




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


import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
       from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
       _UpperCamelCase:					List[Any]     =None

if is_ray_available():
       import ray  # noqa: E402 # isort:skip
       from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
       _UpperCamelCase:					List[str]     =None
       _UpperCamelCase:					Tuple     =None
       _UpperCamelCase:					Any     =None



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]    ):






  """simple docstring"""




  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    _lowerCAmelCase						=		unittest.skip('test requires Datasets, Faiss, psutil'    )(__SCREAMING_SNAKE_CASE    )
  return test_case





@require_distributed_retrieval
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[Any]:
         _lowerCAmelCase						=		tempfile.mkdtemp()
         _lowerCAmelCase						=		8

         # DPR tok
         _lowerCAmelCase						=		[
             '[UNK]',
             '[CLS]',
             '[SEP]',
             '[PAD]',
             '[MASK]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      'dpr_tokenizer'       )
         os.makedirs(_lowerCAmelCase  ,      exist_ok=_lowerCAmelCase       )
         _lowerCAmelCase						=		os.path.join(_lowerCAmelCase  ,      DPR_VOCAB_FILES_NAMES['vocab_file']       )
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'       ) as vocab_writer:
           vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

         # BART tok
         _lowerCAmelCase						=		[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         _lowerCAmelCase						=		{'unk_token': '<unk>'}

         _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      'bart_tokenizer'       )
         os.makedirs(_lowerCAmelCase  ,      exist_ok=_lowerCAmelCase       )
         _lowerCAmelCase						=		os.path.join(_lowerCAmelCase  ,      BART_VOCAB_FILES_NAMES['vocab_file']       )
         _lowerCAmelCase						=		os.path.join(_lowerCAmelCase  ,      BART_VOCAB_FILES_NAMES['merges_file']       )
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'       ) as fp:
           fp.write(json.dumps(_lowerCAmelCase       ) + '\n'       )
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'       ) as fp:
           fp.write('\n'.join(_lowerCAmelCase       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   DPRQuestionEncoderTokenizer:
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,      'dpr_tokenizer'       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   BartTokenizer:
         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,      'bart_tokenizer'       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   str:
         _lowerCAmelCase						=		Dataset.from_dict(
             {
                 'id': ['0', '1'],
                 'text': ['foo', 'bar'],
                 'title': ['Foo', 'Bar'],
                 'embeddings': [np.ones(self.retrieval_vector_size       ), 2 * np.ones(self.retrieval_vector_size       )],
             }       )
         dataset.add_faiss_index('embeddings'  ,      string_factory='Flat'  ,      metric_type=faiss.METRIC_INNER_PRODUCT       )
         return dataset



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			Tuple=1_2345       )					->   RagPyTorchDistributedRetriever:
         _lowerCAmelCase						=		self.get_dummy_dataset()
         _lowerCAmelCase						=		RagConfig(
             retrieval_vector_size=self.retrieval_vector_size  ,      question_encoder=DPRConfig().to_dict()  ,      generator=BartConfig().to_dict()  ,      )
         with patch('transformers.models.rag.retrieval_rag.load_dataset'       ) as mock_load_dataset:
           _lowerCAmelCase						=		dataset
           _lowerCAmelCase						=		RagPyTorchDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      )
           if init_retrieval:
             retriever.init_retrieval(_lowerCAmelCase       )
         return retriever



       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			bool       )					->   RagRayDistributedRetriever:
         # Have to run in local mode because sys.path modifications at top of
         # file are not propogated to remote workers.
         # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
         ray.init(local_mode=_lowerCAmelCase       )
         _lowerCAmelCase						=		RagConfig(
             retrieval_vector_size=self.retrieval_vector_size  ,      question_encoder=DPRConfig().to_dict()  ,      generator=BartConfig().to_dict()  ,      )
         _lowerCAmelCase						=		ray.remote(_lowerCAmelCase       )
         _lowerCAmelCase						=		[remote_cls.remote() for _ in range(1       )]
         with patch('transformers.models.rag.retrieval_rag.load_dataset'       ) as mock_load_dataset:
           _lowerCAmelCase						=		self.get_dummy_dataset()
           _lowerCAmelCase						=		RagRayDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      retrieval_workers=_lowerCAmelCase  ,      )
           if init_retrieval:
             retriever.init_retrieval()
         return retriever



