
"""simple docstring"""




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def 		A_	(							snake_case							:				torch.Tensor	, snake_case							:				int	, snake_case							:				bool = False	, snake_case							:				float = 1	, snake_case							:				float = 1	, snake_case							:				int = 10000	, ) ->      List[Any]:



						'''simple docstring'''

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

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

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

						# scale embeddings
						__UpperCamelCase       					=     scale * emb

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

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

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




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



						'''simple docstring'''

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

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




def 		A_	(							snake_case							:				Tuple	, snake_case							:				int	) ->      Optional[Any]:



						'''simple docstring'''

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

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

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




def 		A_	(							snake_case							:				Dict	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

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

						__UpperCamelCase       					=     np.arange(embed_dim // 2	, dtype=np.floataa	)
						omega /= embed_dim / 2.0
						__UpperCamelCase       					=     1.0 / 10000**omega  # (D/2,)

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

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

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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()

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

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

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()

													__UpperCamelCase       					=     nn.Linear(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE     )

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

													__UpperCamelCase       					=     get_activation(__SCREAMING_SNAKE_CASE     )

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

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				List[str]:





													'''simple docstring'''






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

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

													__UpperCamelCase       					=     self.linear_a(__SCREAMING_SNAKE_CASE     )

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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     get_timestep_embedding(
													    __SCREAMING_SNAKE_CASE       ,		self.num_channels       ,		flip_sin_to_cos=self.flip_sin_to_cos       ,		downscale_freq_shift=self.downscale_freq_shift       ,		)
													return t_emb






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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

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

																			__UpperCamelCase       					=     self.W



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Dict:





													'''simple docstring'''






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

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

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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()

													__UpperCamelCase       					=     height
													__UpperCamelCase       					=     width
													__UpperCamelCase       					=     num_embed
													__UpperCamelCase       					=     embed_dim

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.emb(__SCREAMING_SNAKE_CASE     )

													__UpperCamelCase       					=     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
													__UpperCamelCase       					=     height_emb.unsqueeze(2     )

													__UpperCamelCase       					=     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
													__UpperCamelCase       					=     width_emb.unsqueeze(1     )

													__UpperCamelCase       					=     height_emb + width_emb

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

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

													return emb






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				Optional[Any]:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				Dict:





													'''simple docstring'''






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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()

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



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





													'''simple docstring'''






													__UpperCamelCase       					=     text_embeds.shape[0]

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

													# text
													__UpperCamelCase       					=     self.text_proj(__SCREAMING_SNAKE_CASE     )

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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()

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



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





													'''simple docstring'''






													__UpperCamelCase       					=     image_embeds.shape[0]

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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				Any:





													'''simple docstring'''






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

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

													__UpperCamelCase       					=     timesteps_emb + class_labels  # (N, D)

													return conditioning






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Any:





													'''simple docstring'''






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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






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

													# image
													__UpperCamelCase       					=     self.image_proj(__SCREAMING_SNAKE_CASE     )

													return time_image_embeds + time_text_embeds






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






													super().__init__()
													__UpperCamelCase       					=     nn.Linear(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE     )
													__UpperCamelCase       					=     nn.LayerNorm(__SCREAMING_SNAKE_CASE     )
													__UpperCamelCase       					=     nn.Sequential(
													    nn.Convad(3       ,		16       ,		3       ,		padding=1     )       ,		nn.SiLU()       ,		nn.Convad(16       ,		16       ,		3       ,		padding=1     )       ,		nn.SiLU()       ,		nn.Convad(16       ,		32       ,		3       ,		padding=1       ,		stride=2     )       ,		nn.SiLU()       ,		nn.Convad(32       ,		32       ,		3       ,		padding=1     )       ,		nn.SiLU()       ,		nn.Convad(32       ,		96       ,		3       ,		padding=1       ,		stride=2     )       ,		nn.SiLU()       ,		nn.Convad(96       ,		96       ,		3       ,		padding=1     )       ,		nn.SiLU()       ,		nn.Convad(96       ,		256       ,		3       ,		padding=1       ,		stride=2     )       ,		nn.SiLU()       ,		nn.Convad(256       ,		4       ,		3       ,		padding=1     )       ,		)



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






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






class     SCREAMING_SNAKE_CASE__      (							nn.Module      ):

							"""simple docstring"""






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





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase       					=     x.size()

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

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

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

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

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

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

													return a[:, 0, :]  # cls_token






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


lowercase__							:							List[Any]													=     get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tempfile.mkdtemp()

													__UpperCamelCase       					=     SpeechTaTokenizer(SCREAMING_SNAKE_CASE_     )
													tokenizer.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     {
													    '''feature_size''': 1,
													    '''padding_value''': 0.0,
													    '''sampling_rate''': 16000,
													    '''do_normalize''': False,
													    '''num_mel_bins''': 80,
													    '''hop_length''': 16,
													    '''win_length''': 64,
													    '''win_function''': '''hann_window''',
													    '''fmin''': 80,
													    '''fmax''': 7600,
													    '''mel_floor''': 1E-10,
													    '''reduction_factor''': 2,
													    '''return_attention_mask''': True,
													}

													__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		SCREAMING_SNAKE_CASE_     )
													with open(self.feature_extraction_file       ,		'''w'''       ,		encoding='''utf-8'''     ) as fp:
																			fp.write(json.dumps(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				str:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_tokenizer()
													__UpperCamelCase       					=     self.get_feature_extractor()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													processor.save_pretrained(self.tmpdirname     )
													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(self.tmpdirname     )

													self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer.get_vocab()     )
													self.assertIsInstance(processor.tokenizer       ,		SCREAMING_SNAKE_CASE_     )

													self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor.to_json_string()     )
													self.assertIsInstance(processor.feature_extractor       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()     )
													processor.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     self.get_tokenizer(bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''     )
													__UpperCamelCase       					=     self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(
													    self.tmpdirname       ,		bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''       ,		do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer_add_kwargs.get_vocab()     )
													self.assertIsInstance(processor.tokenizer       ,		SCREAMING_SNAKE_CASE_     )

													self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor_add_kwargs.to_json_string()     )
													self.assertIsInstance(processor.feature_extractor       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text_target=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__UpperCamelCase       					=     processor.batch_decode(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(
													    processor.model_input_names       ,		feature_extractor.model_input_names       ,		msg='''`processor` and `feature_extractor` model input names do not match'''       ,		)




