lowercase    )

     __UpperCAmelCase      :					Union[str, Any]    				=     alpha.new_zeros((*((1,) * len(alpha.shape[:-1]    )), 2)    )
     __UpperCAmelCase      :					Optional[Any]    				=     1

     # [*, N, 8, 2]
     __UpperCAmelCase      :					Dict    				=     torch.cat([bb_rot.expand(*alpha.shape[:-2]     ,   -1     ,   -1    ), alpha]     ,   dim=-2    )

     # [*, N, 8, 3, 3]
     # Produces rotation matrices of the form:
     # [
     #   [1, 0  , 0  ],
     #   [0, a_2,-a_1],
     #   [0, a_1, a_2]
     # ]
     # This follows the original code rather than the supplement, which uses
     # different indices.

     __UpperCAmelCase      :					Optional[int]    				=     alpha.new_zeros(default_r.get_rots().get_rot_mats().shape    )
     __UpperCAmelCase      :					Union[str, Any]    				=     1
     __UpperCAmelCase      :					Optional[Any]    				=     alpha[..., 1]
     __UpperCAmelCase      :					Tuple    				=     -alpha[..., 0]
     __UpperCAmelCase      :					str    				=     alpha

     __UpperCAmelCase      :					List[Any]    				=     default_r.compose(Rigid(Rotation(rot_mats=_lowercase    )     ,   _lowercase    )    )

     __UpperCAmelCase      :					List[Any]    				=     all_frames[..., 5]
     __UpperCAmelCase      :					Tuple    				=     all_frames[..., 6]
     __UpperCAmelCase      :					Dict    				=     all_frames[..., 7]

     __UpperCAmelCase      :					str    				=     all_frames[..., 4]
     __UpperCAmelCase      :					int    				=     chia_frame_to_bb.compose(_lowercase    )
     __UpperCAmelCase      :					List[Any]    				=     chia_frame_to_bb.compose(_lowercase    )
     __UpperCAmelCase      :					Dict    				=     chia_frame_to_bb.compose(_lowercase    )

     __UpperCAmelCase      :					List[Any]    				=     Rigid.cat(
         [
             all_frames[..., :5],
             chia_frame_to_bb.unsqueeze(-1    ),
             chia_frame_to_bb.unsqueeze(-1    ),
             chia_frame_to_bb.unsqueeze(-1    ),
         ]     ,   dim=-1     ,   )

     __UpperCAmelCase      :					List[str]    				=     r[..., None].compose(_lowercase    )

     return all_frames_to_global




def       _a      (		_lowercase   :      Rigid     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   _lowercase   :      torch.Tensor     ,   ):





     '''simple docstring'''






     __UpperCAmelCase      :					int    				=     group_idx[aatype, ...]

     # [*, N, 14, 8]
     __UpperCAmelCase      :					torch.LongTensor    				=     nn.functional.one_hot(
         _lowercase     ,   num_classes=default_frames.shape[-3]     ,   )

     # [*, N, 14, 8]
     __UpperCAmelCase      :					Any    				=     r[..., None, :] * group_mask_one_hot

     # [*, N, 14]
     __UpperCAmelCase      :					List[str]    				=     t_atoms_to_global.map_tensor_fn(lambda _lowercase    : torch.sum(_lowercase     ,   dim=-1    )    )

     # [*, N, 14, 1]
     __UpperCAmelCase      :					Union[str, Any]    				=     atom_mask[aatype, ...].unsqueeze(-1    )

     # [*, N, 14, 3]
     __UpperCAmelCase      :					Tuple    				=     lit_positions[aatype, ...]
     __UpperCAmelCase      :					Union[str, Any]    				=     t_atoms_to_global.apply(_lowercase    )
     __UpperCAmelCase      :					Optional[int]    				=     pred_positions * atom_mask

     return pred_positions



'''simple docstring'''



import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def       _a      (		_lowercase   :      torch.Tensor     ,   _lowercase   :      int     ,   _lowercase   :      bool = False     ,   _lowercase   :      float = 1     ,   _lowercase   :      float = 1     ,   _lowercase   :      int = 10000     ,   ):





