
import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


__lowerCAmelCase				      =					logging.get_logger(__name__)

__lowerCAmelCase				      =					[
    'yjernite/retribert-base-uncased',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]





class 						lowerCamelCase			(					__lowerCamelCase				):
	UpperCamelCase_     :				Any								=  RetriBertConfig
	UpperCamelCase_     :				str								=  None
	UpperCamelCase_     :				List[Any]								=  'retribert'







	def    snake_case__    (    self			:List[str]      ,    lowercase			:str )		->			int:







				"""simple docstring"""






				if isinstance(lowercase      ,    nn.Linear ):
							module.weight.data.normal_(mean=0.0      ,    std=self.config.initializer_range )
							if module.bias is not None:
										module.bias.data.zero_()
				elif isinstance(lowercase      ,    nn.Embedding ):
							module.weight.data.normal_(mean=0.0      ,    std=self.config.initializer_range )
							if module.padding_idx is not None:
										module.weight.data[module.padding_idx].zero_()
				elif isinstance(lowercase      ,    nn.LayerNorm ):
							module.bias.data.zero_()
							module.weight.data.fill_(1.0 )



__lowerCAmelCase				      =					R'\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'





@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'   ,		__lowerCamelCase   ,		)
class 						lowerCamelCase			(					__lowerCamelCase				):







	def __init__(    self			:Dict      ,    lowercase			:RetriBertConfig )		->			None:







				"""simple docstring"""






				super().__init__(lowercase )
				SCREAMING_SNAKE_CASE    	=   config.projection_dim

				SCREAMING_SNAKE_CASE    	=   BertModel(lowercase )
				SCREAMING_SNAKE_CASE    	=   None if config.share_encoders else BertModel(lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Dropout(config.hidden_dropout_prob )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(config.hidden_size      ,    config.projection_dim      ,    bias=lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(config.hidden_size      ,    config.projection_dim      ,    bias=lowercase )

				SCREAMING_SNAKE_CASE    	=   nn.CrossEntropyLoss(reduction='''mean''' )

				# Initialize weights and apply final processing
				self.post_init()







	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Any      ,    lowercase			:Optional[Any]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]=-1      ,    )		->			Optional[Any]:







				"""simple docstring"""






				if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
							return sent_encoder(lowercase      ,    attention_mask=lowercase )[1]
				else:
							# prepare implicit variables
							SCREAMING_SNAKE_CASE    	=   input_ids.device
							SCREAMING_SNAKE_CASE    	=   input_ids.size()
							SCREAMING_SNAKE_CASE    	=   torch.zeros(lowercase      ,    dtype=torch.long      ,    device=lowercase )
							SCREAMING_SNAKE_CASE    	=   [None] * sent_encoder.config.num_hidden_layers
							SCREAMING_SNAKE_CASE    	=   sent_encoder.get_extended_attention_mask(
							    lowercase      ,    lowercase )

							# define function for checkpointing
							def partial_encode(*lowercase			:Optional[int] ):
										SCREAMING_SNAKE_CASE    	=   sent_encoder.encoder(
										    inputs[0]      ,    attention_mask=inputs[1]      ,    head_mask=lowercase      ,    )
										SCREAMING_SNAKE_CASE    	=   encoder_outputs[0]
										SCREAMING_SNAKE_CASE    	=   sent_encoder.pooler(lowercase )
										return pooled_output

							# run embedding layer on everything at once
							SCREAMING_SNAKE_CASE    	=   sent_encoder.embeddings(
							    input_ids=lowercase      ,    position_ids=lowercase      ,    token_type_ids=lowercase      ,    inputs_embeds=lowercase )
							# run encoding and pooling on one mini-batch at a time
							SCREAMING_SNAKE_CASE    	=   []
							for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size ) ):
										SCREAMING_SNAKE_CASE    	=   embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
										SCREAMING_SNAKE_CASE    	=   extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
										SCREAMING_SNAKE_CASE    	=   checkpoint.checkpoint(lowercase      ,    lowercase      ,    lowercase )
										pooled_output_list.append(lowercase )
							return torch.cat(lowercase      ,    dim=0 )







