




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
}





class 		A__    (       __SCREAMING_SNAKE_CASE			):
	lowerCamelCase__		:			List[Any]          ="vilt"






	def __init__(     self    ,					lowerCamelCase=30522    ,					lowerCamelCase=2    ,					lowerCamelCase=2    ,					lowerCamelCase=40    ,					lowerCamelCase=768    ,					lowerCamelCase=12    ,					lowerCamelCase=12    ,					lowerCamelCase=3072    ,					lowerCamelCase="gelu"    ,					lowerCamelCase=0.0    ,					lowerCamelCase=0.0    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=1e-12    ,					lowerCamelCase=384    ,					lowerCamelCase=32    ,					lowerCamelCase=3    ,					lowerCamelCase=True    ,					lowerCamelCase=-1    ,					lowerCamelCase=False    ,					lowerCamelCase=-1    ,					**lowerCamelCase    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__(tie_word_embeddings=lowerCamelCase    ,					**lowerCamelCase						)

						__magic_name__ :							str		       =							vocab_size
						__magic_name__ :							Any		       =							type_vocab_size
						__magic_name__ :							Union[str, Any]		       =							modality_type_vocab_size
						__magic_name__ :							List[Any]		       =							max_position_embeddings

						__magic_name__ :							List[str]		       =							hidden_size
						__magic_name__ :							Union[str, Any]		       =							num_hidden_layers
						__magic_name__ :							Tuple		       =							num_attention_heads
						__magic_name__ :							Any		       =							intermediate_size
						__magic_name__ :							str		       =							hidden_act
						__magic_name__ :							str		       =							hidden_dropout_prob
						__magic_name__ :							Union[str, Any]		       =							attention_probs_dropout_prob
						__magic_name__ :							Tuple		       =							initializer_range
						__magic_name__ :							str		       =							layer_norm_eps

						__magic_name__ :							Dict		       =							image_size
						__magic_name__ :							str		       =							patch_size
						__magic_name__ :							Dict		       =							num_channels
						__magic_name__ :							Optional[int]		       =							qkv_bias
						__magic_name__ :							List[str]		       =							max_image_length
						__magic_name__ :							List[str]		       =							num_images









import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase = False,     UpperCAmelCase = 1,     UpperCAmelCase = 1,     UpperCAmelCase = 1_0000,     )  ->List[Any]:






					"""simple docstring"""






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

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

					__magic_name__ :							Tuple		       =							torch.exp(UpperCAmelCase	)
					__magic_name__ :							Tuple		       =							timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__magic_name__ :							Optional[Any]		       =							scale * emb

					# concat sine and cosine embeddings
					__magic_name__ :							Optional[int]		       =							torch.cat([torch.sin(UpperCAmelCase	), torch.cos(UpperCAmelCase	)],     dim=-1	)

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

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__ :							List[str]		       =							torch.nn.functional.pad(UpperCAmelCase,     (0, 1, 0, 0)	)
					return emb







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase=False,     UpperCAmelCase=0	)  ->Dict:






					"""simple docstring"""






					__magic_name__ :							Any		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.arange(UpperCAmelCase,     dtype=np.floataa	)
					__magic_name__ :							List[str]		       =							np.meshgrid(UpperCAmelCase,     UpperCAmelCase	)  # here w goes first
					__magic_name__ :							Tuple		       =							np.stack(UpperCAmelCase,     axis=0	)

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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[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
					__magic_name__ :							Union[str, Any]		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[0]	)  # (H*W, D/2)
					__magic_name__ :							Dict		       =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,     grid[1]	)  # (H*W, D/2)

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







def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase	)  ->List[Any]:






					"""simple docstring"""






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

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

					__magic_name__ :							List[str]		       =							pos.reshape(-1	)  # (M,)
					__magic_name__ :							Dict		       =							np.einsum('''m,d->md''',     UpperCAmelCase,     UpperCAmelCase	)  # (M, D/2), outer product

					__magic_name__ :							Union[str, Any]		       =							np.sin(UpperCAmelCase	)  # (M, D/2)
					__magic_name__ :							str		       =							np.cos(UpperCAmelCase	)  # (M, D/2)

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





class 		A__    (       nn.Module			):






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




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							(height // patch_size) * (width // patch_size)
						__magic_name__ :							Tuple		       =							flatten
						__magic_name__ :							List[Any]		       =							layer_norm

