




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,	)  ->Any:






					"""simple docstring"""






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

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

					__magic_name__					:							Any		   =							torch.exp(a__       )
					__magic_name__					:							Optional[Any]		   =							timesteps[:, None].float() * emb[None, :]

					# scale embeddings
					__magic_name__					:							Union[str, Any]		   =							scale * emb

					# concat sine and cosine embeddings
					__magic_name__					:							Union[str, Any]		   =							torch.cat([torch.sin(a__       ), torch.cos(a__       )],	dim=-1       )

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

					# zero pad
					if embedding_dim % 2 == 1:
										__magic_name__					:							Optional[Any]		   =							torch.nn.functional.pad(a__,	(0, 1, 0, 0)       )
					return emb







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






					"""simple docstring"""






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

					__magic_name__					:							int		   =							grid.reshape([2, 1, grid_size, grid_size]       )
					__magic_name__					:							Union[str, Any]		   =							get_ad_sincos_pos_embed_from_grid(a__,	a__       )
					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__					:							Any		   =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,	grid[0]       )  # (H*W, D/2)
					__magic_name__					:							Any		   =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2,	grid[1]       )  # (H*W, D/2)

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






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

					__magic_name__					:							Optional[Any]		   =							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__					:							Tuple		   =							pos.reshape(-1       )  # (M,)
					__magic_name__					:							int		   =							np.einsum('''m,d->md''',	a__,	a__       )  # (M, D/2), outer product

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

					__magic_name__					:							Any		   =							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				,     )      ->   int:




						"""simple docstring"""






						super().__init__()

						__magic_name__					:							int		   =							(height // patch_size) * (width // patch_size)
						__magic_name__					:							List[str]		   =							flatten
						__magic_name__					:							int		   =							layer_norm

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

						__magic_name__					:							Dict		   =							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						)      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							self.proj(lowerCamelCase__						)
						if self.flatten:
											__magic_name__					:							int		   =							latent.flatten(2						).transpose(1				,     2						)  # BCHW -> BNC
						if self.layer_norm:
											__magic_name__					:							List[str]		   =							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				,     )      ->   List[str]:




						"""simple docstring"""






						super().__init__()

						__magic_name__					:							Dict		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__						)

						if cond_proj_dim is not None:
											__magic_name__					:							List[str]		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__				,     bias=lowerCamelCase__						)
						else:
											__magic_name__					:							str		   =							None

						__magic_name__					:							str		   =							get_activation(lowerCamelCase__						)

						if out_dim is not None:
											__magic_name__					:							List[str]		   =							out_dim
						else:
											__magic_name__					:							Optional[int]		   =							time_embed_dim
						__magic_name__					:							Optional[Any]		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__						)

						if post_act_fn is None:
											__magic_name__					:							Optional[Any]		   =							None
						else:
											__magic_name__					:							Union[str, Any]		   =							get_activation(lowerCamelCase__						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   List[Any]:




						"""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__					:							int		   =							self.act(lowerCamelCase__						)

						__magic_name__					:							Tuple		   =							self.linear_a(lowerCamelCase__						)

						if self.post_act is not None:
											__magic_name__					:							Optional[Any]		   =							self.post_act(lowerCamelCase__						)
						return sample





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






						super().__init__()
						__magic_name__					:							int		   =							num_channels
						__magic_name__					:							str		   =							flip_sin_to_cos
						__magic_name__					:							int		   =							downscale_freq_shift






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							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						)      ->   Tuple:




						"""simple docstring"""






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

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

											__magic_name__					:							str		   =							self.W






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						if self.log:
											__magic_name__					:							Optional[Any]		   =							torch.log(lowerCamelCase__						)

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

						if self.flip_sin_to_cos:
											__magic_name__					:							int		   =							torch.cat([torch.cos(lowerCamelCase__						), torch.sin(lowerCamelCase__						)]				,     dim=-1						)
						else:
											__magic_name__					:							Optional[int]		   =							torch.cat([torch.sin(lowerCamelCase__						), torch.cos(lowerCamelCase__						)]				,     dim=-1						)
						return out





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






						super().__init__()

