



"""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__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__ = False       ,							UpperCamelCase__ = 1       ,							UpperCamelCase__ = 1       ,							UpperCamelCase__ = 10_000       ,							):


  '''simple docstring'''


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

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

  _SCREAMING_SNAKE_CASE        =			torch.exp(snake_case__    )
  _SCREAMING_SNAKE_CASE        =			timesteps[:, None].float() * emb[None, :]

  # scale embeddings
  _SCREAMING_SNAKE_CASE        =			scale * emb

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

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

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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=False       ,							UpperCamelCase__=0    ):


  '''simple docstring'''


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

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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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

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

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


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


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

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

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

  _SCREAMING_SNAKE_CASE        =			np.sin(snake_case__    )  # (M, D/2)
  _SCREAMING_SNAKE_CASE        =			np.cos(snake_case__    )  # (M, D/2)

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




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_=224    ,							A_=224    ,							A_=16    ,							A_=3    ,							A_=768    ,							A_=False    ,							A_=True    ,							A_=True    ,							):





      '''simple docstring'''



      super().__init__()

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

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

      _SCREAMING_SNAKE_CASE        =			get_ad_sincos_pos_embed(lowercase__    ,							int(num_patches**0.5			)			)
      self.register_buffer('''pos_embed'''    ,							torch.from_numpy(lowercase__			).float().unsqueeze(0			)    ,							persistent=lowercase__			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



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




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_ = "silu"    ,							A_ = None    ,							A_ = None    ,							A_=None    ,							):





      '''simple docstring'''



      super().__init__()

      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)

      if cond_proj_dim is not None:
        _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__    ,							bias=lowercase__			)
      else:
        _SCREAMING_SNAKE_CASE        =			None

      _SCREAMING_SNAKE_CASE        =			get_activation(lowercase__			)

      if out_dim is not None:
        _SCREAMING_SNAKE_CASE        =			out_dim
      else:
        _SCREAMING_SNAKE_CASE        =			time_embed_dim
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)

      if post_act_fn is None:
        _SCREAMING_SNAKE_CASE        =			None
      else:
        _SCREAMING_SNAKE_CASE        =			get_activation(lowercase__			)




    def   A   (   self    ,							A_    ,							A_=None			):





      '''simple docstring'''



      if condition is not None:
        _SCREAMING_SNAKE_CASE        =			sample + self.cond_proj(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.linear_a(lowercase__			)

      if self.act is not None:
        _SCREAMING_SNAKE_CASE        =			self.act(lowercase__			)

      _SCREAMING_SNAKE_CASE        =			self.linear_a(lowercase__			)

      if self.post_act is not None:
        _SCREAMING_SNAKE_CASE        =			self.post_act(lowercase__			)
      return sample




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			num_channels
      _SCREAMING_SNAKE_CASE        =			flip_sin_to_cos
      _SCREAMING_SNAKE_CASE        =			downscale_freq_shift




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			get_timestep_embedding(
          lowercase__    ,							self.num_channels    ,							flip_sin_to_cos=self.flip_sin_to_cos    ,							downscale_freq_shift=self.downscale_freq_shift    ,							)
      return t_emb




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_ = 256    ,							A_ = 1.0    ,							A_=True    ,							A_=True    ,							A_=False			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			nn.Parameter(torch.randn(lowercase__			) * scale    ,							requires_grad=lowercase__			)
      _SCREAMING_SNAKE_CASE        =			log
      _SCREAMING_SNAKE_CASE        =			flip_sin_to_cos

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

        _SCREAMING_SNAKE_CASE        =			self.W




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      if self.log:
        _SCREAMING_SNAKE_CASE        =			torch.log(lowercase__			)

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

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




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_    ,							A_    ,							):





      '''simple docstring'''



      super().__init__()

      _SCREAMING_SNAKE_CASE        =			height
      _SCREAMING_SNAKE_CASE        =			width
      _SCREAMING_SNAKE_CASE        =			num_embed
      _SCREAMING_SNAKE_CASE        =			embed_dim

      _SCREAMING_SNAKE_CASE        =			nn.Embedding(self.num_embed    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.Embedding(self.height    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.Embedding(self.width    ,							lowercase__			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.emb(lowercase__			)

      _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			height_emb.unsqueeze(2			)

      _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			width_emb.unsqueeze(1			)

      _SCREAMING_SNAKE_CASE        =			height_emb + width_emb

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

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

      return emb




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			dropout_prob > 0
      _SCREAMING_SNAKE_CASE        =			nn.Embedding(num_classes + use_cfg_embedding    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			num_classes
      _SCREAMING_SNAKE_CASE        =			dropout_prob




    def   A   (   self    ,							A_    ,							A_=None			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_    ,							A_=None			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dropout_prob > 0
      if (self.training and use_dropout) or (force_drop_ids is not None):
        _SCREAMING_SNAKE_CASE        =			self.token_drop(lowercase__    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.embedding_table(lowercase__			)
      return embeddings




