






import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation



def        _lowerCamelCase(      lowerCAmelCase__					:		torch.Tensor     ,     lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		bool = False     ,     lowerCAmelCase__					:		float = 1     ,     lowerCAmelCase__					:		float = 1     ,     lowerCAmelCase__					:		int = 1_0000     ,     ):





				'''simple docstring'''







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

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

				SCREAMING_SNAKE_CASE_ :						Tuple    				= torch.exp(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						str    				= timesteps[:, None].float() * emb[None, :]

				# scale embeddings
				SCREAMING_SNAKE_CASE_ :						Dict    				= scale * emb

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

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

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



def        _lowerCamelCase(      lowerCAmelCase__					:		Tuple     ,     lowerCAmelCase__					:		Dict     ,     lowerCAmelCase__					:		Optional[int]=False     ,     lowerCAmelCase__					:		List[str]=0   ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= grid.reshape([2, 1, grid_size, grid_size]   )
				SCREAMING_SNAKE_CASE_ :						Dict    				= get_ad_sincos_pos_embed_from_grid(lowerCAmelCase__     ,     lowerCAmelCase__   )
				if cls_token and extra_tokens > 0:
								SCREAMING_SNAKE_CASE_ :						int    				= np.concatenate([np.zeros([extra_tokens, embed_dim]   ), pos_embed]     ,     axis=0   )
				return pos_embed



def        _lowerCamelCase(      lowerCAmelCase__					:		Dict     ,     lowerCAmelCase__					:		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
				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,     grid[0]   )  # (H*W, D/2)
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,     grid[1]   )  # (H*W, D/2)

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



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]     ,     lowerCAmelCase__					:		Union[str, Any]   ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_ :						List[str]    				= np.arange(embed_dim // 2     ,     dtype=np.floataa   )
				omega /= embed_dim / 2.0
				SCREAMING_SNAKE_CASE_ :						Dict    				= 1.0 / 1_0000**omega  # (D/2,)

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

				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.sin(lowerCAmelCase__   )  # (M, D/2)
				SCREAMING_SNAKE_CASE_ :						Dict    				= np.cos(lowerCAmelCase__   )  # (M, D/2)

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


class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__=224			,     UpperCamelCase__=224			,     UpperCamelCase__=16			,     UpperCamelCase__=3			,     UpperCamelCase__=768			,     UpperCamelCase__=False			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     ):
						super().__init__()

						SCREAMING_SNAKE_CASE_ :						Dict    				= (height // patch_size) * (width // patch_size)
						SCREAMING_SNAKE_CASE_ :						str    				= flatten
						SCREAMING_SNAKE_CASE_ :						Any    				= layer_norm

						SCREAMING_SNAKE_CASE_ :						Tuple    				= nn.Convad(
						    UpperCamelCase__			,     UpperCamelCase__			,     kernel_size=(patch_size, patch_size)			,     stride=UpperCamelCase__			,     bias=UpperCamelCase__							)
						if layer_norm:
										SCREAMING_SNAKE_CASE_ :						int    				= nn.LayerNorm(UpperCamelCase__			,     elementwise_affine=UpperCamelCase__			,     eps=1E-6							)
						else:
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= None

						SCREAMING_SNAKE_CASE_ :						Dict    				= get_ad_sincos_pos_embed(UpperCamelCase__			,     int(num_patches**0.5							)							)
						self.register_buffer('pos_embed'			,     torch.from_numpy(UpperCamelCase__							).float().unsqueeze(0							)			,     persistent=UpperCamelCase__							)






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






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__ = "silu"			,     UpperCamelCase__ = None			,     UpperCamelCase__ = None			,     UpperCamelCase__=None			,     ):
						super().__init__()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)

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

						SCREAMING_SNAKE_CASE_ :						str    				= get_activation(UpperCamelCase__							)

						if out_dim is not None:
										SCREAMING_SNAKE_CASE_ :						str    				= out_dim
						else:
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= time_embed_dim
						SCREAMING_SNAKE_CASE_ :						Any    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)

						if post_act_fn is None:
										SCREAMING_SNAKE_CASE_ :						Dict    				= None
						else:
										SCREAMING_SNAKE_CASE_ :						Any    				= get_activation(UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						if condition is not None:
										SCREAMING_SNAKE_CASE_ :						Dict    				= sample + self.cond_proj(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.linear_a(UpperCamelCase__							)

						if self.act is not None:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= self.act(UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.linear_a(UpperCamelCase__							)

						if self.post_act is not None:
										SCREAMING_SNAKE_CASE_ :						str    				= self.post_act(UpperCamelCase__							)
						return sample






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= num_channels
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= flip_sin_to_cos
						SCREAMING_SNAKE_CASE_ :						str    				= downscale_freq_shift






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= get_timestep_embedding(
						    UpperCamelCase__			,     self.num_channels			,     flip_sin_to_cos=self.flip_sin_to_cos			,     downscale_freq_shift=self.downscale_freq_shift			,     )
						return t_emb