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			List[Any]=1_2345       )					->   List[Any]:
         _lowerCAmelCase						=		self.get_dummy_dataset()
         _lowerCAmelCase						=		RagConfig(
             retrieval_vector_size=self.retrieval_vector_size  ,      question_encoder=DPRConfig().to_dict()  ,      generator=BartConfig().to_dict()  ,      index_name='custom'  ,      )
         if from_disk:
           _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      'dataset'       )
           _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      'index.faiss'       )
           dataset.get_index('embeddings'       ).save(os.path.join(self.tmpdirname  ,      'index.faiss'       )       )
           dataset.drop_index('embeddings'       )
           dataset.save_to_disk(os.path.join(self.tmpdirname  ,      'dataset'       )       )
           del dataset
           _lowerCAmelCase						=		RagPyTorchDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      )
         else:
           _lowerCAmelCase						=		RagPyTorchDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      index=CustomHFIndex(config.retrieval_vector_size  ,      _lowerCAmelCase       )  ,      )
         if init_retrieval:
           retriever.init_retrieval(_lowerCAmelCase       )
         return retriever



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			bool       )					->   int:
         # Have to run in local mode because sys.path modifications at top of
         # file are not propogated to remote workers.
         # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
         ray.init(local_mode=_lowerCAmelCase       )
         _lowerCAmelCase						=		self.get_dummy_dataset()
         _lowerCAmelCase						=		RagConfig(
             retrieval_vector_size=self.retrieval_vector_size  ,      question_encoder=DPRConfig().to_dict()  ,      generator=BartConfig().to_dict()  ,      index_name='custom'  ,      )
         _lowerCAmelCase						=		ray.remote(_lowerCAmelCase       )
         _lowerCAmelCase						=		[remote_cls.remote() for _ in range(1       )]
         if from_disk:
           _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      'dataset'       )
           _lowerCAmelCase						=		os.path.join(self.tmpdirname  ,      'index.faiss'       )
           dataset.get_index('embeddings'       ).save(os.path.join(self.tmpdirname  ,      'index.faiss'       )       )
           dataset.drop_index('embeddings'       )
           dataset.save_to_disk(os.path.join(self.tmpdirname  ,      'dataset'       )       )
           del dataset
           _lowerCAmelCase						=		RagRayDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      retrieval_workers=_lowerCAmelCase  ,      index=CustomHFIndex.load_from_disk(
                   vector_size=config.retrieval_vector_size  ,      dataset_path=config.passages_path  ,      index_path=config.index_path  ,      )  ,      )
         else:
           _lowerCAmelCase						=		RagRayDistributedRetriever(
               _lowerCAmelCase  ,      question_encoder_tokenizer=self.get_dpr_tokenizer()  ,      generator_tokenizer=self.get_bart_tokenizer()  ,      retrieval_workers=_lowerCAmelCase  ,      index=CustomHFIndex(config.retrieval_vector_size  ,      _lowerCAmelCase       )  ,      )
         if init_retrieval:
           retriever.init_retrieval()
         return retriever



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			RagRetriever  ,      _lowerCAmelCase					:			np.array  ,      _lowerCAmelCase					:			int       )					->   None:
         _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		retriever.retrieve(_lowerCAmelCase  ,      n_docs=_lowerCAmelCase       )
         self.assertEqual(retrieved_doc_embeds.shape  ,      (2, n_docs, self.retrieval_vector_size)       )
         self.assertEqual(len(_lowerCAmelCase       )  ,      2       )
         self.assertEqual(sorted(doc_dicts[0]       )  ,      ['embeddings', 'id', 'text', 'title']       )
         self.assertEqual(len(doc_dicts[0]['id']       )  ,      _lowerCAmelCase       )
         self.assertEqual(doc_dicts[0]['id'][0]  ,      '1'       )  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]['id'][0]  ,      '0'       )  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()  ,      [[1], [0]]       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Optional[int]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]  ,      dtype=np.floataa       )

         self.distributed_retriever_check(
             self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Any:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]  ,      dtype=np.floataa       )

         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[Any]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]  ,      dtype=np.floataa       )

         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      )



       @require_ray
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]  ,      dtype=np.floataa       )

         self.distributed_retriever_check(
             self.get_dummy_ray_distributed_retriever(init_retrieval=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         ray.shutdown()



       @require_ray
       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[Any]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]  ,      dtype=np.floataa       )
         with self.assertRaises(_lowerCAmelCase       ):
           self.distributed_retriever_check(
               self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase  ,      )
         ray.shutdown()






       @require_ray
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   List[Any]:
         _lowerCAmelCase						=		1
         _lowerCAmelCase						=		np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]  ,      dtype=np.floataa       )

         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowerCAmelCase  ,      from_disk=_lowerCAmelCase       )  ,      _lowerCAmelCase  ,      _lowerCAmelCase       )
         ray.shutdown()