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_ = 1_024    ,							A_ = 768    ,							A_ = 768    ,							A_ = 10    ,							):





      '''simple docstring'''



      super().__init__()

      _SCREAMING_SNAKE_CASE        =			num_image_text_embeds
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							self.num_image_text_embeds * cross_attention_dim			)
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			text_embeds.shape[0]

      # image
      _SCREAMING_SNAKE_CASE        =			self.image_embeds(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			image_text_embeds.reshape(lowercase__    ,							self.num_image_text_embeds    ,							-1			)

      # text
      _SCREAMING_SNAKE_CASE        =			self.text_proj(lowercase__			)

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




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_ = 768    ,							A_ = 768    ,							A_ = 32    ,							):





      '''simple docstring'''



      super().__init__()

      _SCREAMING_SNAKE_CASE        =			num_image_text_embeds
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							self.num_image_text_embeds * cross_attention_dim			)
      _SCREAMING_SNAKE_CASE        =			nn.LayerNorm(lowercase__			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			image_embeds.shape[0]

      # image
      _SCREAMING_SNAKE_CASE        =			self.image_embeds(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			image_embeds.reshape(lowercase__    ,							self.num_image_text_embeds    ,							-1			)
      _SCREAMING_SNAKE_CASE        =			self.norm(lowercase__			)
      return image_embeds




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_=0.1			):





      '''simple docstring'''



      super().__init__()

      _SCREAMING_SNAKE_CASE        =			Timesteps(num_channels=256    ,							flip_sin_to_cos=lowercase__    ,							downscale_freq_shift=1			)
      _SCREAMING_SNAKE_CASE        =			TimestepEmbedding(in_channels=256    ,							time_embed_dim=lowercase__			)
      _SCREAMING_SNAKE_CASE        =			LabelEmbedding(lowercase__    ,							lowercase__    ,							lowercase__			)




    def   A   (   self    ,							A_    ,							A_    ,							A_=None			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.time_proj(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.timestep_embedder(timesteps_proj.to(dtype=lowercase__			)			)  # (N, D)

      _SCREAMING_SNAKE_CASE        =			self.class_embedder(lowercase__			)  # (N, D)

      _SCREAMING_SNAKE_CASE        =			timesteps_emb + class_labels  # (N, D)

      return conditioning




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_ = 64			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			nn.LayerNorm(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			AttentionPooling(lowercase__    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.LayerNorm(lowercase__			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.norma(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.pool(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.proj(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.norma(lowercase__			)
      return hidden_states




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_ = 768    ,							A_ = 768    ,							A_ = 1_536			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.LayerNorm(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.text_proj(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.text_norm(lowercase__			)

      # image
      _SCREAMING_SNAKE_CASE        =			self.image_proj(lowercase__			)

      return time_image_embeds + time_text_embeds




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_ = 768    ,							A_ = 1_536			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.LayerNorm(lowercase__			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.image_proj(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.image_norm(lowercase__			)
      return time_image_embeds




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_ = 768    ,							A_ = 1_536			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__			)
      _SCREAMING_SNAKE_CASE        =			nn.LayerNorm(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			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    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.image_proj(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.image_norm(lowercase__			)
      _SCREAMING_SNAKE_CASE        =			self.input_hint_block(lowercase__			)
      return time_image_embeds, hint




class    __snake_case(					nn.Module       ):




    def __init__(   self    ,							A_    ,							A_    ,							A_=None			):





      '''simple docstring'''



      super().__init__()
      _SCREAMING_SNAKE_CASE        =			dtype
      _SCREAMING_SNAKE_CASE        =			nn.Parameter(torch.randn(1    ,							lowercase__			) / embed_dim**0.5			)
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__    ,							dtype=self.dtype			)
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__    ,							dtype=self.dtype			)
      _SCREAMING_SNAKE_CASE        =			nn.Linear(lowercase__    ,							lowercase__    ,							dtype=self.dtype			)
      _SCREAMING_SNAKE_CASE        =			num_heads
      _SCREAMING_SNAKE_CASE        =			embed_dim // self.num_heads




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			x.size()

      def shape(A_			):
        # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
        _SCREAMING_SNAKE_CASE        =			x.view(lowercase__    ,							-1    ,							self.num_heads    ,							self.dim_per_head			)
        # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
        _SCREAMING_SNAKE_CASE        =			x.transpose(1    ,							2			)
        # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
        _SCREAMING_SNAKE_CASE        =			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)
        _SCREAMING_SNAKE_CASE        =			x.transpose(1    ,							2			)
        return x

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

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

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

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

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

      return a[:, 0, :]  # cls_token








'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