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__ = 256			,     UpperCamelCase__ = 1.0			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__=False							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						List[str]    				= nn.Parameter(torch.randn(UpperCamelCase__							) * scale			,     requires_grad=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= log
						SCREAMING_SNAKE_CASE_ :						Tuple    				= flip_sin_to_cos

						if set_W_to_weight:
										# to delete later
										SCREAMING_SNAKE_CASE_ :						int    				= nn.Parameter(torch.randn(UpperCamelCase__							) * scale			,     requires_grad=UpperCamelCase__							)

										SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.W






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						if self.log:
										SCREAMING_SNAKE_CASE_ :						Any    				= torch.log(UpperCamelCase__							)

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

						if self.flip_sin_to_cos:
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= torch.cat([torch.cos(UpperCamelCase__							), torch.sin(UpperCamelCase__							)]			,     dim=-1							)
						else:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= torch.cat([torch.sin(UpperCamelCase__							), torch.cos(UpperCamelCase__							)]			,     dim=-1							)
						return out






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     ):
						super().__init__()

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= height
						SCREAMING_SNAKE_CASE_ :						int    				= width
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= num_embed
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= embed_dim

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= nn.Embedding(self.num_embed			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= nn.Embedding(self.height			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= nn.Embedding(self.width			,     UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.emb(UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= 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_ :						int    				= height_emb.unsqueeze(2							)

						SCREAMING_SNAKE_CASE_ :						Any    				= self.width_emb(torch.arange(self.width			,     device=index.device							).view(1			,     self.width							)							)

						# 1 x W x D -> 1 x 1 x W x D
						SCREAMING_SNAKE_CASE_ :						str    				= width_emb.unsqueeze(1							)

						SCREAMING_SNAKE_CASE_ :						Any    				= height_emb + width_emb

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

						SCREAMING_SNAKE_CASE_ :						str    				= emb + pos_emb[:, : emb.shape[1], :]

						return emb






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= dropout_prob > 0
						SCREAMING_SNAKE_CASE_ :						Dict    				= nn.Embedding(num_classes + use_cfg_embedding			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= num_classes
						SCREAMING_SNAKE_CASE_ :						str    				= dropout_prob


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						if force_drop_ids is None:
										SCREAMING_SNAKE_CASE_ :						Dict    				= torch.rand(labels.shape[0]			,     device=labels.device							) < self.dropout_prob
						else:
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= torch.tensor(force_drop_ids == 1							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= torch.where(UpperCamelCase__			,     self.num_classes			,     UpperCamelCase__							)
						return labels






		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						SCREAMING_SNAKE_CASE_ :						str    				= self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
										SCREAMING_SNAKE_CASE_ :						Tuple    				= self.token_drop(UpperCamelCase__			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= self.embedding_table(UpperCamelCase__							)
						return embeddings






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__ = 1024			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 10			,     ):
						super().__init__()

						SCREAMING_SNAKE_CASE_ :						str    				= num_image_text_embeds
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= nn.Linear(UpperCamelCase__			,     self.num_image_text_embeds * cross_attention_dim							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Any    				= text_embeds.shape[0]

						# image
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.image_embeds(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						str    				= image_text_embeds.reshape(UpperCamelCase__			,     self.num_image_text_embeds			,     -1							)

						# text
						SCREAMING_SNAKE_CASE_ :						int    				= self.text_proj(UpperCamelCase__							)

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






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 32			,     ):
						super().__init__()

						SCREAMING_SNAKE_CASE_ :						str    				= num_image_text_embeds
						SCREAMING_SNAKE_CASE_ :						Tuple    				= nn.Linear(UpperCamelCase__			,     self.num_image_text_embeds * cross_attention_dim							)
						SCREAMING_SNAKE_CASE_ :						str    				= nn.LayerNorm(UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= image_embeds.shape[0]

						# image
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.image_embeds(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= image_embeds.reshape(UpperCamelCase__			,     self.num_image_text_embeds			,     -1							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.norm(UpperCamelCase__							)
						return image_embeds






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__=0.1							):
						super().__init__()

						SCREAMING_SNAKE_CASE_ :						Any    				= Timesteps(num_channels=256			,     flip_sin_to_cos=UpperCamelCase__			,     downscale_freq_shift=1							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= TimestepEmbedding(in_channels=256			,     time_embed_dim=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= LabelEmbedding(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.time_proj(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.timestep_embedder(timesteps_proj.to(dtype=UpperCamelCase__							)							)  # (N, D)

						SCREAMING_SNAKE_CASE_ :						Any    				= self.class_embedder(UpperCamelCase__							)  # (N, D)

						SCREAMING_SNAKE_CASE_ :						str    				= timesteps_emb + class_labels  # (N, D)

						return conditioning






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__ = 64							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						Any    				= nn.LayerNorm(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= AttentionPooling(UpperCamelCase__			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= nn.LayerNorm(UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						str    				= self.norma(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.pool(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.proj(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.norma(UpperCamelCase__							)
						return hidden_states






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 1536							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						Dict    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= nn.LayerNorm(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__							):
						# text
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.text_proj(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.text_norm(UpperCamelCase__							)

