




'''simple docstring'''



import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def      _UpperCamelCase	(_lowerCamelCase							:       torch.Tensor				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       bool = False				,		_lowerCamelCase							:       float = 1				,		_lowerCamelCase							:       float = 1				,		_lowerCamelCase							:       int = 1_00_00				,		)->				List[Any]:




     '''simple docstring'''





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

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

     __snake_case         =					torch.exp(_lowerCamelCase		)
     __snake_case         =					timesteps[:, None].float() * emb[None, :]

     # scale embeddings
     __snake_case         =					scale * emb

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

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

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







def      _UpperCamelCase	(_lowerCamelCase							:       Tuple				,		_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       Union[str, Any]=False				,		_lowerCamelCase							:       str=0		)->				Tuple:




     '''simple docstring'''





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

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







def      _UpperCamelCase	(_lowerCamelCase							:       Tuple				,		_lowerCamelCase							:       int		)->				Optional[Any]:




     '''simple docstring'''





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

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

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







def      _UpperCamelCase	(_lowerCamelCase							:       Dict				,		_lowerCamelCase							:       Union[str, Any]		)->				Dict:




     '''simple docstring'''





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

     __snake_case         =					np.arange(embed_dim // 2				,		dtype=np.floataa		)
     omega /= embed_dim / 2.0
     __snake_case         =					1.0 / 1_00_00**omega  # (D/2,)

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

     __snake_case         =					np.sin(_lowerCamelCase		)  # (M, D/2)
     __snake_case         =					np.cos(_lowerCamelCase		)  # (M, D/2)

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





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE=224      ,					__SCREAMING_SNAKE_CASE=224      ,					__SCREAMING_SNAKE_CASE=16      ,					__SCREAMING_SNAKE_CASE=3      ,					__SCREAMING_SNAKE_CASE=768      ,					__SCREAMING_SNAKE_CASE=False      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=True      ,					) ->					Any:



            '''simple docstring'''




            super().__init__()

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

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

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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




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





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = "silu"      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE=None      ,					) ->					Optional[Any]:



            '''simple docstring'''




            super().__init__()

            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)

            if cond_proj_dim is not None:
                 __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					bias=__SCREAMING_SNAKE_CASE					)
            else:
                 __snake_case         =					None

            __snake_case         =					get_activation(__SCREAMING_SNAKE_CASE					)

            if out_dim is not None:
                 __snake_case         =					out_dim
            else:
                 __snake_case         =					time_embed_dim
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)

            if post_act_fn is None:
                 __snake_case         =					None
            else:
                 __snake_case         =					get_activation(__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None					) ->					List[str]:



            '''simple docstring'''




            if condition is not None:
                 __snake_case         =					sample + self.cond_proj(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.linear_a(__SCREAMING_SNAKE_CASE					)

            if self.act is not None:
                 __snake_case         =					self.act(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					self.linear_a(__SCREAMING_SNAKE_CASE					)

            if self.post_act is not None:
                 __snake_case         =					self.post_act(__SCREAMING_SNAKE_CASE					)
            return sample





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					get_timestep_embedding(
                __SCREAMING_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       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 256      ,					__SCREAMING_SNAKE_CASE = 1.0      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=False					) ->					Tuple:



            '''simple docstring'''




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

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

                 __snake_case         =					self.W




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Dict:



            '''simple docstring'''




            if self.log:
                 __snake_case         =					torch.log(__SCREAMING_SNAKE_CASE					)

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

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





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					List[str]:



            '''simple docstring'''




            super().__init__()

            __snake_case         =					height
            __snake_case         =					width
            __snake_case         =					num_embed
            __snake_case         =					embed_dim

            __snake_case         =					nn.Embedding(self.num_embed      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.Embedding(self.height      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.Embedding(self.width      ,					__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					self.emb(__SCREAMING_SNAKE_CASE					)

            __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
            __snake_case         =					height_emb.unsqueeze(2					)

            __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
            __snake_case         =					width_emb.unsqueeze(1					)

            __snake_case         =					height_emb + width_emb

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

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

            return emb





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					List[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case         =					dropout_prob > 0
            __snake_case         =					nn.Embedding(num_classes + use_cfg_embedding      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					num_classes
            __snake_case         =					dropout_prob


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None					) ->					Optional[Any]:



            '''simple docstring'''




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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None					) ->					Dict:



            '''simple docstring'''




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





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 1024      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 10      ,					) ->					Optional[int]:



            '''simple docstring'''




            super().__init__()

            __snake_case         =					num_image_text_embeds
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					self.num_image_text_embeds * cross_attention_dim					)
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					text_embeds.shape[0]

