




import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def    snake_case   (				A		,  A		,  A = False		,  A = 1		,  A = 1		,  A = 10000		,  )				->				List[Any]:




	'''simple docstring'''


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

	lowerCamelCase_				:						List[Any]				  =				embedding_dim // 2
	lowerCamelCase_				:						Dict				  =				-math.log(A     ) * torch.arange(
	    start=0		,  end=A		,  dtype=torch.floataa		,  device=timesteps.device     )
	lowerCamelCase_				:						Dict				  =				exponent / (half_dim - downscale_freq_shift)

	lowerCamelCase_				:						Any				  =				torch.exp(A     )
	lowerCamelCase_				:						int				  =				timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	lowerCamelCase_				:						Dict				  =				scale * emb

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

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

	# zero pad
	if embedding_dim % 2 == 1:
		lowerCamelCase_				:						Optional[Any]				  =				torch.nn.functional.pad(A		,  (0, 1, 0, 0)     )
	return emb






def    snake_case   (				A		,  A		,  A=False		,  A=0     )				->				List[str]:




	'''simple docstring'''


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

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






def    snake_case   (				A		,  A     )				->				Tuple:




	'''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_				:						Tuple				  =				get_ad_sincos_pos_embed_from_grid(embed_dim // 2		,  grid[0]     )  # (H*W, D/2)
	lowerCamelCase_				:						Dict				  =				get_ad_sincos_pos_embed_from_grid(embed_dim // 2		,  grid[1]     )  # (H*W, D/2)

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






def    snake_case   (				A		,  A     )				->				List[Any]:




	'''simple docstring'''


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

	lowerCamelCase_				:						Union[str, Any]				  =				np.arange(embed_dim // 2		,  dtype=np.floataa     )
	omega /= embed_dim / 2.0
	lowerCamelCase_				:						int				  =				1.0 / 10000**omega  # (D/2,)

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

	lowerCamelCase_				:						Union[str, Any]				  =				np.sin(A     )  # (M, D/2)
	lowerCamelCase_				:						List[Any]				  =				np.cos(A     )  # (M, D/2)

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


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase=224					,			__lowercase=224					,			__lowercase=16					,			__lowercase=3					,			__lowercase=768					,			__lowercase=False					,			__lowercase=True					,			__lowercase=True					,			):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_				:						Any				  =				(height // patch_size) * (width // patch_size)
						lowerCamelCase_				:						Dict				  =				flatten
						lowerCamelCase_				:						Union[str, Any]				  =				layer_norm

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

						lowerCamelCase_				:						Optional[Any]				  =				get_ad_sincos_pos_embed(__lowercase					,			int(num_patches**0.5   )   )
						self.register_buffer('pos_embed'					,			torch.from_numpy(__lowercase   ).float().unsqueeze(0   )					,			persistent=__lowercase   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				self.proj(__lowercase   )
						if self.flatten:
							lowerCamelCase_				:						List[str]				  =				latent.flatten(2   ).transpose(1					,			2   )  # BCHW -> BNC
						if self.layer_norm:
							lowerCamelCase_				:						str				  =				self.norm(__lowercase   )
						return latent + self.pos_embed


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase = "silu"					,			__lowercase = None					,			__lowercase = None					,			__lowercase=None					,			):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_				:						List[str]				  =				nn.Linear(__lowercase					,			__lowercase   )

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

						lowerCamelCase_				:						Optional[int]				  =				get_activation(__lowercase   )

						if out_dim is not None:
							lowerCamelCase_				:						int				  =				out_dim
						else:
							lowerCamelCase_				:						int				  =				time_embed_dim
						lowerCamelCase_				:						int				  =				nn.Linear(__lowercase					,			__lowercase   )

						if post_act_fn is None:
							lowerCamelCase_				:						int				  =				None
						else:
							lowerCamelCase_				:						int				  =				get_activation(__lowercase   )





					def       lowercase_				(							self					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						if condition is not None:
							lowerCamelCase_				:						int				  =				sample + self.cond_proj(__lowercase   )
						lowerCamelCase_				:						str				  =				self.linear_a(__lowercase   )

						if self.act is not None:
							lowerCamelCase_				:						Tuple				  =				self.act(__lowercase   )

						lowerCamelCase_				:						Any				  =				self.linear_a(__lowercase   )

						if self.post_act is not None:
							lowerCamelCase_				:						Union[str, Any]				  =				self.post_act(__lowercase   )
						return sample


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						int				  =				num_channels
						lowerCamelCase_				:						List[str]				  =				flip_sin_to_cos
						lowerCamelCase_				:						int				  =				downscale_freq_shift