	def    snake_case__    (    self			:Tuple      ,    lowercase			:Union[str, Any]      ,    lowercase			:Dict=None      ,    lowercase			:Tuple=-1      ,    )		->			Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.embed_sentences_checkpointed(
				    lowercase      ,    lowercase      ,    self.bert_query      ,    lowercase      ,    )
				return self.project_query(lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int]      ,    lowercase			:int=None      ,    lowercase			:Any=-1      ,    )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.embed_sentences_checkpointed(
				    lowercase      ,    lowercase      ,    self.bert_query if self.bert_doc is None else self.bert_doc      ,    lowercase      ,    )
				return self.project_doc(lowercase )







	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:torch.LongTensor      ,    lowercase			:Optional[torch.FloatTensor]      ,    lowercase			:torch.LongTensor      ,    lowercase			:Optional[torch.FloatTensor]      ,    lowercase			:int = -1      ,    )		->			torch.FloatTensor:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   input_ids_query.device
				SCREAMING_SNAKE_CASE    	=   self.embed_questions(lowercase      ,    lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   self.embed_answers(lowercase      ,    lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   torch.mm(lowercase      ,    a_reps.t() )
				SCREAMING_SNAKE_CASE    	=   self.ce_loss(lowercase      ,    torch.arange(compare_scores.shape[1] ).to(lowercase ) )
				SCREAMING_SNAKE_CASE    	=   self.ce_loss(compare_scores.t()      ,    torch.arange(compare_scores.shape[0] ).to(lowercase ) )
				SCREAMING_SNAKE_CASE    	=   (loss_qa + loss_aq) / 2
				return loss

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def       a			(   a		,	a		,	a = False		,	a = 1		,	a = 1		,	a = 1_0000		,	)    ->Dict:




			'''simple docstring'''


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

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

			SCREAMING_SNAKE_CASE    	=   torch.exp(a  )
			SCREAMING_SNAKE_CASE    	=   timesteps[:, None].float() * emb[None, :]

			# scale embeddings
			SCREAMING_SNAKE_CASE    	=   scale * emb

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

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

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


def       a			(   a		,	a		,	a=False		,	a=0  )    ->Union[str, Any]:




			'''simple docstring'''


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

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


def       a			(   a		,	a  )    ->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
			SCREAMING_SNAKE_CASE    	=   get_ad_sincos_pos_embed_from_grid(embed_dim // 2		,	grid[0]  )  # (H*W, D/2)
			SCREAMING_SNAKE_CASE    	=   get_ad_sincos_pos_embed_from_grid(embed_dim // 2		,	grid[1]  )  # (H*W, D/2)

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


def       a			(   a		,	a  )    ->List[Any]:




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE    	=   np.arange(embed_dim // 2		,	dtype=np.floataa  )
			omega /= embed_dim / 2.0
			SCREAMING_SNAKE_CASE    	=   1.0 / 1_0000**omega  # (D/2,)

			SCREAMING_SNAKE_CASE    	=   pos.reshape(-1  )  # (M,)
			SCREAMING_SNAKE_CASE    	=   np.einsum('''m,d->md'''		,	a		,	a  )  # (M, D/2), outer product

			SCREAMING_SNAKE_CASE    	=   np.sin(a  )  # (M, D/2)
			SCREAMING_SNAKE_CASE    	=   np.cos(a  )  # (M, D/2)

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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:str      ,    lowercase			:int=2_2_4      ,    lowercase			:Dict=2_2_4      ,    lowercase			:List[Any]=1_6      ,    lowercase			:str=3      ,    lowercase			:List[str]=7_6_8      ,    lowercase			:Tuple=False      ,    lowercase			:Optional[int]=True      ,    lowercase			:str=True      ,    )		->			int:







				"""simple docstring"""






				super().__init__()

