






'''simple docstring'''
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Dict = False			,							lowerCAmelCase__	:  Any = 1			,							lowerCAmelCase__	:  Optional[Any] = 1			,							lowerCAmelCase__	:  Tuple = 10000			,							):

			"""simple docstring"""





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

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

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

			# scale embeddings
			__UpperCAmelCase   :      str										=     scale * emb

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

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

			# zero pad
			if embedding_dim % 2 == 1:
						__UpperCAmelCase   :      Union[str, Any]										=     torch.nn.functional.pad(lowerCAmelCase__			,							(0, 1, 0, 0) )
			return emb


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any]			,							lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Optional[int]=False			,							lowerCAmelCase__	:  Union[str, Any]=0 ):

			"""simple docstring"""





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

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


def   lowercase_	(   lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Optional[int] ):

			"""simple docstring"""





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

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

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


def   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Dict ):

			"""simple docstring"""





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

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

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

			__UpperCAmelCase   :      int										=     np.sin(lowerCAmelCase__ )  # (M, D/2)
			__UpperCAmelCase   :      Optional[Any]										=     np.cos(lowerCAmelCase__ )  # (M, D/2)

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



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase=224 ,       __UpperCAmelCase=224 ,       __UpperCAmelCase=16 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=True ,       __UpperCAmelCase=True ,       )       ->     int:

					'''simple docstring'''
					super().__init__()

					__UpperCAmelCase   :      Optional[int]										=     (height // patch_size) * (width // patch_size)
					__UpperCAmelCase   :      int										=     flatten
					__UpperCAmelCase   :      Optional[int]										=     layer_norm

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

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





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.proj(__UpperCAmelCase				)
					if self.flatten:
								__UpperCAmelCase   :      int										=     latent.flatten(2				).transpose(1 ,       2				)  # BCHW -> BNC
					if self.layer_norm:
								__UpperCAmelCase   :      Union[str, Any]										=     self.norm(__UpperCAmelCase				)
					return latent + self.pos_embed



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase = "silu" ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase=None ,       )       ->     Optional[Any]:

					'''simple docstring'''
					super().__init__()

					__UpperCAmelCase   :      Dict										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)

					if cond_proj_dim is not None:
								__UpperCAmelCase   :      Optional[int]										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase ,       bias=__UpperCAmelCase				)
					else:
								__UpperCAmelCase   :      str										=     None

					__UpperCAmelCase   :      Optional[int]										=     get_activation(__UpperCAmelCase				)

					if out_dim is not None:
								__UpperCAmelCase   :      Optional[Any]										=     out_dim
					else:
								__UpperCAmelCase   :      Optional[int]										=     time_embed_dim
					__UpperCAmelCase   :      Dict										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)

					if post_act_fn is None:
								__UpperCAmelCase   :      Dict										=     None
					else:
								__UpperCAmelCase   :      List[str]										=     get_activation(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     Tuple:

					'''simple docstring'''






					if condition is not None:
								__UpperCAmelCase   :      Dict										=     sample + self.cond_proj(__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     self.linear_a(__UpperCAmelCase				)

					if self.act is not None:
								__UpperCAmelCase   :      Optional[int]										=     self.act(__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[int]										=     self.linear_a(__UpperCAmelCase				)

					if self.post_act is not None:
								__UpperCAmelCase   :      int										=     self.post_act(__UpperCAmelCase				)
					return sample



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     int:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      List[Any]										=     num_channels
					__UpperCAmelCase   :      List[str]										=     flip_sin_to_cos
					__UpperCAmelCase   :      List[str]										=     downscale_freq_shift





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     get_timestep_embedding(
					    __UpperCAmelCase ,       self.num_channels ,       flip_sin_to_cos=self.flip_sin_to_cos ,       downscale_freq_shift=self.downscale_freq_shift ,       )
					return t_emb



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase = 256 ,       __UpperCAmelCase = 1.0 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=True ,       __UpperCAmelCase=False				)       ->     Optional[int]:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      int										=     nn.Parameter(torch.randn(__UpperCAmelCase				) * scale ,       requires_grad=__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     log
					__UpperCAmelCase   :      Any										=     flip_sin_to_cos

