
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def  A			(   lowercase       ,			lowercase       ,			lowercase = False       ,			lowercase = 1       ,			lowercase = 1       ,			lowercase = 10_000       ,			)  ->    Optional[int]:
		'''simple docstring'''

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

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

		UpperCamelCase            =	torch.exp(__A					)
		UpperCamelCase            =	timesteps[:, None].float() * emb[None, :]

		# scale embeddings
		UpperCamelCase            =	scale * emb

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

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

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


def  A			(   lowercase       ,			lowercase       ,			lowercase=False       ,			lowercase=0					)  ->    str:
		'''simple docstring'''

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

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


def  A			(   lowercase       ,			lowercase					)  ->    Union[str, 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
		UpperCamelCase            =	get_ad_sincos_pos_embed_from_grid(embed_dim // 2       ,			grid[0]					)  # (H*W, D/2)
		UpperCamelCase            =	get_ad_sincos_pos_embed_from_grid(embed_dim // 2       ,			grid[1]					)  # (H*W, D/2)

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


def  A			(   lowercase       ,			lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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

		UpperCamelCase            =	np.arange(embed_dim // 2       ,			dtype=np.floataa					)
		omega /= embed_dim / 2.0
		UpperCamelCase            =	1.0 / 10_000**omega  # (D/2,)

		UpperCamelCase            =	pos.reshape(-1					)  # (M,)
		UpperCamelCase            =	np.einsum('m,d->md'       ,			__A       ,			__A					)  # (M, D/2), outer product

		UpperCamelCase            =	np.sin(__A					)  # (M, D/2)
		UpperCamelCase            =	np.cos(__A					)  # (M, D/2)

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


class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_=224							, A_=224							, A_=16							, A_=3							, A_=768							, A_=False							, A_=True							, A_=True							, )    ->   int:
				"""simple docstring"""
				super().__init__()

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

				UpperCamelCase            =	nn.Convad(
				    lowerCAmelCase__							, lowerCAmelCase__							, kernel_size=(patch_size, patch_size)							, stride=lowerCAmelCase__							, bias=lowerCAmelCase__			)
				if layer_norm:
						UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__							, elementwise_affine=lowerCAmelCase__							, eps=1e-6			)
				else:
						UpperCamelCase            =	None

				UpperCamelCase            =	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							, A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	self.proj(lowerCAmelCase__			)
				if self.flatten:
						UpperCamelCase            =	latent.flatten(2			).transpose(1							, 2			)  # BCHW -> BNC
				if self.layer_norm:
						UpperCamelCase            =	self.norm(lowerCAmelCase__			)
				return latent + self.pos_embed






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_ = "silu"							, A_ = None							, A_ = None							, A_=None							, )    ->   Optional[int]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)

				if cond_proj_dim is not None:
						UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__							, bias=lowerCAmelCase__			)
				else:
						UpperCamelCase            =	None

				UpperCamelCase            =	get_activation(lowerCAmelCase__			)

				if out_dim is not None:
						UpperCamelCase            =	out_dim
				else:
						UpperCamelCase            =	time_embed_dim
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)

				if post_act_fn is None:
						UpperCamelCase            =	None
				else:
						UpperCamelCase            =	get_activation(lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_							, A_=None			)    ->   List[Any]:
				"""simple docstring"""
				if condition is not None:
						UpperCamelCase            =	sample + self.cond_proj(lowerCAmelCase__			)
				UpperCamelCase            =	self.linear_a(lowerCAmelCase__			)

				if self.act is not None:
						UpperCamelCase            =	self.act(lowerCAmelCase__			)

				UpperCamelCase            =	self.linear_a(lowerCAmelCase__			)

				if self.post_act is not None:
						UpperCamelCase            =	self.post_act(lowerCAmelCase__			)
				return sample






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_			)    ->   Dict:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	num_channels
				UpperCamelCase            =	flip_sin_to_cos
				UpperCamelCase            =	downscale_freq_shift



