
'''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     (      UpperCamelCase_					:     torch.Tensor						,	UpperCamelCase_					:     int						,	UpperCamelCase_					:     bool = False						,	UpperCamelCase_					:     float = 1						,	UpperCamelCase_					:     float = 1						,	UpperCamelCase_					:     int = 1_00_00						,	)					->       Dict:







			'''simple docstring'''



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

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

			lowerCAmelCase__								=							torch.exp(UpperCamelCase_      )
			lowerCAmelCase__								=							timesteps[:, None].float() * emb[None, :]

			# scale embeddings
			lowerCAmelCase__								=							scale * emb

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

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

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







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple=False						,	UpperCamelCase_					:     Any=0      )					->       List[str]:







			'''simple docstring'''



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

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







def   A     (      UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Any      )					->       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
			lowerCAmelCase__								=							get_ad_sincos_pos_embed_from_grid(embed_dim // 2						,	grid[0]      )  # (H*W, D/2)
			lowerCAmelCase__								=							get_ad_sincos_pos_embed_from_grid(embed_dim // 2						,	grid[1]      )  # (H*W, D/2)

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







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Union[str, Any]      )					->       int:







			'''simple docstring'''



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

			lowerCAmelCase__								=							np.arange(embed_dim // 2						,	dtype=np.floataa      )
			omega /= embed_dim / 2.0
			lowerCAmelCase__								=							1.0 / 1_00_00**omega  # (D/2,)

			lowerCAmelCase__								=							pos.reshape(-1      )  # (M,)
			lowerCAmelCase__								=							np.einsum("m,d->md"						,	UpperCamelCase_						,	UpperCamelCase_      )  # (M, D/2), outer product

			lowerCAmelCase__								=							np.sin(UpperCamelCase_      )  # (M, D/2)
			lowerCAmelCase__								=							np.cos(UpperCamelCase_      )  # (M, D/2)

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






class    A     (	nn.Module    ):





	def __init__(					self			:       Union[str, Any]      ,							__magic_name__			:       List[Any]=224      ,							__magic_name__			:       List[str]=224      ,							__magic_name__			:       List[Any]=16      ,							__magic_name__			:       str=3      ,							__magic_name__			:       int=768      ,							__magic_name__			:       Union[str, Any]=False      ,							__magic_name__			:       Tuple=True      ,							__magic_name__			:       Tuple=True      ,							):


				"""simple docstring"""

				super().__init__()

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

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

				lowerCAmelCase__								=							get_ad_sincos_pos_embed(__magic_name__      ,							int(num_patches**0.5							)							)
				self.register_buffer("pos_embed"      ,							torch.from_numpy(__magic_name__							).float().unsqueeze(0							)      ,							persistent=__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Union[str, Any]							):


				"""simple docstring"""

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






class    A     (	nn.Module    ):





	def __init__(					self			:       Any      ,							__magic_name__			:       int      ,							__magic_name__			:       int      ,							__magic_name__			:       str = "silu"      ,							__magic_name__			:       int = None      ,							__magic_name__			:       Optional[str] = None      ,							__magic_name__			:       int=None      ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)

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

				lowerCAmelCase__								=							get_activation(__magic_name__							)

				if out_dim is not None:
							lowerCAmelCase__								=							out_dim
				else:
							lowerCAmelCase__								=							time_embed_dim
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)

				if post_act_fn is None:
							lowerCAmelCase__								=							None
				else:
							lowerCAmelCase__								=							get_activation(__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Union[str, Any]=None							):


				"""simple docstring"""

				if condition is not None:
							lowerCAmelCase__								=							sample + self.cond_proj(__magic_name__							)
				lowerCAmelCase__								=							self.linear_a(__magic_name__							)

				if self.act is not None:
							lowerCAmelCase__								=							self.act(__magic_name__							)

				lowerCAmelCase__								=							self.linear_a(__magic_name__							)

				if self.post_act is not None:
							lowerCAmelCase__								=							self.post_act(__magic_name__							)
				return sample






class    A     (	nn.Module    ):





	def __init__(					self			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       bool      ,							__magic_name__			:       float							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       str							):


				"""simple docstring"""

				lowerCAmelCase__								=							get_timestep_embedding(
				    __magic_name__      ,							self.num_channels      ,							flip_sin_to_cos=self.flip_sin_to_cos      ,							downscale_freq_shift=self.downscale_freq_shift      ,							)
				return t_emb






class    A     (	nn.Module    ):





	def __init__(					self			:       Optional[int]      ,							__magic_name__			:       int = 256      ,							__magic_name__			:       float = 1.0      ,							__magic_name__			:       Optional[Any]=True      ,							__magic_name__			:       Optional[int]=True      ,							__magic_name__			:       Any=False							):


				"""simple docstring"""

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

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

							lowerCAmelCase__								=							self.W







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

				if self.log:
							lowerCAmelCase__								=							torch.log(__magic_name__							)

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

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






class    A     (	nn.Module    ):





	def __init__(					self			:       Tuple      ,							__magic_name__			:       int      ,							__magic_name__			:       int      ,							__magic_name__			:       int      ,							__magic_name__			:       int      ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__								=							height
				lowerCAmelCase__								=							width
				lowerCAmelCase__								=							num_embed
				lowerCAmelCase__								=							embed_dim

				lowerCAmelCase__								=							nn.Embedding(self.num_embed      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.Embedding(self.height      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.Embedding(self.width      ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Dict							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.emb(__magic_name__							)

				lowerCAmelCase__								=							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
				lowerCAmelCase__								=							height_emb.unsqueeze(2							)

				lowerCAmelCase__								=							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
				lowerCAmelCase__								=							width_emb.unsqueeze(1							)

				lowerCAmelCase__								=							height_emb + width_emb

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

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

				return emb






class    A     (	nn.Module    ):





	def __init__(					self			:       Optional[int]      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__								=							dropout_prob > 0
				lowerCAmelCase__								=							nn.Embedding(num_classes + use_cfg_embedding      ,							__magic_name__							)
				lowerCAmelCase__								=							num_classes
				lowerCAmelCase__								=							dropout_prob





