

import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	return input_array.reshape((input_array.size, 1)      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= np.nan
	for i in range(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Tuple									= features[:, labels == i]
		UpperCAmelCase__:	int									= data.mean(1      )
		# Centralize the data of class i
		UpperCAmelCase__:	Optional[Any]									= data - column_reshape(SCREAMING_SNAKE_CASE      )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      )
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCAmelCase__:	Dict									= np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      )

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= features.mean(1      )
	UpperCAmelCase__:	Optional[Any]									= np.nan
	for i in range(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[Any]									= features[:, labels == i]
		UpperCAmelCase__:	Optional[Any]									= data.shape[1]
		UpperCAmelCase__:	List[Any]									= data.mean(1      )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += device_data * np.dot(
			    column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )	,(column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )).T	,)
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCAmelCase__:	Tuple									= device_data * np.dot(
			    column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )	,(column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )).T	,)

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):

	# Check if the features have been loaded
	if features.any():
		UpperCAmelCase__:	Tuple									= features.mean(1      )
		# Center the dataset
		UpperCAmelCase__:	List[Any]									= features - np.reshape(SCREAMING_SNAKE_CASE	,(data_mean.size, 1)      )
		UpperCAmelCase__:	Optional[int]									= np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      ) / features.shape[1]
		UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= np.linalg.eigh(SCREAMING_SNAKE_CASE      )
		# Take all the columns in the reverse order (-1), and then takes only the first
		UpperCAmelCase__:	Optional[Any]									= eigenvectors[:, ::-1][:, 0:dimensions]
		# Project the database on the new space
		UpperCAmelCase__:	Tuple									= np.dot(filtered_eigenvectors.T	,SCREAMING_SNAKE_CASE      )
		logging.info("Principal Component Analysis computed"      )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR	,format="%(message)s"	,force=SCREAMING_SNAKE_CASE      )
		logging.error("Dataset empty"      )
		raise AssertionError



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	assert classes > dimensions

	# Check if features have been already loaded
	if features.any:
		UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= eigh(
		    covariance_between_classes(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )	,covariance_within_classes(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )	,)
		UpperCAmelCase__:	int									= eigenvectors[:, ::-1][:, :dimensions]
		UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[Any]									= np.linalg.svd(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Optional[Any]									= svd_matrix[:, 0:dimensions]
		UpperCAmelCase__:	Optional[int]									= np.dot(filtered_svd_matrix.T	,SCREAMING_SNAKE_CASE      )
		logging.info("Linear Discriminant Analysis computed"      )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR	,format="%(message)s"	,force=SCREAMING_SNAKE_CASE      )
		logging.error("Dataset empty"      )
		raise AssertionError



def 			_A	(      ):
	# Create dummy dataset with 2 classes and 3 features
	UpperCAmelCase__:	List[str]									= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]      )
	UpperCAmelCase__:	Optional[int]									= np.array([0, 0, 0, 1, 1]      )
	UpperCAmelCase__:	List[Any]									= 2
	UpperCAmelCase__:	Union[str, Any]									= 2

	# Assert that the function raises an AssertionError if dimensions > classes
	with pytest.raises(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	List[Any]									= linear_discriminant_analysis(
		    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if isinstance(SCREAMING_SNAKE_CASE	,np.ndarray      ):
			raise AssertionError(
			    "Did not raise AssertionError for dimensions > classes"      )
		assert error_info.type is AssertionError



def 			_A	(      ):
	UpperCAmelCase__:	Optional[Any]									= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]      )
	UpperCAmelCase__:	Optional[int]									= 2
	UpperCAmelCase__:	Union[str, Any]									= np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]      )

	with pytest.raises(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	Any									= principal_component_analysis(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if not np.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
			raise AssertionError
		assert error_info.type is AssertionError


if __name__ == "__main__":
					import doctest

					doctest.testmod()


import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class __UpperCamelCase     (				_a					,_a		):






					'''simple docstring'''



					@register_to_config
					def __init__(				self				,      *,
					    lowerCamelCase__ = 4				,      lowerCamelCase__ = 7_6_8				,      lowerCamelCase__				,      lowerCamelCase__				,      ):
						super().__init__()

						UpperCAmelCase__:	int									= nn.Parameter(torch.zeros(lowerCamelCase__						)						)

						# parameters for additional clip time embeddings
						UpperCAmelCase__:	Optional[Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)

						# parameters for encoder hidden states
						UpperCAmelCase__:	Tuple									= clip_extra_context_tokens
						UpperCAmelCase__:	List[str]									= nn.Linear(
						    lowerCamelCase__				,      self.clip_extra_context_tokens * cross_attention_dim						)
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(lowerCamelCase__				,      lowerCamelCase__						)
						UpperCAmelCase__:	str									= nn.LayerNorm(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      *, lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							UpperCAmelCase__:	Any									= image_embeddings.shape[0]
							UpperCAmelCase__:	Optional[Any]									= self.learned_classifier_free_guidance_embeddings.unsqueeze(0						)
							UpperCAmelCase__:	Union[str, Any]									= classifier_free_guidance_embeddings.expand(
							    lowerCamelCase__				,      -1						)
							UpperCAmelCase__:	Tuple									= torch.cat([classifier_free_guidance_embeddings, image_embeddings]				,      dim=0						)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						UpperCAmelCase__:	str									= prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						UpperCAmelCase__:	List[str]									= self.embedding_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						UpperCAmelCase__:	Dict									= self.clip_extra_context_tokens_proj(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= clip_extra_context_tokens.reshape(lowerCamelCase__				,      -1				,      self.clip_extra_context_tokens						)
						UpperCAmelCase__:	Dict									= clip_extra_context_tokens.permute(0				,      2				,      1						)

						UpperCAmelCase__:	Any									= self.encoder_hidden_states_proj(lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.text_encoder_hidden_states_norm(lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]				,      dim=1						)

						return text_encoder_hidden_states, additive_clip_time_embeddings