code: 
  from sklearn.metrics import precision_score
  
  import datasets
  
  
  __lowerCAmelCase				      =					'\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n'
  
  
  __lowerCAmelCase				      =					'\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - \'warn\': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'precision\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'precision\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(results)\n        {\'precision\': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'precision\': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results[\'precision\']])\n        [0.67, 0.0, 0.0]\n'
  
  
  __lowerCAmelCase				      =					'\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,		_KWARGS_DESCRIPTION				)
  class 						lowerCamelCase			(					datasets.Metric				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any] )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return datasets.MetricInfo(
  				    description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(
  				        {
  				            '''predictions''': datasets.Sequence(datasets.Value('''int32''' ) ),
  				            '''references''': datasets.Sequence(datasets.Value('''int32''' ) ),
  				        }
  				        if self.config_name == '''multilabel'''
  				        else {
  				            '''predictions''': datasets.Value('''int32''' ),
  				            '''references''': datasets.Value('''int32''' ),
  				        } )      ,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html''']      ,    )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Dict      ,    lowercase			:Tuple=None      ,    lowercase			:int=1      ,    lowercase			:int="binary"      ,    lowercase			:Dict=None      ,    lowercase			:Optional[int]="warn"      ,    )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   precision_score(
  				    lowercase      ,    lowercase      ,    labels=lowercase      ,    pos_label=lowercase      ,    average=lowercase      ,    sample_weight=lowercase      ,    zero_division=lowercase      ,    )
  				return {"precision": float(lowercase ) if score.size == 1 else score}
style_context: 
  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