     '''simple docstring'''






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

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

     __UpperCAmelCase      :					Optional[int]    				=     torch.exp(_lowercase    )
     __UpperCAmelCase      :					Union[str, Any]    				=     timesteps[:, None].float() * emb[None, :]

     # scale embeddings
     __UpperCAmelCase      :					List[str]    				=     scale * emb

     # concat sine and cosine embeddings
     __UpperCAmelCase      :					str    				=     torch.cat([torch.sin(_lowercase    ), torch.cos(_lowercase    )]     ,   dim=-1    )

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

     # zero pad
     if embedding_dim % 2 == 1:
          __UpperCAmelCase      :					Optional[Any]    				=     torch.nn.functional.pad(_lowercase     ,   (0, 1, 0, 0)    )
     return emb




def       _a      (		_lowercase   :      str     ,   _lowercase   :      Union[str, Any]     ,   _lowercase   :      Optional[Any]=False     ,   _lowercase   :      Optional[Any]=0    ):





     '''simple docstring'''






     __UpperCAmelCase      :					int    				=     np.arange(_lowercase     ,   dtype=np.floataa    )
     __UpperCAmelCase      :					List[Any]    				=     np.arange(_lowercase     ,   dtype=np.floataa    )
     __UpperCAmelCase      :					Optional[int]    				=     np.meshgrid(_lowercase     ,   _lowercase    )  # here w goes first
     __UpperCAmelCase      :					int    				=     np.stack(_lowercase     ,   axis=0    )

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




def       _a      (		_lowercase   :      Dict     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






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

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

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




def       _a      (		_lowercase   :      str     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






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

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

     __UpperCAmelCase      :					int    				=     pos.reshape(-1    )  # (M,)
     __UpperCAmelCase      :					str    				=     np.einsum('''m,d->md'''     ,   _lowercase     ,   _lowercase    )  # (M, D/2), outer product

     __UpperCAmelCase      :					Dict    				=     np.sin(_lowercase    )  # (M, D/2)
     __UpperCAmelCase      :					List[str]    				=     np.cos(_lowercase    )  # (M, D/2)

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



class       a							(						nn.Module			):


     """simple docstring"""






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

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

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

          __UpperCAmelCase      :					Union[str, Any]    				=     get_ad_sincos_pos_embed(snake_case				,					int(num_patches**0.5     )     )
          self.register_buffer('''pos_embed'''				,					torch.from_numpy(snake_case     ).float().unsqueeze(0     )				,					persistent=snake_case     )






     def 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						List[Any]     )		->				List[Any]:
          __UpperCAmelCase      :					List[Any]    				=     self.proj(snake_case     )
          if self.flatten:
               __UpperCAmelCase      :					int    				=     latent.flatten(2     ).transpose(1				,					2     )  # BCHW -> BNC
          if self.layer_norm:
               __UpperCAmelCase      :					List[Any]    				=     self.norm(snake_case     )
          return latent + self.pos_embed



class       a							(						nn.Module			):


     """simple docstring"""






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

          __UpperCAmelCase      :					Dict    				=     nn.Linear(snake_case				,					snake_case     )

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

          __UpperCAmelCase      :					Any    				=     get_activation(snake_case     )

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

          if post_act_fn is None:
               __UpperCAmelCase      :					Optional[int]    				=     None
          else:
               __UpperCAmelCase      :					Tuple    				=     get_activation(snake_case     )






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						Dict				,					snake_case	:						Optional[int]=None     )		->				List[str]:
          if condition is not None:
               __UpperCAmelCase      :					str    				=     sample + self.cond_proj(snake_case     )
          __UpperCAmelCase      :					List[str]    				=     self.linear_a(snake_case     )

          if self.act is not None:
               __UpperCAmelCase      :					Any    				=     self.act(snake_case     )

