


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Tuple      ,						lowercase__  :Tuple = False      ,						lowercase__  :Optional[int] = 1      ,						lowercase__  :Dict = 1      ,						lowercase__  :int = 1_0_0_0_0      ,						)       ->  Tuple:
						assert len(timesteps.shape				) == 1, "Timesteps should be a 1d-array"

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

						__magic_name__				:							Dict				=      torch.exp(UpperCAmelCase_				)
						__magic_name__				:							List[str]				=      timesteps[:, None].float() * emb[None, :]

						# scale embeddings
						__magic_name__				:							str				=      scale * emb

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

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

						# zero pad
						if embedding_dim % 2 == 1:
												__magic_name__				:							Tuple				=      torch.nn.functional.pad(UpperCAmelCase_      ,						(0, 1, 0, 0)				)
						return emb
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Dict      ,						lowercase__  :Any=False      ,						lowercase__  :Dict=0				)       ->  Optional[Any]:
						__magic_name__				:							Dict				=      np.arange(UpperCAmelCase_      ,						dtype=np.floataa				)
						__magic_name__				:							List[Any]				=      np.arange(UpperCAmelCase_      ,						dtype=np.floataa				)
						__magic_name__				:							str				=      np.meshgrid(UpperCAmelCase_      ,						UpperCAmelCase_				)  # here w goes first
						__magic_name__				:							Optional[Any]				=      np.stack(UpperCAmelCase_      ,						axis=0				)

						__magic_name__				:							str				=      grid.reshape([2, 1, grid_size, grid_size]				)
						__magic_name__				:							Union[str, Any]				=      get_ad_sincos_pos_embed_from_grid(UpperCAmelCase_      ,						UpperCAmelCase_				)
						if cls_token and extra_tokens > 0:
												__magic_name__				:							Optional[int]				=      np.concatenate([np.zeros([extra_tokens, embed_dim]				), pos_embed]      ,						axis=0				)
						return pos_embed
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :int      ,						lowercase__  :Tuple				)       ->  List[Any]:






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

						# use half of dimensions to encode grid_h
						__magic_name__				:							Union[str, Any]				=      get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,						grid[0]				)  # (H*W, D/2)
						__magic_name__				:							List[str]				=      get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,						grid[1]				)  # (H*W, D/2)

						__magic_name__				:							str				=      np.concatenate([emb_h, emb_w]      ,						axis=1				)  # (H*W, D)
						return emb
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :Union[str, Any]				)       ->  Any:






						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__				:							List[str]				=      1.0 / 1_0_0_0_0**omega  # (D/2,)

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

						__magic_name__				:							List[Any]				=      np.sin(UpperCAmelCase_				)  # (M, D/2)
						__magic_name__				:							int				=      np.cos(UpperCAmelCase_				)  # (M, D/2)

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



class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase=224     ,      _UpperCAmelCase=224     ,      _UpperCAmelCase=16     ,      _UpperCAmelCase=3     ,      _UpperCAmelCase=768     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      ):




										"""simple docstring"""




										super().__init__()

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

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

										__magic_name__				:							List[str]				=      get_ad_sincos_pos_embed(_UpperCAmelCase     ,      int(num_patches**0.5      )      )
										self.register_buffer('pos_embed'     ,      torch.from_numpy(_UpperCAmelCase      ).float().unsqueeze(0      )     ,      persistent=_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = "silu"     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase=None     ,      ):




										"""simple docstring"""




										super().__init__()

										__magic_name__				:							Optional[int]				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase      )

										if cond_proj_dim is not None:
																__magic_name__				:							Dict				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase     ,      bias=_UpperCAmelCase      )
										else:
																__magic_name__				:							int				=      None

										__magic_name__				:							List[str]				=      get_activation(_UpperCAmelCase      )

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

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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										if condition is not None:
																__magic_name__				:							Tuple				=      sample + self.cond_proj(_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      self.linear_a(_UpperCAmelCase      )

										if self.act is not None:
																__magic_name__				:							List[str]				=      self.act(_UpperCAmelCase      )