						__magic_name__ :							Optional[Any]		       =							nn.Convad(
						    lowerCamelCase    ,					lowerCamelCase    ,					kernel_size=(patch_size, patch_size)    ,					stride=lowerCamelCase    ,					bias=lowerCamelCase						)
						if layer_norm:
											__magic_name__ :							Optional[int]		       =							nn.LayerNorm(lowerCamelCase    ,					elementwise_affine=lowerCamelCase    ,					eps=1e-6						)
						else:
											__magic_name__ :							int		       =							None

						__magic_name__ :							List[Any]		       =							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 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.proj(lowerCamelCase						)
						if self.flatten:
											__magic_name__ :							Dict		       =							latent.flatten(2						).transpose(1    ,					2						)  # BCHW -> BNC
						if self.layer_norm:
											__magic_name__ :							Tuple		       =							self.norm(lowerCamelCase						)
						return latent + self.pos_embed





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = "silu"    ,					lowerCamelCase = None    ,					lowerCamelCase = None    ,					lowerCamelCase=None    ,					)				->       Tuple:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Union[str, Any]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if cond_proj_dim is not None:
											__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					bias=lowerCamelCase						)
						else:
											__magic_name__ :							Union[str, Any]		       =							None

						__magic_name__ :							Union[str, Any]		       =							get_activation(lowerCamelCase						)

						if out_dim is not None:
											__magic_name__ :							Tuple		       =							out_dim
						else:
											__magic_name__ :							List[str]		       =							time_embed_dim
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)

						if post_act_fn is None:
											__magic_name__ :							List[str]		       =							None
						else:
											__magic_name__ :							Dict		       =							get_activation(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if condition is not None:
											__magic_name__ :							List[Any]		       =							sample + self.cond_proj(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							self.linear_a(lowerCamelCase						)

						if self.act is not None:
											__magic_name__ :							str		       =							self.act(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							self.linear_a(lowerCamelCase						)

						if self.post_act is not None:
											__magic_name__ :							List[str]		       =							self.post_act(lowerCamelCase						)
						return sample





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							num_channels
						__magic_name__ :							Tuple		       =							flip_sin_to_cos
						__magic_name__ :							List[Any]		       =							downscale_freq_shift






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							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 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 256    ,					lowerCamelCase = 1.0    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=False						)				->       int:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)
						__magic_name__ :							List[str]		       =							log
						__magic_name__ :							Optional[int]		       =							flip_sin_to_cos

						if set_W_to_weight:
											# to delete later
											__magic_name__ :							Tuple		       =							nn.Parameter(torch.randn(lowerCamelCase						) * scale    ,					requires_grad=lowerCamelCase						)

											__magic_name__ :							List[str]		       =							self.W






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						if self.log:
											__magic_name__ :							Tuple		       =							torch.log(lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
											__magic_name__ :							Any		       =							torch.cat([torch.cos(lowerCamelCase						), torch.sin(lowerCamelCase						)]    ,					dim=-1						)
						else:
											__magic_name__ :							Dict		       =							torch.cat([torch.sin(lowerCamelCase						), torch.cos(lowerCamelCase						)]    ,					dim=-1						)
						return out





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					)				->       int:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Tuple		       =							height
						__magic_name__ :							Dict		       =							width
						__magic_name__ :							Any		       =							num_embed
						__magic_name__ :							List[str]		       =							embed_dim

						__magic_name__ :							Tuple		       =							nn.Embedding(self.num_embed    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.height    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Embedding(self.width    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							self.emb(lowerCamelCase						)

						__magic_name__ :							Dict		       =							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
						__magic_name__ :							Any		       =							height_emb.unsqueeze(2						)

						__magic_name__ :							Optional[int]		       =							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
						__magic_name__ :							str		       =							width_emb.unsqueeze(1						)

						__magic_name__ :							Dict		       =							height_emb + width_emb

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

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

						return emb





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							dropout_prob > 0
						__magic_name__ :							Optional[int]		       =							nn.Embedding(num_classes + use_cfg_embedding    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							num_classes
						__magic_name__ :							Union[str, Any]		       =							dropout_prob






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Tuple:




						"""simple docstring"""






						if force_drop_ids is None:
											__magic_name__ :							Dict		       =							torch.rand(labels.shape[0]    ,					device=labels.device						) < self.dropout_prob
						else:
											__magic_name__ :							List[str]		       =							torch.tensor(force_drop_ids == 1						)
						__magic_name__ :							Optional[int]		       =							torch.where(lowerCamelCase    ,					self.num_classes    ,					lowerCamelCase						)
						return labels






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
											__magic_name__ :							Optional[Any]		       =							self.token_drop(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							self.embedding_table(lowerCamelCase						)
						return embeddings





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 1024    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 10    ,					)				->       str:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							int		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[str]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							text_embeds.shape[0]