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

								__UpperCAmelCase   :      Optional[int]										=     self.W





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Optional[Any]:

					'''simple docstring'''






					if self.log:
								__UpperCAmelCase   :      Tuple										=     torch.log(__UpperCAmelCase				)

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

					if self.flip_sin_to_cos:
								__UpperCAmelCase   :      Union[str, Any]										=     torch.cat([torch.cos(__UpperCAmelCase				), torch.sin(__UpperCAmelCase				)] ,       dim=-1				)
					else:
								__UpperCAmelCase   :      Dict										=     torch.cat([torch.sin(__UpperCAmelCase				), torch.cos(__UpperCAmelCase				)] ,       dim=-1				)
					return out



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       )       ->     List[str]:

					'''simple docstring'''
					super().__init__()

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

					__UpperCAmelCase   :      List[Any]										=     nn.Embedding(self.num_embed ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     nn.Embedding(self.height ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     nn.Embedding(self.width ,       __UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.emb(__UpperCAmelCase				)

					__UpperCAmelCase   :      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
					__UpperCAmelCase   :      Any										=     height_emb.unsqueeze(2				)

					__UpperCAmelCase   :      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
					__UpperCAmelCase   :      str										=     width_emb.unsqueeze(1				)

					__UpperCAmelCase   :      Optional[int]										=     height_emb + width_emb

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

					__UpperCAmelCase   :      Union[str, Any]										=     emb + pos_emb[:, : emb.shape[1], :]

					return emb



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      List[str]										=     dropout_prob > 0
					__UpperCAmelCase   :      Dict										=     nn.Embedding(num_classes + use_cfg_embedding ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     num_classes
					__UpperCAmelCase   :      List[Any]										=     dropout_prob





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     Dict:

					'''simple docstring'''






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





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
								__UpperCAmelCase   :      str										=     self.token_drop(__UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     self.embedding_table(__UpperCAmelCase				)
					return embeddings



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase = 1_024 ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 10 ,       )       ->     Optional[int]:

					'''simple docstring'''
					super().__init__()

					__UpperCAmelCase   :      Tuple										=     num_image_text_embeds
					__UpperCAmelCase   :      List[str]										=     nn.Linear(__UpperCAmelCase ,       self.num_image_text_embeds * cross_attention_dim				)
					__UpperCAmelCase   :      str										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     text_embeds.shape[0]

					# image
					__UpperCAmelCase   :      Dict										=     self.image_embeds(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     image_text_embeds.reshape(__UpperCAmelCase ,       self.num_image_text_embeds ,       -1				)

					# text
					__UpperCAmelCase   :      Union[str, Any]										=     self.text_proj(__UpperCAmelCase				)

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



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 32 ,       )       ->     Dict:

					'''simple docstring'''
					super().__init__()

					__UpperCAmelCase   :      Optional[Any]										=     num_image_text_embeds
					__UpperCAmelCase   :      Union[str, Any]										=     nn.Linear(__UpperCAmelCase ,       self.num_image_text_embeds * cross_attention_dim				)
					__UpperCAmelCase   :      List[Any]										=     nn.LayerNorm(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     image_embeds.shape[0]

					# image
					__UpperCAmelCase   :      int										=     self.image_embeds(__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     image_embeds.reshape(__UpperCAmelCase ,       self.num_image_text_embeds ,       -1				)
					__UpperCAmelCase   :      List[str]										=     self.norm(__UpperCAmelCase				)
					return image_embeds



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=0.1				)       ->     int:

					'''simple docstring'''
					super().__init__()

					__UpperCAmelCase   :      str										=     Timesteps(num_channels=256 ,       flip_sin_to_cos=__UpperCAmelCase ,       downscale_freq_shift=1				)
					__UpperCAmelCase   :      List[str]										=     TimestepEmbedding(in_channels=256 ,       time_embed_dim=__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     LabelEmbedding(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     self.time_proj(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     self.timestep_embedder(timesteps_proj.to(dtype=__UpperCAmelCase				)				)  # (N, D)