										__magic_name__				:							Any				=      self.linear_a(_UpperCAmelCase      )

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





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      get_timestep_embedding(
										    _UpperCAmelCase     ,      self.num_channels     ,      flip_sin_to_cos=self.flip_sin_to_cos     ,      downscale_freq_shift=self.downscale_freq_shift     ,      )
										return t_emb





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase = 256     ,      _UpperCAmelCase = 1.0     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=False      ):




										"""simple docstring"""




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

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

																__magic_name__				:							Dict				=      self.W





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if self.log:
																__magic_name__				:							List[str]				=      torch.log(_UpperCAmelCase      )

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

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





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__()

										__magic_name__				:							Any				=      height
										__magic_name__				:							Optional[int]				=      width
										__magic_name__				:							int				=      num_embed
										__magic_name__				:							List[str]				=      embed_dim

										__magic_name__				:							str				=      nn.Embedding(self.num_embed     ,      _UpperCAmelCase      )
										__magic_name__				:							List[str]				=      nn.Embedding(self.height     ,      _UpperCAmelCase      )
										__magic_name__				:							str				=      nn.Embedding(self.width     ,      _UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.emb(_UpperCAmelCase      )

										__magic_name__				:							Optional[Any]				=      self.height_emb(torch.arange(self.height     ,      device=index.device      ).view(1     ,      self.height      )      )

										# 1 x H x D -> 1 x H x 1 x D
										__magic_name__				:							Dict				=      height_emb.unsqueeze(2      )

										__magic_name__				:							int				=      self.width_emb(torch.arange(self.width     ,      device=index.device      ).view(1     ,      self.width      )      )

										# 1 x W x D -> 1 x 1 x W x D
										__magic_name__				:							int				=      width_emb.unsqueeze(1      )

										__magic_name__				:							List[Any]				=      height_emb + width_emb

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

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

										return emb





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										super().__init__()
										__magic_name__				:							Union[str, Any]				=      dropout_prob > 0
										__magic_name__				:							Any				=      nn.Embedding(num_classes + use_cfg_embedding     ,      _UpperCAmelCase      )
										__magic_name__				:							str				=      num_classes
										__magic_name__				:							Dict				=      dropout_prob
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




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





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase = 1024     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 10     ,      ):




										"""simple docstring"""




										super().__init__()

										__magic_name__				:							Optional[int]				=      num_image_text_embeds
										__magic_name__				:							Tuple				=      nn.Linear(_UpperCAmelCase     ,      self.num_image_text_embeds * cross_attention_dim      )
										__magic_name__				:							Dict				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      text_embeds.shape[0]

										# image
										__magic_name__				:							List[str]				=      self.image_embeds(_UpperCAmelCase      )
										__magic_name__				:							str				=      image_text_embeds.reshape(_UpperCAmelCase     ,      self.num_image_text_embeds     ,      -1      )

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

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





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 32     ,      ):




										"""simple docstring"""




										super().__init__()

										__magic_name__				:							Tuple				=      num_image_text_embeds
										__magic_name__				:							List[Any]				=      nn.Linear(_UpperCAmelCase     ,      self.num_image_text_embeds * cross_attention_dim      )
										__magic_name__				:							str				=      nn.LayerNorm(_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      image_embeds.shape[0]

										# image
										__magic_name__				:							Optional[Any]				=      self.image_embeds(_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      image_embeds.reshape(_UpperCAmelCase     ,      self.num_image_text_embeds     ,      -1      )
										__magic_name__				:							Tuple				=      self.norm(_UpperCAmelCase      )
										return image_embeds





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=0.1      ):




										"""simple docstring"""




										super().__init__()

										__magic_name__				:							Optional[Any]				=      Timesteps(num_channels=256     ,      flip_sin_to_cos=_UpperCAmelCase     ,      downscale_freq_shift=1      )
										__magic_name__				:							Tuple				=      TimestepEmbedding(in_channels=256     ,      time_embed_dim=_UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      LabelEmbedding(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      self.time_proj(_UpperCAmelCase      )
										__magic_name__				:							Tuple				=      self.timestep_embedder(timesteps_proj.to(dtype=_UpperCAmelCase      )      )  # (N, D)