						# image
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.image_proj(UpperCamelCase__							)

						return time_image_embeds + time_text_embeds






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 1536							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= nn.LayerNorm(UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						# image
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.image_proj(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.image_norm(UpperCamelCase__							)
						return time_image_embeds






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__ = 768			,     UpperCamelCase__ = 1536							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= nn.LayerNorm(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						str    				= nn.Sequential(
						    nn.Convad(3			,     16			,     3			,     padding=1							)			,     nn.SiLU()			,     nn.Convad(16			,     16			,     3			,     padding=1							)			,     nn.SiLU()			,     nn.Convad(16			,     32			,     3			,     padding=1			,     stride=2							)			,     nn.SiLU()			,     nn.Convad(32			,     32			,     3			,     padding=1							)			,     nn.SiLU()			,     nn.Convad(32			,     96			,     3			,     padding=1			,     stride=2							)			,     nn.SiLU()			,     nn.Convad(96			,     96			,     3			,     padding=1							)			,     nn.SiLU()			,     nn.Convad(96			,     256			,     3			,     padding=1			,     stride=2							)			,     nn.SiLU()			,     nn.Convad(256			,     4			,     3			,     padding=1							)			,     )






		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__							):
						# image
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.image_proj(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.image_norm(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= self.input_hint_block(UpperCamelCase__							)
						return time_image_embeds, hint






class 				__a				(	nn.Module    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						super().__init__()
						SCREAMING_SNAKE_CASE_ :						List[str]    				= dtype
						SCREAMING_SNAKE_CASE_ :						int    				= nn.Parameter(torch.randn(1			,     UpperCamelCase__							) / embed_dim**0.5							)
						SCREAMING_SNAKE_CASE_ :						int    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__			,     dtype=self.dtype							)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__			,     dtype=self.dtype							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= nn.Linear(UpperCamelCase__			,     UpperCamelCase__			,     dtype=self.dtype							)
						SCREAMING_SNAKE_CASE_ :						str    				= num_heads
						SCREAMING_SNAKE_CASE_ :						Dict    				= embed_dim // self.num_heads






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						int    				= x.size()

						def shape(UpperCamelCase__							):
										# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
										SCREAMING_SNAKE_CASE_ :						Dict    				= x.view(UpperCamelCase__			,     -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_ :						Dict    				= x.transpose(1			,     2							)
										# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
										SCREAMING_SNAKE_CASE_ :						Dict    				= 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_ :						Dict    				= x.transpose(1			,     2							)
										return x

						SCREAMING_SNAKE_CASE_ :						Dict    				= x.mean(dim=1			,     keepdim=UpperCamelCase__							) + self.positional_embedding.to(x.dtype							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= torch.cat([class_token, x]			,     dim=1							)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= shape(self.q_proj(UpperCamelCase__							)							)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						SCREAMING_SNAKE_CASE_ :						Dict    				= shape(self.k_proj(UpperCamelCase__							)							)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= shape(self.v_proj(UpperCamelCase__							)							)

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

						# (bs*n_heads, dim_per_head, class_token_length)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= torch.einsum('bts,bcs->bct'			,     UpperCamelCase__			,     UpperCamelCase__							)

						# (bs, length+1, width)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= a.reshape(UpperCamelCase__			,     -1			,     1							).transpose(1			,     2							)

						return a[:, 0, :]  # cls_token







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				__a				(	__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    HerbertTokenizer
		UpperCAmelCase__   :					List[Any]					     =    HerbertTokenizerFast
		UpperCAmelCase__   :					Optional[Any]					     =    True


		def 	__snake_case      (    self							):
						super().setUp()

						# Use a simpler test file without japanese/chinese characters
						with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''			,     encoding='utf-8'							) as f_data:
										SCREAMING_SNAKE_CASE_ :						Dict    				= f_data.read().replace('\n\n'			,     '\n'							).strip()

						SCREAMING_SNAKE_CASE_ :						str    				= [
						    '<s>',
						    '</s>',
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    ',</w>',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE_ :						str    				= dict(zip(UpperCamelCase__			,     range(len(UpperCamelCase__							)							)							)							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']							)
						with open(self.vocab_file			,     'w'							) as fp:
										fp.write(json.dumps(UpperCamelCase__							)							)
						with open(self.merges_file			,     'w'							) as fp:
										fp.write('\n'.join(UpperCamelCase__							)							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 'lower newer'
						SCREAMING_SNAKE_CASE_ :						int    				= 'lower newer'
						return input_text, output_text


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.tokenizer_class(vocab_file=self.vocab_file			,     merges_file=self.merges_file							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 'lower'
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ['low', 'er</w>']
						SCREAMING_SNAKE_CASE_ :						int    				= tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokens + ['<unk>']
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= [16, 17, 23]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						if not self.test_rust_tokenizer:
										return

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= 'lower,newer'

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.tokenize(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= rust_tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= rust_tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.encode(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= rust_tokenizer.encode(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'							)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= tokenizer.encode('konstruowanie sekwencji'			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.encode('konstruowanie wielu sekwencji'			,     add_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Any    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,     UpperCamelCase__							)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + text_a + [2]