





'''simple docstring'''






import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation





def      lowercase  (  lowerCAmelCase :						Dict   , lowerCAmelCase :						Dict   , lowerCAmelCase :						Optional[Any] = False   , lowerCAmelCase :						Optional[int] = 1   , lowerCAmelCase :						Dict = 1   , lowerCAmelCase :						List[str] = 1_0000   , ):



				"""simple docstring"""


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

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

				_A :				List[Any]				=  torch.exp(_SCREAMING_SNAKE_CASE)
				_A :				Optional[int]				=  timesteps[:, None].float() * emb[None, :]

				# scale embeddings
				_A :				int				=  scale * emb

				# concat sine and cosine embeddings
				_A :				List[Any]				=  torch.cat([torch.sin(_SCREAMING_SNAKE_CASE), torch.cos(_SCREAMING_SNAKE_CASE)]   , dim=-1)

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

				# zero pad
				if embedding_dim % 2 == 1:
								_A :				List[Any]				=  torch.nn.functional.pad(_SCREAMING_SNAKE_CASE   , (0, 1, 0, 0))
				return emb





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						List[str]   , lowerCAmelCase :						Optional[int]=False   , lowerCAmelCase :						Union[str, Any]=0):



				"""simple docstring"""


				_A :				Optional[int]				=  np.arange(_SCREAMING_SNAKE_CASE   , dtype=np.floataa)
				_A :				List[str]				=  np.arange(_SCREAMING_SNAKE_CASE   , dtype=np.floataa)
				_A :				str				=  np.meshgrid(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE)  # here w goes first
				_A :				Optional[Any]				=  np.stack(_SCREAMING_SNAKE_CASE   , axis=0)

				_A :				List[str]				=  grid.reshape([2, 1, grid_size, grid_size])
				_A :				Optional[Any]				=  get_ad_sincos_pos_embed_from_grid(_SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE)
				if cls_token and extra_tokens > 0:
								_A :				int				=  np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed]   , axis=0)
				return pos_embed





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


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

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

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





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						Optional[Any]):



				"""simple docstring"""


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

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

				_A :				int				=  pos.reshape(-1)  # (M,)
				_A :				Union[str, Any]				=  np.einsum('''m,d->md'''   , _SCREAMING_SNAKE_CASE   , _SCREAMING_SNAKE_CASE)  # (M, D/2), outer product

				_A :				List[Any]				=  np.sin(_SCREAMING_SNAKE_CASE)  # (M, D/2)
				_A :				Optional[Any]				=  np.cos(_SCREAMING_SNAKE_CASE)  # (M, D/2)

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



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__=2_2_4   ,     UpperCAmelCase__=2_2_4   ,     UpperCAmelCase__=1_6   ,     UpperCAmelCase__=3   ,     UpperCAmelCase__=7_6_8   ,     UpperCAmelCase__=False   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=True   ,     )       ->				Optional[Any]:
					super().__init__()

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

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

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




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



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = "silu"   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__=None   ,     )       ->				Optional[int]:
					super().__init__()

					_A :				List[Any]				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)

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

					_A :				List[Any]				=  get_activation(lowerCamelCase__			)

					if out_dim is not None:
									_A :				Tuple				=  out_dim
					else:
									_A :				Optional[Any]				=  time_embed_dim
					_A :				Union[str, Any]				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)

					if post_act_fn is None:
									_A :				List[str]				=  None
					else:
									_A :				int				=  get_activation(lowerCamelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				str:


					if condition is not None:
									_A :				Any				=  sample + self.cond_proj(lowerCamelCase__			)
					_A :				List[Any]				=  self.linear_a(lowerCamelCase__			)

					if self.act is not None:
									_A :				Any				=  self.act(lowerCamelCase__			)

					_A :				int				=  self.linear_a(lowerCamelCase__			)

					if self.post_act is not None:
									_A :				Dict				=  self.post_act(lowerCamelCase__			)
					return sample



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				List[Any]:
					super().__init__()
					_A :				int				=  num_channels
					_A :				Union[str, Any]				=  flip_sin_to_cos
					_A :				Optional[int]				=  downscale_freq_shift




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				int:
					_A :				Dict				=  get_timestep_embedding(
					    lowerCamelCase__   ,     self.num_channels   ,     flip_sin_to_cos=self.flip_sin_to_cos   ,     downscale_freq_shift=self.downscale_freq_shift   ,     )
					return t_emb