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

										__magic_name__				:							List[Any]				=      timesteps_emb + class_labels  # (N, D)

										return conditioning





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = 64      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.norma(_UpperCAmelCase      )
										__magic_name__				:							Tuple				=      self.pool(_UpperCAmelCase      )
										__magic_name__				:							Tuple				=      self.proj(_UpperCAmelCase      )
										__magic_name__				:							int				=      self.norma(_UpperCAmelCase      )
										return hidden_states





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 1536      ):




										"""simple docstring"""




										super().__init__()
										__magic_name__				:							str				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase      )
										__magic_name__				:							Optional[Any]				=      nn.LayerNorm(_UpperCAmelCase      )
										__magic_name__				:							List[str]				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.text_proj(_UpperCAmelCase      )
										__magic_name__				:							Any				=      self.text_norm(_UpperCAmelCase      )

										# image
										__magic_name__				:							int				=      self.image_proj(_UpperCAmelCase      )

										return time_image_embeds + time_text_embeds





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 1536      ):




										"""simple docstring"""




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





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.image_proj(_UpperCAmelCase      )
										__magic_name__				:							Optional[Any]				=      self.image_norm(_UpperCAmelCase      )
										return time_image_embeds





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase = 768     ,      _UpperCAmelCase = 1536      ):




										"""simple docstring"""




										super().__init__()
										__magic_name__				:							Dict				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase      )
										__magic_name__				:							List[Any]				=      nn.LayerNorm(_UpperCAmelCase      )
										__magic_name__				:							int				=      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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




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





class     snake_case__  (      nn.Module  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										super().__init__()
										__magic_name__				:							Any				=      dtype
										__magic_name__				:							List[Any]				=      nn.Parameter(torch.randn(1     ,      _UpperCAmelCase      ) / embed_dim**0.5      )
										__magic_name__				:							Optional[int]				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase     ,      dtype=self.dtype      )
										__magic_name__				:							Optional[Any]				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase     ,      dtype=self.dtype      )
										__magic_name__				:							Union[str, Any]				=      nn.Linear(_UpperCAmelCase     ,      _UpperCAmelCase     ,      dtype=self.dtype      )
										__magic_name__				:							Union[str, Any]				=      num_heads
										__magic_name__				:							Optional[Any]				=      embed_dim // self.num_heads





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      x.size()

										def shape(_UpperCAmelCase      ):
																# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																__magic_name__				:							int				=      x.view(_UpperCAmelCase     ,      -1     ,      self.num_heads     ,      self.dim_per_head      )
																# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
																__magic_name__				:							Optional[Any]				=      x.transpose(1     ,      2      )
																# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																__magic_name__				:							Optional[int]				=      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__				:							Dict				=      x.transpose(1     ,      2      )
																return x

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

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

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

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

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

										return a[:, 0, :]  # cls_token






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


snake_case__     							= logging.get_logger(__name__)



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=21     ,      _UpperCAmelCase=[]     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      model
										__magic_name__				:							List[str]				=      trace_batch_nums
										__magic_name__				:							int				=      abort_after_batch_num

										# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
										__magic_name__				:							Union[str, Any]				=      collections.deque([]     ,      _UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      []
										__magic_name__				:							List[str]				=      0
										__magic_name__				:							Optional[int]				=      0
										__magic_name__				:							Any				=      False
										__magic_name__				:							str				=      '                 '

										self.analyse_model()

										self.register_forward_hook()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										if frame is not None:
																self.expand_frame(_UpperCAmelCase      )
										self.frames.append('\n'.join(self.frame      )      )
										__magic_name__				:							List[Any]				=      []  # start a new frame
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										self.frame.append(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										print('\n'.join(self.frames      )      )
										__magic_name__				:							List[str]				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										print(F"\nDetected inf/nan during batch_number={self.batch_number}"      )
										print(F"Last {len(self.frames      )} forward frames:"      )
										print(F"{'abs min':8} {'abs max':8} metadata"      )
										print('\n'.join(self.frames      )      )
										print('\n\n'      )
										__magic_name__				:							Any				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      {m: name for name, m in self.model.named_modules()}
										# self.longest_module_name = max(len(v) for v in self.module_names.values())
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if torch.is_tensor(_UpperCAmelCase      ):
																self.expand_frame(get_abs_min_max(_UpperCAmelCase     ,      _UpperCAmelCase      )      )
																if detect_overflow(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																						__magic_name__				:							List[str]				=      True
										elif var is None:
																self.expand_frame(F"{'None':>17} {ctx}"      )
										else:
																self.expand_frame(F"{'not a tensor':>17} {ctx}"      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"      )
										self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"      )