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	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 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_ = 256							, A_ = 1.0							, A_=True							, A_=True							, A_=False			)    ->   int:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	nn.Parameter(torch.randn(lowerCAmelCase__			) * scale							, requires_grad=lowerCAmelCase__			)
				UpperCamelCase            =	log
				UpperCamelCase            =	flip_sin_to_cos

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

						UpperCamelCase            =	self.W



		def __UpperCamelCase					(					self							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				if self.log:
						UpperCamelCase            =	torch.log(lowerCAmelCase__			)

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

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






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_							, A_							, )    ->   Optional[int]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase            =	height
				UpperCamelCase            =	width
				UpperCamelCase            =	num_embed
				UpperCamelCase            =	embed_dim

				UpperCamelCase            =	nn.Embedding(self.num_embed							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.Embedding(self.height							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.Embedding(self.width							, lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.emb(lowerCAmelCase__			)

				UpperCamelCase            =	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            =	height_emb.unsqueeze(2			)

				UpperCamelCase            =	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            =	width_emb.unsqueeze(1			)

				UpperCamelCase            =	height_emb + width_emb

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

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

				return emb






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_			)    ->   int:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	dropout_prob > 0
				UpperCamelCase            =	nn.Embedding(num_classes + use_cfg_embedding							, lowerCAmelCase__			)
				UpperCamelCase            =	num_classes
				UpperCamelCase            =	dropout_prob



		def __UpperCamelCase					(					self							, A_							, A_=None			)    ->   Union[str, Any]:
				"""simple docstring"""
				if force_drop_ids is None:
						UpperCamelCase            =	torch.rand(labels.shape[0]							, device=labels.device			) < self.dropout_prob
				else:
						UpperCamelCase            =	torch.tensor(force_drop_ids == 1			)
				UpperCamelCase            =	torch.where(lowerCAmelCase__							, self.num_classes							, lowerCAmelCase__			)
				return labels



		def __UpperCamelCase					(					self							, A_							, A_=None			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	self.dropout_prob > 0
				if (self.training and use_dropout) or (force_drop_ids is not None):
						UpperCamelCase            =	self.token_drop(lowerCAmelCase__							, lowerCAmelCase__			)
				UpperCamelCase            =	self.embedding_table(lowerCAmelCase__			)
				return embeddings






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_ = 1_024							, A_ = 768							, A_ = 768							, A_ = 10							, )    ->   Union[str, Any]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase            =	num_image_text_embeds
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, self.num_image_text_embeds * cross_attention_dim			)
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	text_embeds.shape[0]

				# image
				UpperCamelCase            =	self.image_embeds(lowerCAmelCase__			)
				UpperCamelCase            =	image_text_embeds.reshape(lowerCAmelCase__							, self.num_image_text_embeds							, -1			)

				# text
				UpperCamelCase            =	self.text_proj(lowerCAmelCase__			)

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






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_ = 768							, A_ = 768							, A_ = 32							, )    ->   Dict:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase            =	num_image_text_embeds
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, self.num_image_text_embeds * cross_attention_dim			)
				UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	image_embeds.shape[0]

				# image
				UpperCamelCase            =	self.image_embeds(lowerCAmelCase__			)
				UpperCamelCase            =	image_embeds.reshape(lowerCAmelCase__							, self.num_image_text_embeds							, -1			)
				UpperCamelCase            =	self.norm(lowerCAmelCase__			)
				return image_embeds






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_=0.1			)    ->   List[Any]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase            =	Timesteps(num_channels=256							, flip_sin_to_cos=lowerCAmelCase__							, downscale_freq_shift=1			)
				UpperCamelCase            =	TimestepEmbedding(in_channels=256							, time_embed_dim=lowerCAmelCase__			)
				UpperCamelCase            =	LabelEmbedding(lowerCAmelCase__							, lowerCAmelCase__							, lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_=None			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.time_proj(lowerCAmelCase__			)
				UpperCamelCase            =	self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase__			)			)  # (N, D)

				UpperCamelCase            =	self.class_embedder(lowerCAmelCase__			)  # (N, D)

