
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			,   )   ->     int:



						'''simple docstring'''

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

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

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

						# scale embeddings
						__UpperCamelCase  	=      scale * emb

						# concat sine and cosine embeddings
						__UpperCamelCase  	=      torch.cat([torch.sin(snake_case			), torch.cos(snake_case			)]			,   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(snake_case			,   (0, 1, 0, 0)			)
						return emb




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



						'''simple docstring'''

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

						__UpperCamelCase  	=      grid.reshape([2, 1, grid_size, grid_size]			)
						__UpperCamelCase  	=      get_ad_sincos_pos_embed_from_grid(snake_case			,   snake_case			)
						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				: List[str]			,   snake_case				: str			)   ->     List[str]:



						'''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				: Optional[int]			,   snake_case				: Tuple			)   ->     Optional[int]:



						'''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'''			,   snake_case			,   snake_case			)  # (M, D/2), outer product

						__UpperCamelCase  	=      np.sin(snake_case			)  # (M, D/2)
						__UpperCamelCase  	=      np.cos(snake_case			)  # (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  ,						)-> str:





													'''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      )-> 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_      )-> List[Any]:





													'''simple docstring'''






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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''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      )-> Optional[Any]:





													'''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_      )-> str:





													'''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_  ,						)-> Dict:





													'''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_      )-> Dict:





													'''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_      )-> Tuple:





													'''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      )-> Union[str, 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      )-> int:





													'''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  ,						)-> List[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.Linear(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



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





													'''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[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.LayerNorm(SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''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      )-> Any:





													'''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      )-> int:





													'''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[int]:





													'''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_      )-> Optional[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_      )-> str:





													'''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_      )-> List[Any]:





													'''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[str]:





													'''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_      )-> List[str]:





													'''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      )-> Union[str, Any]:





													'''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_      )-> Any:





													'''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







'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
						        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
						        "Not needed for `l0`"
						    ),
						)
						parser.add_argument(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