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

				SCREAMING_SNAKE_CASE    	=   nn.Convad(
				    lowercase      ,    lowercase      ,    kernel_size=(patch_size, patch_size)      ,    stride=lowercase      ,    bias=lowercase )
				if layer_norm:
							SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase      ,    elementwise_affine=lowercase      ,    eps=1e-6 )
				else:
							SCREAMING_SNAKE_CASE    	=   None

				SCREAMING_SNAKE_CASE    	=   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    snake_case__    (    self			:Optional[int]      ,    lowercase			:str )		->			Union[str, Any]:







				"""simple docstring"""






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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:str = "silu"      ,    lowercase			:int = None      ,    lowercase			:Optional[str] = None      ,    lowercase			:int=None      ,    )		->			Optional[Any]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )

				if cond_proj_dim is not None:
							SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase      ,    bias=lowercase )
				else:
							SCREAMING_SNAKE_CASE    	=   None

				SCREAMING_SNAKE_CASE    	=   get_activation(lowercase )

				if out_dim is not None:
							SCREAMING_SNAKE_CASE    	=   out_dim
				else:
							SCREAMING_SNAKE_CASE    	=   time_embed_dim
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )

				if post_act_fn is None:
							SCREAMING_SNAKE_CASE    	=   None
				else:
							SCREAMING_SNAKE_CASE    	=   get_activation(lowercase )







	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]=None )		->			Tuple:







				"""simple docstring"""






				if condition is not None:
							SCREAMING_SNAKE_CASE    	=   sample + self.cond_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.linear_a(lowercase )

				if self.act is not None:
							SCREAMING_SNAKE_CASE    	=   self.act(lowercase )

				SCREAMING_SNAKE_CASE    	=   self.linear_a(lowercase )

				if self.post_act is not None:
							SCREAMING_SNAKE_CASE    	=   self.post_act(lowercase )
				return sample





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:int      ,    lowercase			:int      ,    lowercase			:bool      ,    lowercase			:float )		->			int:







				"""simple docstring"""






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







	def    snake_case__    (    self			:str      ,    lowercase			:str )		->			List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   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 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[str]      ,    lowercase			:int = 2_5_6      ,    lowercase			:float = 1.0      ,    lowercase			:int=True      ,    lowercase			:Any=True      ,    lowercase			:List[Any]=False )		->			Optional[int]:







				"""simple docstring"""






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

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

							SCREAMING_SNAKE_CASE    	=   self.W







	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Optional[Any] )		->			Optional[Any]:







				"""simple docstring"""






				if self.log:
							SCREAMING_SNAKE_CASE    	=   torch.log(lowercase )

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

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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Optional[int]      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int      ,    )		->			List[str]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   height
				SCREAMING_SNAKE_CASE    	=   width
				SCREAMING_SNAKE_CASE    	=   num_embed
				SCREAMING_SNAKE_CASE    	=   embed_dim

				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.num_embed      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.height      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.width      ,    lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Tuple )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.emb(lowercase )

				SCREAMING_SNAKE_CASE    	=   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
				SCREAMING_SNAKE_CASE    	=   height_emb.unsqueeze(2 )

				SCREAMING_SNAKE_CASE    	=   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
				SCREAMING_SNAKE_CASE    	=   width_emb.unsqueeze(1 )

				SCREAMING_SNAKE_CASE    	=   height_emb + width_emb

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

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

				return emb





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Optional[Any]      ,    lowercase			:Tuple      ,    lowercase			:int      ,    lowercase			:Union[str, Any] )		->			Union[str, Any]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   dropout_prob > 0
				SCREAMING_SNAKE_CASE    	=   nn.Embedding(num_classes + use_cfg_embedding      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   num_classes
				SCREAMING_SNAKE_CASE    	=   dropout_prob







	def    snake_case__    (    self			:int      ,    lowercase			:Tuple      ,    lowercase			:List[str]=None )		->			Dict:







				"""simple docstring"""






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







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:torch.LongTensor      ,    lowercase			:Tuple=None )		->			Dict:







				"""simple docstring"""