				UpperCamelCase            =	timesteps_emb + class_labels  # (N, D)

				return conditioning






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_ = 64			)    ->   int:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__			)
				UpperCamelCase            =	AttentionPooling(lowerCAmelCase__							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	self.norma(lowerCAmelCase__			)
				UpperCamelCase            =	self.pool(lowerCAmelCase__			)
				UpperCamelCase            =	self.proj(lowerCAmelCase__			)
				UpperCamelCase            =	self.norma(lowerCAmelCase__			)
				return hidden_states






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_ = 768							, A_ = 768							, A_ = 1_536			)    ->   Optional[Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__			)
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   List[str]:
				"""simple docstring"""
				# text
				UpperCamelCase            =	self.text_proj(lowerCAmelCase__			)
				UpperCamelCase            =	self.text_norm(lowerCAmelCase__			)

				# image
				UpperCamelCase            =	self.image_proj(lowerCAmelCase__			)

				return time_image_embeds + time_text_embeds






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_ = 768							, A_ = 1_536			)    ->   Optional[Any]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				# image
				UpperCamelCase            =	self.image_proj(lowerCAmelCase__			)
				UpperCamelCase            =	self.image_norm(lowerCAmelCase__			)
				return time_image_embeds






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_ = 768							, A_ = 1_536			)    ->   Any:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__			)
				UpperCamelCase            =	nn.LayerNorm(lowerCAmelCase__			)
				UpperCamelCase            =	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							, A_							, A_			)    ->   str:
				"""simple docstring"""
				# image
				UpperCamelCase            =	self.image_proj(lowerCAmelCase__			)
				UpperCamelCase            =	self.image_norm(lowerCAmelCase__			)
				UpperCamelCase            =	self.input_hint_block(lowerCAmelCase__			)
				return time_image_embeds, hint






class 							lowercase   (						nn.Module  ):



		def __init__(					self							, A_							, A_							, A_=None			)    ->   Any:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase            =	dtype
				UpperCamelCase            =	nn.Parameter(torch.randn(1							, lowerCAmelCase__			) / embed_dim**0.5			)
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__							, dtype=self.dtype			)
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__							, dtype=self.dtype			)
				UpperCamelCase            =	nn.Linear(lowerCAmelCase__							, lowerCAmelCase__							, dtype=self.dtype			)
				UpperCamelCase            =	num_heads
				UpperCamelCase            =	embed_dim // self.num_heads



		def __UpperCamelCase					(					self							, A_			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase      ,    UpperCamelCase            =	x.size()

				def shape(A_			):
						# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
						UpperCamelCase            =	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)
						UpperCamelCase            =	x.transpose(1							, 2			)
						# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
						UpperCamelCase            =	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            =	x.transpose(1							, 2			)
						return x

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

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

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

				# (bs*n_heads, dim_per_head, class_token_length)
				UpperCamelCase            =	torch.einsum('bts,bcs->bct'							, lowerCAmelCase__							, lowerCAmelCase__			)

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

				return a[:, 0, :]  # cls_token



from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_calculate_expected_aligned_error(
      alignment_confidence_breaks=__A ,							aligned_distance_error_probs=__A ,							)

  return {
      "aligned_confidence_probs": aligned_confidence_probs,
      "predicted_aligned_error": predicted_aligned_error,
      "max_predicted_aligned_error": max_predicted_aligned_error,
  }





def   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  torch.sum(__A      )
  UpperCAmelCase          =	logits.shape[-2]
  UpperCAmelCase          =	max(__A ,							19      )

  UpperCAmelCase          =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

  UpperCAmelCase          =	1.0 / (1 + (bin_centers**2) / (da**2))
  UpperCAmelCase          =	torch.sum(probs * tm_per_bin ,							dim=-1      )

  UpperCAmelCase          =	residue_weights / (eps + residue_weights.sum())
  UpperCAmelCase          =	torch.sum(predicted_tm_term * normed_residue_mask ,							dim=-1      )

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