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__ = 2_5_6   ,     UpperCAmelCase__ = 1.0   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=True   ,     UpperCAmelCase__=False			)       ->				Tuple:
					super().__init__()
					_A :				Tuple				=  nn.Parameter(torch.randn(lowerCamelCase__			) * scale   ,     requires_grad=lowerCamelCase__			)
					_A :				Optional[int]				=  log
					_A :				List[Any]				=  flip_sin_to_cos

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

									_A :				Optional[Any]				=  self.W




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Optional[int]:


					if self.log:
									_A :				Optional[int]				=  torch.log(lowerCamelCase__			)

					_A :				Union[str, Any]				=  x[:, None] * self.weight[None, :] * 2 * np.pi

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



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     )       ->				List[Any]:
					super().__init__()

					_A :				Dict				=  height
					_A :				int				=  width
					_A :				str				=  num_embed
					_A :				Optional[Any]				=  embed_dim

					_A :				Any				=  nn.Embedding(self.num_embed   ,     lowerCamelCase__			)
					_A :				Dict				=  nn.Embedding(self.height   ,     lowerCamelCase__			)
					_A :				str				=  nn.Embedding(self.width   ,     lowerCamelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					_A :				Any				=  self.emb(lowerCamelCase__			)

					_A :				List[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
					_A :				List[Any]				=  height_emb.unsqueeze(2			)

					_A :				Tuple				=  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
					_A :				Any				=  width_emb.unsqueeze(1			)

					_A :				List[str]				=  height_emb + width_emb

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

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

					return emb



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Tuple:
					super().__init__()
					_A :				List[str]				=  dropout_prob > 0
					_A :				str				=  nn.Embedding(num_classes + use_cfg_embedding   ,     lowerCamelCase__			)
					_A :				Tuple				=  num_classes
					_A :				List[Any]				=  dropout_prob




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:


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




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				Optional[Any]:
					_A :				List[str]				=  self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
									_A :				List[str]				=  self.token_drop(lowerCamelCase__   ,     lowerCamelCase__			)
					_A :				Any				=  self.embedding_table(lowerCamelCase__			)
					return embeddings



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__ = 1_0_2_4   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 1_0   ,     )       ->				str:
					super().__init__()

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




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				int:
					_A :				str				=  text_embeds.shape[0]

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

					# text
					_A :				Optional[int]				=  self.text_proj(lowerCamelCase__			)

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



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 3_2   ,     )       ->				Any:
					super().__init__()

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




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					_A :				List[Any]				=  image_embeds.shape[0]

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



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__=0.1			)       ->				int:
					super().__init__()

					_A :				str				=  Timesteps(num_channels=2_5_6   ,     flip_sin_to_cos=lowerCamelCase__   ,     downscale_freq_shift=1			)
					_A :				Dict				=  TimestepEmbedding(in_channels=2_5_6   ,     time_embed_dim=lowerCamelCase__			)
					_A :				Tuple				=  LabelEmbedding(lowerCamelCase__   ,     lowerCamelCase__   ,     lowerCamelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[str]:
					_A :				List[Any]				=  self.time_proj(lowerCamelCase__			)
					_A :				Any				=  self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase__			)			)  # (N, D)

					_A :				int				=  self.class_embedder(lowerCamelCase__			)  # (N, D)

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

					return conditioning



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = 6_4			)       ->				int:
					super().__init__()
					_A :				Union[str, Any]				=  nn.LayerNorm(lowerCamelCase__			)
					_A :				int				=  AttentionPooling(lowerCamelCase__   ,     lowerCamelCase__			)
					_A :				Union[str, Any]				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)
					_A :				Optional[int]				=  nn.LayerNorm(lowerCamelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A :				Optional[Any]				=  self.norma(lowerCamelCase__			)
					_A :				Union[str, Any]				=  self.pool(lowerCamelCase__			)
					_A :				Union[str, Any]				=  self.proj(lowerCamelCase__			)
					_A :				List[str]				=  self.norma(lowerCamelCase__			)
					return hidden_states