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				get_timestep_embedding(
						    __lowercase					,			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    ):

					def __init__(							self					,			__lowercase = 256					,			__lowercase = 1.0					,			__lowercase=True					,			__lowercase=True					,			__lowercase=False   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Tuple				  =				nn.Parameter(torch.randn(__lowercase   ) * scale					,			requires_grad=__lowercase   )
						lowerCamelCase_				:						int				  =				log
						lowerCamelCase_				:						List[Any]				  =				flip_sin_to_cos

						if set_W_to_weight:
							# to delete later
							lowerCamelCase_				:						List[str]				  =				nn.Parameter(torch.randn(__lowercase   ) * scale					,			requires_grad=__lowercase   )

							lowerCamelCase_				:						Tuple				  =				self.W





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						if self.log:
							lowerCamelCase_				:						Optional[Any]				  =				torch.log(__lowercase   )

						lowerCamelCase_				:						List[Any]				  =				x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
							lowerCamelCase_				:						List[Any]				  =				torch.cat([torch.cos(__lowercase   ), torch.sin(__lowercase   )]					,			dim=-1   )
						else:
							lowerCamelCase_				:						Any				  =				torch.cat([torch.sin(__lowercase   ), torch.cos(__lowercase   )]					,			dim=-1   )
						return out


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_				:						Optional[Any]				  =				height
						lowerCamelCase_				:						Union[str, Any]				  =				width
						lowerCamelCase_				:						Optional[int]				  =				num_embed
						lowerCamelCase_				:						List[str]				  =				embed_dim

						lowerCamelCase_				:						Any				  =				nn.Embedding(self.num_embed					,			__lowercase   )
						lowerCamelCase_				:						int				  =				nn.Embedding(self.height					,			__lowercase   )
						lowerCamelCase_				:						int				  =				nn.Embedding(self.width					,			__lowercase   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.emb(__lowercase   )

						lowerCamelCase_				:						Dict				  =				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_				:						List[Any]				  =				height_emb.unsqueeze(2   )

						lowerCamelCase_				:						List[str]				  =				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_				:						Optional[Any]				  =				width_emb.unsqueeze(1   )

						lowerCamelCase_				:						List[str]				  =				height_emb + width_emb

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

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

						return emb


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						List[Any]				  =				dropout_prob > 0
						lowerCamelCase_				:						Dict				  =				nn.Embedding(num_classes + use_cfg_embedding					,			__lowercase   )
						lowerCamelCase_				:						Any				  =				num_classes
						lowerCamelCase_				:						Optional[int]				  =				dropout_prob

					def       lowercase_				(							self					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




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





					def       lowercase_				(							self					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
							lowerCamelCase_				:						Optional[int]				  =				self.token_drop(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Any				  =				self.embedding_table(__lowercase   )
						return embeddings


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase = 1_024					,			__lowercase = 768					,			__lowercase = 768					,			__lowercase = 10					,			):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_				:						Dict				  =				num_image_text_embeds
						lowerCamelCase_				:						List[Any]				  =				nn.Linear(__lowercase					,			self.num_image_text_embeds * cross_attention_dim   )
						lowerCamelCase_				:						Dict				  =				nn.Linear(__lowercase					,			__lowercase   )





					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				text_embeds.shape[0]

						# image
						lowerCamelCase_				:						List[str]				  =				self.image_embeds(__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				image_text_embeds.reshape(__lowercase					,			self.num_image_text_embeds					,			-1   )

						# text
						lowerCamelCase_				:						List[str]				  =				self.text_proj(__lowercase   )

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


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase = 768					,			__lowercase = 768					,			__lowercase = 32					,			):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_				:						List[str]				  =				num_image_text_embeds
						lowerCamelCase_				:						Union[str, Any]				  =				nn.Linear(__lowercase					,			self.num_image_text_embeds * cross_attention_dim   )
						lowerCamelCase_				:						Tuple				  =				nn.LayerNorm(__lowercase   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				image_embeds.shape[0]

						# image
						lowerCamelCase_				:						List[Any]				  =				self.image_embeds(__lowercase   )
						lowerCamelCase_				:						Union[str, Any]				  =				image_embeds.reshape(__lowercase					,			self.num_image_text_embeds					,			-1   )
						lowerCamelCase_				:						Union[str, Any]				  =				self.norm(__lowercase   )
						return image_embeds


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase=0.1   ):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_				:						str				  =				Timesteps(num_channels=256					,			flip_sin_to_cos=__lowercase					,			downscale_freq_shift=1   )
						lowerCamelCase_				:						List[str]				  =				TimestepEmbedding(in_channels=256					,			time_embed_dim=__lowercase   )
						lowerCamelCase_				:						List[Any]				  =				LabelEmbedding(__lowercase					,			__lowercase					,			__lowercase   )