            # image
            __snake_case         =					self.image_embeds(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					image_text_embeds.reshape(__SCREAMING_SNAKE_CASE      ,					self.num_image_text_embeds      ,					-1					)

            # text
            __snake_case         =					self.text_proj(__SCREAMING_SNAKE_CASE					)

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





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 32      ,					) ->					Optional[Any]:



            '''simple docstring'''




            super().__init__()

            __snake_case         =					num_image_text_embeds
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					self.num_image_text_embeds * cross_attention_dim					)
            __snake_case         =					nn.LayerNorm(__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					image_embeds.shape[0]

            # image
            __snake_case         =					self.image_embeds(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					image_embeds.reshape(__SCREAMING_SNAKE_CASE      ,					self.num_image_text_embeds      ,					-1					)
            __snake_case         =					self.norm(__SCREAMING_SNAKE_CASE					)
            return image_embeds





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=0.1					) ->					Dict:



            '''simple docstring'''




            super().__init__()

            __snake_case         =					Timesteps(num_channels=256      ,					flip_sin_to_cos=__SCREAMING_SNAKE_CASE      ,					downscale_freq_shift=1					)
            __snake_case         =					TimestepEmbedding(in_channels=256      ,					time_embed_dim=__SCREAMING_SNAKE_CASE					)
            __snake_case         =					LabelEmbedding(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None					) ->					Any:



            '''simple docstring'''




            __snake_case         =					self.time_proj(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.timestep_embedder(timesteps_proj.to(dtype=__SCREAMING_SNAKE_CASE					)					)  # (N, D)

            __snake_case         =					self.class_embedder(__SCREAMING_SNAKE_CASE					)  # (N, D)

            __snake_case         =					timesteps_emb + class_labels  # (N, D)

            return conditioning





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 64					) ->					Optional[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case         =					nn.LayerNorm(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					AttentionPooling(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.LayerNorm(__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            __snake_case         =					self.norma(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.pool(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.proj(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.norma(__SCREAMING_SNAKE_CASE					)
            return hidden_states





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 1536					) ->					str:



            '''simple docstring'''




            super().__init__()
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.LayerNorm(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					self.text_proj(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.text_norm(__SCREAMING_SNAKE_CASE					)

            # image
            __snake_case         =					self.image_proj(__SCREAMING_SNAKE_CASE					)

            return time_image_embeds + time_text_embeds





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 1536					) ->					Optional[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.LayerNorm(__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					self.image_proj(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.image_norm(__SCREAMING_SNAKE_CASE					)
            return time_image_embeds





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 768      ,					__SCREAMING_SNAKE_CASE = 1536					) ->					List[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case         =					nn.Linear(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					nn.LayerNorm(__SCREAMING_SNAKE_CASE					)
            __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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.image_proj(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.image_norm(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					self.input_hint_block(__SCREAMING_SNAKE_CASE					)
            return time_image_embeds, hint





class       lowerCAmelCase				(	nn.Module):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=None					) ->					int:



            '''simple docstring'''




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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case   ,      __snake_case         =					x.size()

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

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

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

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

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

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

            return a[:, 0, :]  # cls_token











'''simple docstring'''



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax





@require_flax
class       lowerCAmelCase				:


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					model.init(inputs_dict['''prng_key''']      ,					inputs_dict['''sample''']					)
            jax.lax.stop_gradient(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					model.apply(__SCREAMING_SNAKE_CASE      ,					inputs_dict['''sample''']					)

            if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					output.sample

            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					inputs_dict['''sample'''].shape
            self.assertEqual(output.shape      ,					__SCREAMING_SNAKE_CASE      ,					'''Input and output shapes do not match'''					)


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					16
            __snake_case         =					(16, 32)

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					model.init(inputs_dict['''prng_key''']      ,					inputs_dict['''sample''']					)
            jax.lax.stop_gradient(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					model.apply(__SCREAMING_SNAKE_CASE      ,					inputs_dict['''sample''']					)

            if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					output.sample

            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					inputs_dict['''sample'''].shape
            self.assertEqual(output.shape      ,					__SCREAMING_SNAKE_CASE      ,					'''Input and output shapes do not match'''					)




       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					'''kwargs''' in inspect.signature(self.model_class.__init__					).parameters
            __snake_case         =					len(self.model_class._deprecated_kwargs					) > 0

            if has_kwarg_in_model_class and not has_deprecated_kwarg:
                 raise ValueError(
                     F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
                     ''' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'''
                     ''' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='''
                     ''' [<deprecated_argument>]`'''					)

            if not has_kwarg_in_model_class and has_deprecated_kwarg:
                 raise ValueError(
                     F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
                     ''' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'''
                     F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
                     ''' from `_deprecated_kwargs = [<deprecated_argument>]`'''					)