						__magic_name__					:							Union[str, Any]		   =							height
						__magic_name__					:							Tuple		   =							width
						__magic_name__					:							Optional[Any]		   =							num_embed
						__magic_name__					:							Optional[Any]		   =							embed_dim

						__magic_name__					:							Optional[int]		   =							nn.Embedding(self.num_embed				,     lowerCamelCase__						)
						__magic_name__					:							Any		   =							nn.Embedding(self.height				,     lowerCamelCase__						)
						__magic_name__					:							Union[str, Any]		   =							nn.Embedding(self.width				,     lowerCamelCase__						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							str		   =							self.emb(lowerCamelCase__						)

						__magic_name__					:							Optional[int]		   =							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__					:							Optional[int]		   =							height_emb.unsqueeze(2						)

						__magic_name__					:							Optional[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
						__magic_name__					:							Union[str, Any]		   =							width_emb.unsqueeze(1						)

						__magic_name__					:							str		   =							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__					:							List[str]		   =							emb + pos_emb[:, : emb.shape[1], :]

						return emb





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






						super().__init__()
						__magic_name__					:							Dict		   =							dropout_prob > 0
						__magic_name__					:							Tuple		   =							nn.Embedding(num_classes + use_cfg_embedding				,     lowerCamelCase__						)
						__magic_name__					:							Any		   =							num_classes
						__magic_name__					:							Any		   =							dropout_prob






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   Union[str, Any]:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   Optional[int]:




						"""simple docstring"""






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





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






						super().__init__()

						__magic_name__					:							Optional[Any]		   =							num_image_text_embeds
						__magic_name__					:							Optional[Any]		   =							nn.Linear(lowerCamelCase__				,     self.num_image_text_embeds * cross_attention_dim						)
						__magic_name__					:							str		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__						)






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




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							text_embeds.shape[0]

						# image
						__magic_name__					:							Union[str, Any]		   =							self.image_embeds(lowerCamelCase__						)
						__magic_name__					:							Optional[int]		   =							image_text_embeds.reshape(lowerCamelCase__				,     self.num_image_text_embeds				,     -1						)

						# text
						__magic_name__					:							Tuple		   =							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				,     )      ->   Tuple:




						"""simple docstring"""






						super().__init__()

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






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




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							image_embeds.shape[0]

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





class 		A__				(   nn.Module ):






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase=0.1						)      ->   Any:




						"""simple docstring"""






						super().__init__()

						__magic_name__					:							str		   =							Timesteps(num_channels=256				,     flip_sin_to_cos=lowerCamelCase__				,     downscale_freq_shift=1						)
						__magic_name__					:							List[Any]		   =							TimestepEmbedding(in_channels=256				,     time_embed_dim=lowerCamelCase__						)
						__magic_name__					:							List[Any]		   =							LabelEmbedding(lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__						)






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   Union[str, Any]:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							self.time_proj(lowerCamelCase__						)
						__magic_name__					:							str		   =							self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase__						)						)  # (N, D)

						__magic_name__					:							Union[str, Any]		   =							self.class_embedder(lowerCamelCase__						)  # (N, D)

						__magic_name__					:							Optional[Any]		   =							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__					:							Dict		   =							nn.LayerNorm(lowerCamelCase__						)
						__magic_name__					:							Optional[int]		   =							AttentionPooling(lowerCamelCase__				,     lowerCamelCase__						)
						__magic_name__					:							Optional[int]		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__						)
						__magic_name__					:							str		   =							nn.LayerNorm(lowerCamelCase__						)






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




						"""simple docstring"""






						__magic_name__					:							str		   =							self.norma(lowerCamelCase__						)
						__magic_name__					:							Optional[Any]		   =							self.pool(lowerCamelCase__						)
						__magic_name__					:							Optional[int]		   =							self.proj(lowerCamelCase__						)
						__magic_name__					:							int		   =							self.norma(lowerCamelCase__						)
						return hidden_states





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






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






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




						"""simple docstring"""






						__magic_name__					:							Tuple		   =							self.text_proj(lowerCamelCase__						)
						__magic_name__					:							List[Any]		   =							self.text_norm(lowerCamelCase__						)