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 1_5_3_6			)       ->				Tuple:
					super().__init__()
					_A :				Dict				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)
					_A :				List[str]				=  nn.LayerNorm(lowerCamelCase__			)
					_A :				Optional[Any]				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				str:
					_A :				Optional[Any]				=  self.text_proj(lowerCamelCase__			)
					_A :				Optional[Any]				=  self.text_norm(lowerCamelCase__			)

					# image
					_A :				int				=  self.image_proj(lowerCamelCase__			)

					return time_image_embeds + time_text_embeds



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 1_5_3_6			)       ->				List[Any]:
					super().__init__()
					_A :				List[str]				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)
					_A :				Union[str, Any]				=  nn.LayerNorm(lowerCamelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Optional[Any]:
					_A :				List[str]				=  self.image_proj(lowerCamelCase__			)
					_A :				int				=  self.image_norm(lowerCamelCase__			)
					return time_image_embeds



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__ = 7_6_8   ,     UpperCAmelCase__ = 1_5_3_6			)       ->				List[str]:
					super().__init__()
					_A :				Dict				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__			)
					_A :				Any				=  nn.LayerNorm(lowerCamelCase__			)
					_A :				Dict				=  nn.Sequential(
					    nn.Convad(3   ,     1_6   ,     3   ,     padding=1			)   ,     nn.SiLU()   ,     nn.Convad(1_6   ,     1_6   ,     3   ,     padding=1			)   ,     nn.SiLU()   ,     nn.Convad(1_6   ,     3_2   ,     3   ,     padding=1   ,     stride=2			)   ,     nn.SiLU()   ,     nn.Convad(3_2   ,     3_2   ,     3   ,     padding=1			)   ,     nn.SiLU()   ,     nn.Convad(3_2   ,     9_6   ,     3   ,     padding=1   ,     stride=2			)   ,     nn.SiLU()   ,     nn.Convad(9_6   ,     9_6   ,     3   ,     padding=1			)   ,     nn.SiLU()   ,     nn.Convad(9_6   ,     2_5_6   ,     3   ,     padding=1   ,     stride=2			)   ,     nn.SiLU()   ,     nn.Convad(2_5_6   ,     4   ,     3   ,     padding=1			)   ,     )




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					_A :				Union[str, Any]				=  self.image_proj(lowerCamelCase__			)
					_A :				List[str]				=  self.image_norm(lowerCamelCase__			)
					_A :				int				=  self.input_hint_block(lowerCamelCase__			)
					return time_image_embeds, hint



class  lowerCamelCase__				(						nn.Module						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				Dict:
					super().__init__()
					_A :				Optional[int]				=  dtype
					_A :				int				=  nn.Parameter(torch.randn(1   ,     lowerCamelCase__			) / embed_dim**0.5			)
					_A :				Dict				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__   ,     dtype=self.dtype			)
					_A :				int				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__   ,     dtype=self.dtype			)
					_A :				Optional[Any]				=  nn.Linear(lowerCamelCase__   ,     lowerCamelCase__   ,     dtype=self.dtype			)
					_A :				List[Any]				=  num_heads
					_A :				Union[str, Any]				=  embed_dim // self.num_heads




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Any:
					_A	,       _A	,       _A :				Union[str, Any]				=  x.size()

					def shape(UpperCAmelCase__			):
									# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
									_A :				Union[str, Any]				=  x.view(lowerCamelCase__   ,     -1   ,     self.num_heads   ,     self.dim_per_head			)
									# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
									_A :				Optional[Any]				=  x.transpose(1   ,     2			)
									# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
									_A :				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)
									_A :				Tuple				=  x.transpose(1   ,     2			)
									return x

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

					# (bs*n_heads, class_token_length, dim_per_head)
					_A :				str				=  shape(self.q_proj(lowerCamelCase__			)			)
					# (bs*n_heads, length+class_token_length, dim_per_head)
					_A :				int				=  shape(self.k_proj(lowerCamelCase__			)			)
					_A :				Tuple				=  shape(self.v_proj(lowerCamelCase__			)			)

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

					# (bs*n_heads, dim_per_head, class_token_length)
					_A :				Optional[int]				=  torch.einsum('''bts,bcs->bct'''   ,     lowerCamelCase__   ,     lowerCamelCase__			)

					# (bs, length+1, width)
					_A :				Any				=  a.reshape(lowerCamelCase__   ,     -1   ,     1			).transpose(1   ,     2			)

					return a[:, 0, :]  # cls_token







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
      model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