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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Any      ,    lowercase			:int = 1_0_2_4      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_0      ,    )		->			Optional[int]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   num_image_text_embeds
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    self.num_image_text_embeds * cross_attention_dim )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )







	def    snake_case__    (    self			:Tuple      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   text_embeds.shape[0]

				# image
				SCREAMING_SNAKE_CASE    	=   self.image_embeds(lowercase )
				SCREAMING_SNAKE_CASE    	=   image_text_embeds.reshape(lowercase      ,    self.num_image_text_embeds      ,    -1 )

				# text
				SCREAMING_SNAKE_CASE    	=   self.text_proj(lowercase )

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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Optional[int]      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 3_2      ,    )		->			Dict:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   num_image_text_embeds
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    self.num_image_text_embeds * cross_attention_dim )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )







	def    snake_case__    (    self			:List[str]      ,    lowercase			:torch.FloatTensor )		->			Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   image_embeds.shape[0]

				# image
				SCREAMING_SNAKE_CASE    	=   self.image_embeds(lowercase )
				SCREAMING_SNAKE_CASE    	=   image_embeds.reshape(lowercase      ,    self.num_image_text_embeds      ,    -1 )
				SCREAMING_SNAKE_CASE    	=   self.norm(lowercase )
				return image_embeds





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[str]      ,    lowercase			:int      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0.1 )		->			int:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   Timesteps(num_channels=2_5_6      ,    flip_sin_to_cos=lowercase      ,    downscale_freq_shift=1 )
				SCREAMING_SNAKE_CASE    	=   TimestepEmbedding(in_channels=2_5_6      ,    time_embed_dim=lowercase )
				SCREAMING_SNAKE_CASE    	=   LabelEmbedding(lowercase      ,    lowercase      ,    lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int]      ,    lowercase			:int      ,    lowercase			:Optional[int]=None )		->			List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.time_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.timestep_embedder(timesteps_proj.to(dtype=lowercase ) )  # (N, D)

				SCREAMING_SNAKE_CASE    	=   self.class_embedder(lowercase )  # (N, D)

				SCREAMING_SNAKE_CASE    	=   timesteps_emb + class_labels  # (N, D)

				return conditioning





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:str      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int = 6_4 )		->			str:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
				SCREAMING_SNAKE_CASE    	=   AttentionPooling(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int] )		->			Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.norma(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.pool(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.norma(lowercase )
				return hidden_states





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Tuple      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			Union[str, Any]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )







	def    snake_case__    (    self			:str      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.text_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.text_norm(lowercase )

				# image
				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )

				return time_image_embeds + time_text_embeds





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:int      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			List[str]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )







	def    snake_case__    (    self			:Any      ,    lowercase			:torch.FloatTensor )		->			Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.image_norm(lowercase )
				return time_image_embeds





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[str]      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			str:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Sequential(
				    nn.Convad(3      ,    1_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(1_6      ,    1_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(1_6      ,    3_2      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(3_2      ,    3_2      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(3_2      ,    9_6      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(9_6      ,    9_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(9_6      ,    2_5_6      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(2_5_6      ,    4      ,    3      ,    padding=1 )      ,    )







	def    snake_case__    (    self			:Any      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.image_norm(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.input_hint_block(lowercase )
				return time_image_embeds, hint





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[Any]      ,    lowercase			:Any      ,    lowercase			:Union[str, Any]      ,    lowercase			:int=None )		->			Optional[Any]:







				"""simple docstring"""






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







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int] )		->			List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   x.size()

				def shape(lowercase			:List[Any] ):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							SCREAMING_SNAKE_CASE    	=   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)
							SCREAMING_SNAKE_CASE    	=   x.transpose(1      ,    2 )
							# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
							SCREAMING_SNAKE_CASE    	=   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)
							SCREAMING_SNAKE_CASE    	=   x.transpose(1      ,    2 )
							return x

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

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

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

				# (bs*n_heads, dim_per_head, class_token_length)
				SCREAMING_SNAKE_CASE    	=   torch.einsum('''bts,bcs->bct'''      ,    lowercase      ,    lowercase )

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

				return a[:, 0, :]  # cls_token