						# image
						__magic_name__					:							List[str]		   =							self.image_proj(lowerCamelCase__						)

						return time_image_embeds + time_text_embeds





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							self.image_proj(lowerCamelCase__						)
						__magic_name__					:							Tuple		   =							self.image_norm(lowerCamelCase__						)
						return time_image_embeds





class 		A__				(   nn.Module ):






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




						"""simple docstring"""






						super().__init__()
						__magic_name__					:							str		   =							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						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							self.image_proj(lowerCamelCase__						)
						__magic_name__					:							Optional[int]		   =							self.image_norm(lowerCamelCase__						)
						__magic_name__					:							Tuple		   =							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__					:							List[str]		   =							dtype
						__magic_name__					:							Any		   =							nn.Parameter(torch.randn(1				,     lowerCamelCase__						) / embed_dim**0.5						)
						__magic_name__					:							List[Any]		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__				,     dtype=self.dtype						)
						__magic_name__					:							str		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__				,     dtype=self.dtype						)
						__magic_name__					:							Any		   =							nn.Linear(lowerCamelCase__				,     lowerCamelCase__				,     dtype=self.dtype						)
						__magic_name__					:							int		   =							num_heads
						__magic_name__					:							Optional[Any]		   =							embed_dim // self.num_heads






	def  lowercase      (	self				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						__magic_name__   , __magic_name__   , __magic_name__					:							Union[str, Any]		   =							x.size()

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

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

						# (bs*n_heads, class_token_length, dim_per_head)
						__magic_name__					:							str		   =							shape(self.q_proj(lowerCamelCase__						)						)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						__magic_name__					:							Optional[int]		   =							shape(self.k_proj(lowerCamelCase__						)						)
						__magic_name__					:							List[str]		   =							shape(self.v_proj(lowerCamelCase__						)						)

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

						# (bs*n_heads, dim_per_head, class_token_length)
						__magic_name__					:							Tuple		   =							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









'''simple docstring'''
import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


A							:      Union[str, Any] 	=       logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		eos_token_id=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		max_new_tokens=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						text_encoding_offset
           __lowercase							=						text_pad_token
           __lowercase							=						eos_token_id
           __lowercase							=						semantic_infer_token
           __lowercase							=						semantic_vocab_size
           __lowercase							=						max_input_semantic_length
           __lowercase							=						semantic_rate_hz






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



           super().__init__(
               temperature=lowerCamelCase__					,		do_sample=lowerCamelCase__					,		renormalize_logits=lowerCamelCase__					,		output_scores=lowerCamelCase__					,		return_dict_in_generate=lowerCamelCase__					,		output_hidden_states=lowerCamelCase__					,		output_attentions=lowerCamelCase__					,		**lowerCamelCase__					,		)

           __lowercase							=						coarse_semantic_pad_token
           __lowercase							=						coarse_rate_hz
           __lowercase							=						n_coarse_codebooks
           __lowercase							=						coarse_infer_token
           __lowercase							=						max_coarse_input_length
           __lowercase							=						max_coarse_history
           __lowercase							=						sliding_window_len






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     Dict    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """simple docstring"""



           if semantic_config is None:
               __lowercase							=						{}
               logger.info("""semantic_config is None. initializing the semantic model with default values."""     )

           if coarse_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""     )

           if fine_acoustics_config is None:
               __lowercase							=						{}
               logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""     )

           __lowercase							=						BarkSemanticGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineGenerationConfig(**lowerCamelCase__     )

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """simple docstring"""



           return cls(
               semantic_config=semantic_config.to_dict()					,		coarse_acoustics_config=coarse_acoustics_config.to_dict()					,		fine_acoustics_config=fine_acoustics_config.to_dict()					,		**lowerCamelCase__					,		)




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

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )

           __lowercase							=						self.semantic_config.to_dict()
           __lowercase							=						self.coarse_acoustics_config.to_dict()
           __lowercase							=						self.fine_acoustics_config.to_dict()

           __lowercase							=						self.__class__.model_type
           return output