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       Any=None							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       torch.LongTensor      ,							__magic_name__			:       List[Any]=None							):


				"""simple docstring"""

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






class    A     (	nn.Module    ):





	def __init__(					self			:       Optional[int]      ,							__magic_name__			:       int = 1024      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 10      ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__								=							num_image_text_embeds
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							self.num_image_text_embeds * cross_attention_dim							)
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       torch.FloatTensor      ,							__magic_name__			:       torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__								=							text_embeds.shape[0]

				# image
				lowerCAmelCase__								=							self.image_embeds(__magic_name__							)
				lowerCAmelCase__								=							image_text_embeds.reshape(__magic_name__      ,							self.num_image_text_embeds      ,							-1							)

				# text
				lowerCAmelCase__								=							self.text_proj(__magic_name__							)

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






class    A     (	nn.Module    ):





	def __init__(					self			:       str      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 32      ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__								=							num_image_text_embeds
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							self.num_image_text_embeds * cross_attention_dim							)
				lowerCAmelCase__								=							nn.LayerNorm(__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]      ,							__magic_name__			:       torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__								=							image_embeds.shape[0]

				# image
				lowerCAmelCase__								=							self.image_embeds(__magic_name__							)
				lowerCAmelCase__								=							image_embeds.reshape(__magic_name__      ,							self.num_image_text_embeds      ,							-1							)
				lowerCAmelCase__								=							self.norm(__magic_name__							)
				return image_embeds






class    A     (	nn.Module    ):





	def __init__(					self			:       Any      ,							__magic_name__			:       str      ,							__magic_name__			:       str      ,							__magic_name__			:       str=0.1							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__								=							Timesteps(num_channels=256      ,							flip_sin_to_cos=__magic_name__      ,							downscale_freq_shift=1							)
				lowerCAmelCase__								=							TimestepEmbedding(in_channels=256      ,							time_embed_dim=__magic_name__							)
				lowerCAmelCase__								=							LabelEmbedding(__magic_name__      ,							__magic_name__      ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]      ,							__magic_name__			:       Any      ,							__magic_name__			:       List[str]      ,							__magic_name__			:       Any=None							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.time_proj(__magic_name__							)
				lowerCAmelCase__								=							self.timestep_embedder(timesteps_proj.to(dtype=__magic_name__							)							)  # (N, D)

				lowerCAmelCase__								=							self.class_embedder(__magic_name__							)  # (N, D)

				lowerCAmelCase__								=							timesteps_emb + class_labels  # (N, D)

				return conditioning






class    A     (	nn.Module    ):





	def __init__(					self			:       List[str]      ,							__magic_name__			:       int      ,							__magic_name__			:       int      ,							__magic_name__			:       int = 64							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__								=							nn.LayerNorm(__magic_name__							)
				lowerCAmelCase__								=							AttentionPooling(__magic_name__      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.LayerNorm(__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.norma(__magic_name__							)
				lowerCAmelCase__								=							self.pool(__magic_name__							)
				lowerCAmelCase__								=							self.proj(__magic_name__							)
				lowerCAmelCase__								=							self.norma(__magic_name__							)
				return hidden_states






class    A     (	nn.Module    ):





	def __init__(					self			:       Any      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 1536							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.LayerNorm(__magic_name__							)
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       torch.FloatTensor      ,							__magic_name__			:       torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.text_proj(__magic_name__							)
				lowerCAmelCase__								=							self.text_norm(__magic_name__							)

				# image
				lowerCAmelCase__								=							self.image_proj(__magic_name__							)

				return time_image_embeds + time_text_embeds






class    A     (	nn.Module    ):





	def __init__(					self			:       Dict      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 1536							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.LayerNorm(__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.image_proj(__magic_name__							)
				lowerCAmelCase__								=							self.image_norm(__magic_name__							)
				return time_image_embeds






class    A     (	nn.Module    ):





	def __init__(					self			:       Optional[Any]      ,							__magic_name__			:       int = 768      ,							__magic_name__			:       int = 1536							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__								=							nn.Linear(__magic_name__      ,							__magic_name__							)
				lowerCAmelCase__								=							nn.LayerNorm(__magic_name__							)
				lowerCAmelCase__								=							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 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       torch.FloatTensor      ,							__magic_name__			:       torch.FloatTensor							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.image_proj(__magic_name__							)
				lowerCAmelCase__								=							self.image_norm(__magic_name__							)
				lowerCAmelCase__								=							self.input_hint_block(__magic_name__							)
				return time_image_embeds, hint






class    A     (	nn.Module    ):





	def __init__(					self			:       List[Any]      ,							__magic_name__			:       Any      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Tuple=None							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__								=							x.size()

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

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

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

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

				# (bs*n_heads, dim_per_head, class_token_length)
				lowerCAmelCase__								=							torch.einsum("bts,bcs->bct"      ,							__magic_name__      ,							__magic_name__							)

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

				return a[:, 0, :]  # cls_token







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   : Any					=		{
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__   : str					=		["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
		from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
		from .tokenization_fsmt import FSMTTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
		import sys

		UpperCAmelCase__   : int					=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