          __UpperCAmelCase      :					Dict    				=     self.linear_a(snake_case     )

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



class       a							(						nn.Module			):


     """simple docstring"""






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






     def 					lowerCamelCase__					(							self	:						str				,					snake_case	:						Tuple     )		->				List[Any]:
          __UpperCAmelCase      :					Optional[int]    				=     get_timestep_embedding(
              snake_case				,					self.num_channels				,					flip_sin_to_cos=self.flip_sin_to_cos				,					downscale_freq_shift=self.downscale_freq_shift				,					)
          return t_emb



class       a							(						nn.Module			):


     """simple docstring"""






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

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

               __UpperCAmelCase      :					Tuple    				=     self.W






     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int     )		->				List[Any]:
          if self.log:
               __UpperCAmelCase      :					Union[str, Any]    				=     torch.log(snake_case     )

          __UpperCAmelCase      :					List[Any]    				=     x[:, None] * self.weight[None, :] * 2 * np.pi

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



class       a							(						nn.Module			):


     """simple docstring"""






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

          __UpperCAmelCase      :					Dict    				=     height
          __UpperCAmelCase      :					int    				=     width
          __UpperCAmelCase      :					Dict    				=     num_embed
          __UpperCAmelCase      :					str    				=     embed_dim

          __UpperCAmelCase      :					Optional[Any]    				=     nn.Embedding(self.num_embed				,					snake_case     )
          __UpperCAmelCase      :					Tuple    				=     nn.Embedding(self.height				,					snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     nn.Embedding(self.width				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						Optional[int]     )		->				Dict:
          __UpperCAmelCase      :					Tuple    				=     self.emb(snake_case     )

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

          __UpperCAmelCase      :					Union[str, Any]    				=     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[Any]    				=     width_emb.unsqueeze(1     )

          __UpperCAmelCase      :					List[Any]    				=     height_emb + width_emb

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

          __UpperCAmelCase      :					Optional[Any]    				=     emb + pos_emb[:, : emb.shape[1], :]

          return emb



class       a							(						nn.Module			):


     """simple docstring"""






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

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






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



class       a							(						nn.Module			):


     """simple docstring"""






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

          __UpperCAmelCase      :					int    				=     num_image_text_embeds
          __UpperCAmelCase      :					Optional[Any]    				=     nn.Linear(snake_case				,					self.num_image_text_embeds * cross_attention_dim     )
          __UpperCAmelCase      :					Optional[Any]    				=     nn.Linear(snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				int:
          __UpperCAmelCase      :					Tuple    				=     text_embeds.shape[0]

          # image
          __UpperCAmelCase      :					Tuple    				=     self.image_embeds(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     image_text_embeds.reshape(snake_case				,					self.num_image_text_embeds				,					-1     )

          # text
          __UpperCAmelCase      :					Optional[Any]    				=     self.text_proj(snake_case     )

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



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Optional[int]				,					snake_case	:						int = 768				,					snake_case	:						int = 768				,					snake_case	:						int = 32				,					)		->				Optional[Any]:
          super().__init__()

          __UpperCAmelCase      :					List[str]    				=     num_image_text_embeds
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					self.num_image_text_embeds * cross_attention_dim     )
          __UpperCAmelCase      :					int    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						Optional[int]				,					snake_case	:						torch.FloatTensor     )		->				Union[str, Any]:
          __UpperCAmelCase      :					Tuple    				=     image_embeds.shape[0]

          # image
          __UpperCAmelCase      :					Any    				=     self.image_embeds(snake_case     )
          __UpperCAmelCase      :					List[Any]    				=     image_embeds.reshape(snake_case				,					self.num_image_text_embeds				,					-1     )
          __UpperCAmelCase      :					List[Any]    				=     self.norm(snake_case     )
          return image_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						List[str]				,					snake_case	:						List[Any]				,					snake_case	:						List[str]				,					snake_case	:						str=0.1     )		->				str:
          super().__init__()