										# params
										for name, p in module.named_parameters(recurse=_UpperCAmelCase      ):
																self.analyse_variable(_UpperCAmelCase     ,      _UpperCAmelCase      )

										# inputs
										if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																for i, x in enumerate(_UpperCAmelCase      ):
																						self.analyse_variable(_UpperCAmelCase     ,      F"input[{i}]"      )
										else:
																self.analyse_variable(_UpperCAmelCase     ,      'input'      )

										# outputs
										if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																for i, x in enumerate(_UpperCAmelCase      ):
																						# possibly a tuple of tuples
																						if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																												for j, y in enumerate(_UpperCAmelCase      ):
																																		self.analyse_variable(_UpperCAmelCase     ,      F"output[{i}][{j}]"      )
																						else:
																												self.analyse_variable(_UpperCAmelCase     ,      F"output[{i}]"      )
										else:
																self.analyse_variable(_UpperCAmelCase     ,      'output'      )

										self.save_frame()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.model.apply(self._register_forward_hook      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										module.register_forward_hook(self.forward_hook      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      False

										__magic_name__				:							Any				=      True if self.batch_number in self.trace_batch_nums else False
										if trace_mode:
																self.reset_saved_frames()

										if self.total_calls == 0:
																self.batch_start_frame()
										self.total_calls += 1

										# count batch numbers - the very first forward hook of the batch will be called when the
										# batch completes - i.e. it gets called very last - we know this batch has finished
										if module == self.model:
																self.batch_number += 1
																__magic_name__				:							str				=      True

										self.create_frame(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )

										# if last_frame_of_batch:
										#     self.batch_end_frame()

										if trace_mode:
																self.trace_frames()

										if last_frame_of_batch:
																self.batch_start_frame()

										if self.detected_overflow and not trace_mode:
																self.dump_saved_frames()

																# now we can abort, as it's pointless to continue running
																raise ValueError(
																    'DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '
																    'Please scroll up above this traceback to see the activation values prior to this event.'      )

										# abort after certain batch if requested to do so
										if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																raise ValueError(
																    F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
																    F" `abort_after_batch_num={self.abort_after_batch_num}` arg"      )






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :Any				)       ->  Any:
						__magic_name__				:							List[Any]				=      var.abs()
						return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Optional[int]				)       ->  Optional[Any]:
						__magic_name__				:							str				=      False
						if torch.isnan(lowercase__				).any().item():
												__magic_name__				:							Optional[Any]				=      True
												print(f"{ctx} has nans"				)
						if torch.isinf(lowercase__				).any().item():
												__magic_name__				:							str				=      True
												print(f"{ctx} has infs"				)

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												__magic_name__				:							Any				=      var[torch.ge(var.abs()      ,						1_0_0				)]
												if naaa.numel() > 0:
																		print(f"{ctx}:  n100={naaa.numel()}"				)
												__magic_name__				:							Union[str, Any]				=      var[torch.ge(var.abs()      ,						1_0_0_0				)]
												if naaaa.numel() > 0:
																		print(f"{ctx}: n1000={naaaa.numel()}"				)
												__magic_name__				:							Any				=      var[torch.ge(var.abs()      ,						1_0_0_0_0				)]
												if naaaaa.numel() > 0:
																		print(f"{ctx}: n10000={naaaaa.numel()}"				)

						if 0:
												print(f"min={var.min():9.2e} max={var.max():9.2e}"				)

						if 0:
												print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"				)

						return detected



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''underflow_overflow'''
				__lowerCAmelCase		=      '''tpu_metrics_debug'''