						# image
						__magic_name__ :							Optional[Any]		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_text_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)

						# text
						__magic_name__ :							Dict		       =							self.text_proj(lowerCamelCase						)

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





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 32    ,					)				->       Union[str, Any]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							List[Any]		       =							num_image_text_embeds
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							image_embeds.shape[0]

						# image
						__magic_name__ :							Any		       =							self.image_embeds(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							image_embeds.reshape(lowerCamelCase    ,					self.num_image_text_embeds    ,					-1						)
						__magic_name__ :							Union[str, Any]		       =							self.norm(lowerCamelCase						)
						return image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=0.1						)				->       List[str]:




						"""simple docstring"""






						super().__init__()

						__magic_name__ :							Dict		       =							Timesteps(num_channels=256    ,					flip_sin_to_cos=lowerCamelCase    ,					downscale_freq_shift=1						)
						__magic_name__ :							int		       =							TimestepEmbedding(in_channels=256    ,					time_embed_dim=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							LabelEmbedding(lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							str		       =							self.time_proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase						)						)  # (N, D)

						__magic_name__ :							Optional[Any]		       =							self.class_embedder(lowerCamelCase						)  # (N, D)

						__magic_name__ :							Dict		       =							timesteps_emb + class_labels  # (N, D)

						return conditioning





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase = 64						)				->       str:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							List[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							AttentionPooling(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Dict		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							self.norma(lowerCamelCase						)
						__magic_name__ :							Tuple		       =							self.pool(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.proj(lowerCamelCase						)
						__magic_name__ :							Dict		       =							self.norma(lowerCamelCase						)
						return hidden_states





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Tuple:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Any		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							int		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.text_proj(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.text_norm(lowerCamelCase						)

						# image
						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)

						return time_image_embeds + time_text_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       Optional[int]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							int		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							str		       =							nn.LayerNorm(lowerCamelCase						)






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							int		       =							self.image_norm(lowerCamelCase						)
						return time_image_embeds





class 		A__    (       nn.Module			):






	def __init__(     self    ,					lowerCamelCase = 768    ,					lowerCamelCase = 1536						)				->       List[str]:




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							Optional[int]		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase						)
						__magic_name__ :							Optional[Any]		       =							nn.LayerNorm(lowerCamelCase						)
						__magic_name__ :							Any		       =							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 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.image_proj(lowerCamelCase						)
						__magic_name__ :							str		       =							self.image_norm(lowerCamelCase						)
						__magic_name__ :							Union[str, Any]		       =							self.input_hint_block(lowerCamelCase						)
						return time_image_embeds, hint





class 		A__    (       nn.Module			):






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




						"""simple docstring"""






						super().__init__()
						__magic_name__ :							str		       =							dtype
						__magic_name__ :							str		       =							nn.Parameter(torch.randn(1    ,					lowerCamelCase						) / embed_dim**0.5						)
						__magic_name__ :							Tuple		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							str		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							nn.Linear(lowerCamelCase    ,					lowerCamelCase    ,					dtype=self.dtype						)
						__magic_name__ :							Dict		       =							num_heads
						__magic_name__ :							List[Any]		       =							embed_dim // self.num_heads






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__       ,			__magic_name__       ,			__magic_name__ :							str		       =							x.size()

						def shape(lowerCamelCase						):
											# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
											__magic_name__ :							Union[str, Any]		       =							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)
											__magic_name__ :							str		       =							x.transpose(1    ,					2						)
											# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
											__magic_name__ :							List[str]		       =							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)
											__magic_name__ :							int		       =							x.transpose(1    ,					2						)
											return x

						__magic_name__ :							List[Any]		       =							x.mean(dim=1    ,					keepdim=lowerCamelCase						) + self.positional_embedding.to(x.dtype						)
						__magic_name__ :							int		       =							torch.cat([class_token, x]    ,					dim=1						)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						__magic_name__ :							int		       =							shape(self.q_proj(lowerCamelCase						)						)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						__magic_name__ :							str		       =							shape(self.k_proj(lowerCamelCase						)						)
						__magic_name__ :							Any		       =							shape(self.v_proj(lowerCamelCase						)						)

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

						# (bs*n_heads, dim_per_head, class_token_length)
						__magic_name__ :							Dict		       =							torch.einsum('''bts,bcs->bct'''    ,					lowerCamelCase    ,					lowerCamelCase						)

						# (bs, length+1, width)
						__magic_name__ :							Optional[int]		       =							a.reshape(lowerCamelCase    ,					-1    ,					1						).transpose(1    ,					2						)

						return a[:, 0, :]  # cls_token