          __UpperCAmelCase      :					List[str]    				=     Timesteps(num_channels=256				,					flip_sin_to_cos=snake_case				,					downscale_freq_shift=1     )
          __UpperCAmelCase      :					List[Any]    				=     TimestepEmbedding(in_channels=256				,					time_embed_dim=snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     LabelEmbedding(snake_case				,					snake_case				,					snake_case     )






     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						Any				,					snake_case	:						str				,					snake_case	:						List[str]=None     )		->				Tuple:
          __UpperCAmelCase      :					Tuple    				=     self.time_proj(snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     self.timestep_embedder(timesteps_proj.to(dtype=snake_case     )     )  # (N, D)

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

          __UpperCAmelCase      :					List[str]    				=     timesteps_emb + class_labels  # (N, D)

          return conditioning



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						str				,					snake_case	:						int				,					snake_case	:						int				,					snake_case	:						int = 64     )		->				Dict:
          super().__init__()
          __UpperCAmelCase      :					str    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     AttentionPooling(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Dict    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					Optional[int]    				=     nn.LayerNorm(snake_case     )






     def 					lowerCamelCase__					(							self	:						str				,					snake_case	:						List[str]     )		->				int:
          __UpperCAmelCase      :					str    				=     self.norma(snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.pool(snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     self.proj(snake_case     )
          __UpperCAmelCase      :					Dict    				=     self.norma(snake_case     )
          return hidden_states



class       a							(						nn.Module			):


     """simple docstring"""






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






     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				Optional[int]:
          # text
          __UpperCAmelCase      :					Tuple    				=     self.text_proj(snake_case     )
          __UpperCAmelCase      :					int    				=     self.text_norm(snake_case     )

          # image
          __UpperCAmelCase      :					List[str]    				=     self.image_proj(snake_case     )

          return time_image_embeds + time_text_embeds



class       a							(						nn.Module			):


     """simple docstring"""






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






     def 					lowerCamelCase__					(							self	:						Dict				,					snake_case	:						torch.FloatTensor     )		->				Any:
          # image
          __UpperCAmelCase      :					Dict    				=     self.image_proj(snake_case     )
          __UpperCAmelCase      :					str    				=     self.image_norm(snake_case     )
          return time_image_embeds



class       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						int = 768				,					snake_case	:						int = 1536     )		->				Union[str, Any]:
          super().__init__()
          __UpperCAmelCase      :					Any    				=     nn.Linear(snake_case				,					snake_case     )
          __UpperCAmelCase      :					int    				=     nn.LayerNorm(snake_case     )
          __UpperCAmelCase      :					List[str]    				=     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 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						torch.FloatTensor				,					snake_case	:						torch.FloatTensor     )		->				Dict:
          # image
          __UpperCAmelCase      :					Union[str, Any]    				=     self.image_proj(snake_case     )
          __UpperCAmelCase      :					Optional[Any]    				=     self.image_norm(snake_case     )
          __UpperCAmelCase      :					int    				=     self.input_hint_block(snake_case     )
          return time_image_embeds, hint



class       a							(						nn.Module			):


     """simple docstring"""






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






     def 					lowerCamelCase__					(							self	:						Optional[Any]				,					snake_case	:						List[Any]     )		->				Optional[int]:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					str    				=     x.size()

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

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

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

          # (bs*n_heads, class_token_length, length+class_token_length):
          __UpperCAmelCase      :					List[Any]    				=     1 / math.sqrt(math.sqrt(self.dim_per_head     )     )
          __UpperCAmelCase      :					Dict    				=     torch.einsum('''bct,bcs->bts'''				,					q * scale				,					k * scale     )  # More stable with f16 than dividing afterwards
          __UpperCAmelCase      :					Dict    				=     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'''				,					snake_case				,					snake_case     )

          # (bs, length+1, width)
          __UpperCAmelCase      :					Union[str, Any]    				=     a.reshape(snake_case				,					-1				,					1     ).transpose(1				,					2     )

          return a[:, 0, :]  # cls_token