					__UpperCAmelCase   :      List[Any]										=     self.class_embedder(__UpperCAmelCase				)  # (N, D)

					__UpperCAmelCase   :      Tuple										=     timesteps_emb + class_labels  # (N, D)

					return conditioning



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase = 64				)       ->     str:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      Union[str, Any]										=     nn.LayerNorm(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[Any]										=     AttentionPooling(__UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      List[Any]										=     nn.LayerNorm(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     self.norma(__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     self.pool(__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     self.proj(__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     self.norma(__UpperCAmelCase				)
					return hidden_states



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 1_536				)       ->     Union[str, Any]:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      Dict										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     nn.LayerNorm(__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.text_proj(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     self.text_norm(__UpperCAmelCase				)

					# image
					__UpperCAmelCase   :      Union[str, Any]										=     self.image_proj(__UpperCAmelCase				)

					return time_image_embeds + time_text_embeds



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 1_536				)       ->     List[str]:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      Tuple										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[int]										=     nn.LayerNorm(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.image_proj(__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     self.image_norm(__UpperCAmelCase				)
					return time_image_embeds



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase = 768 ,       __UpperCAmelCase = 1_536				)       ->     str:

					'''simple docstring'''
					super().__init__()
					__UpperCAmelCase   :      List[str]										=     nn.Linear(__UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Union[str, Any]										=     nn.LayerNorm(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[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 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.image_proj(__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     self.image_norm(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[Any]										=     self.input_hint_block(__UpperCAmelCase				)
					return time_image_embeds, hint



class _A  (    nn.Module    ):





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=None				)       ->     Optional[Any]:

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





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      int										=     x.size()

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

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

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

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

					# (bs*n_heads, dim_per_head, class_token_length)
					__UpperCAmelCase   :      List[str]										=     torch.einsum("""bts,bcs->bct""" ,       __UpperCAmelCase ,       __UpperCAmelCase				)

					# (bs, length+1, width)
					__UpperCAmelCase   :      Union[str, Any]										=     a.reshape(__UpperCAmelCase ,       -1 ,       1				).transpose(1 ,       2				)

					return a[:, 0, :]  # cls_token









'''simple docstring'''
import torch

from transformers import AutoTokenizer



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     AutoTokenizer.from_pretrained(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      Dict										=     None
								for l in x:
											__UpperCAmelCase   :      Dict										=     self.tokenizer(
											    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )
											__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor([len(__UpperCAmelCase				)]				)
											if d is not None:
														for k in d.keys():
																	__UpperCAmelCase   :      Dict										=     torch.cat((d[k], t[k]) ,       0				)
											else:
														__UpperCAmelCase   :      List[str]										=     t

								__UpperCAmelCase   :      Union[str, Any]										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""				)				)
								__UpperCAmelCase   :      Any										=     torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""				)				)

					elif isinstance(__UpperCAmelCase ,       __UpperCAmelCase				) and all(isinstance(_x ,       __UpperCAmelCase				) for _x in x				):
								__UpperCAmelCase   :      int										=     self.tokenizer(
								    __UpperCAmelCase ,       padding="""max_length""" ,       max_length=384 ,       truncation=__UpperCAmelCase ,       return_tensors="""pt""" ,       )

					else:
								raise Exception(
								    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
								    """ strings` for supports are supported."""				)

					return d





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase=0.70				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     []
					for idx in range(len(W_query["""input_ids"""]				)				):
								__UpperCAmelCase   :      Optional[int]										=     range(p_start.shape[1]				)

								__UpperCAmelCase   :      Union[str, Any]										=     []
								for start_id in start_indexes:
											for end_id in end_indexes:
														if start_id < end_id:
																	output.append(
																	    (
																	        start_id,
																	        end_id,
																	        p_start[idx][start_id].item(),
																	        p_end[idx][end_id].item(),
																	    )				)

								output.sort(key=lambda __UpperCAmelCase				: (tup[2] * tup[3]) ,       reverse=__UpperCAmelCase				)
								__UpperCAmelCase   :      Optional[Any]										=     []
								for k in range(len(__UpperCAmelCase				)				):
											if output[k][2] * output[k][3] >= thresh:
														__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     output[k][0], output[k][1]
														__UpperCAmelCase   :      int										=     self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]				)
														temp.append((decoded, output[k][2] * output[k][3])				)

								final_outputs.append(__UpperCAmelCase				)

					return final_outputs