					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				self.time_proj(__lowercase   )
						lowerCamelCase_				:						str				  =				self.timestep_embedder(timesteps_proj.to(dtype=__lowercase   )   )  # (N, D)

						lowerCamelCase_				:						Tuple				  =				self.class_embedder(__lowercase   )  # (N, D)

						lowerCamelCase_				:						List[str]				  =				timesteps_emb + class_labels  # (N, D)

						return conditioning


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase = 64   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Optional[int]				  =				nn.LayerNorm(__lowercase   )
						lowerCamelCase_				:						List[Any]				  =				AttentionPooling(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Dict				  =				nn.Linear(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						List[Any]				  =				nn.LayerNorm(__lowercase   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.norma(__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				self.pool(__lowercase   )
						lowerCamelCase_				:						Tuple				  =				self.proj(__lowercase   )
						lowerCamelCase_				:						Any				  =				self.norma(__lowercase   )
						return hidden_states


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase = 768					,			__lowercase = 768					,			__lowercase = 1_536   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Optional[int]				  =				nn.Linear(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Any				  =				nn.LayerNorm(__lowercase   )
						lowerCamelCase_				:						List[Any]				  =				nn.Linear(__lowercase					,			__lowercase   )





					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.text_proj(__lowercase   )
						lowerCamelCase_				:						Dict				  =				self.text_norm(__lowercase   )

						# image
						lowerCamelCase_				:						Tuple				  =				self.image_proj(__lowercase   )

						return time_image_embeds + time_text_embeds


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase = 768					,			__lowercase = 1_536   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Any				  =				nn.Linear(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Union[str, Any]				  =				nn.LayerNorm(__lowercase   )





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				self.image_proj(__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				self.image_norm(__lowercase   )
						return time_image_embeds


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase = 768					,			__lowercase = 1_536   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						str				  =				nn.Linear(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						Any				  =				nn.LayerNorm(__lowercase   )
						lowerCamelCase_				:						str				  =				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       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




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


class 							__SCREAMING_SNAKE_CASE				(						nn.Module    ):

					def __init__(							self					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




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





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_				:						Optional[int]				  =				x.size()

						def shape(__lowercase   ):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							lowerCamelCase_				:						int				  =				x.view(__lowercase					,			-1					,			self.num_heads					,			self.dim_per_head   )
							# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
							lowerCamelCase_				:						List[Any]				  =				x.transpose(1					,			2   )
							# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
							lowerCamelCase_				:						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)
							lowerCamelCase_				:						List[str]				  =				x.transpose(1					,			2   )
							return x

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

						# (bs*n_heads, class_token_length, dim_per_head)
						lowerCamelCase_				:						str				  =				shape(self.q_proj(__lowercase   )   )
						# (bs*n_heads, length+class_token_length, dim_per_head)
						lowerCamelCase_				:						Dict				  =				shape(self.k_proj(__lowercase   )   )
						lowerCamelCase_				:						List[Any]				  =				shape(self.v_proj(__lowercase   )   )

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

						# (bs*n_heads, dim_per_head, class_token_length)
						lowerCamelCase_				:						str				  =				torch.einsum('bts,bcs->bct'					,			__lowercase					,			__lowercase   )

						# (bs, length+1, width)
						lowerCamelCase_				:						Optional[int]				  =				a.reshape(__lowercase					,			-1					,			1   ).transpose(1					,			2   )

						return a[:, 0, :]  # cls_token

'''simple docstring'''





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







     """simple docstring"""
     a							,			a					:							int							= [], []
     for title, text in zip(documents['title']				,	documents['text']  ):
          if text is not None:
               for passage in split_text(snake_case  ):
                    titles.append(title if title is not None else ''  )
                    texts.append(snake_case  )
     return {"title": titles, "text": texts}






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= load_dataset(
         'csv'				,	data_files=[csv_path]				,	split='train'				,	delimiter='\t'				,	column_names=['title', 'text']  )
     a					:							Union[str, Any]							= kb_dataset.map(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

     a					:							Any							= DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
     a					:							List[str]							= ctx_encoder.to(device=snake_case  )

     def embed(
         snake_case       :				dict				,	snake_case       :				DPRContextEncoder				,	snake_case       :				DPRContextEncoderTokenizerFast				,	snake_case       :				Any  ) -> dict:
          a					:							Tuple							= ctx_tokenizer(
              documents['title']				,	documents['text']				,	truncation=snake_case				,	padding='longest'				,	return_tensors='pt'  )['input_ids']
          a					:							Any							= ctx_encoder(input_ids.to(device=snake_case  )				,	return_dict=snake_case  ).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     a					:							Union[str, Any]							= Features(
         {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space

     a					:							List[str]							= data_shrad.map(
         partial(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

     for shard_address in glob(str(snake_case  ) + '/*/'  ):
          data_shard_list.append(load_from_disk(snake_case  )  )

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

     a					:							List[str]							= faiss.IndexHNSWFlat(768				,	128				,	faiss.METRIC_INNER_PRODUCT  )
     concat.add_faiss_index('embeddings'				,	custom_index=snake_case  )
     concat.get_index('embeddings'  ).save(
         snake_case  )  # since we load the index in to memory,we can directly update the index in the disk

