






import torch
from torch import nn

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




class 					UpperCamelCase_ (     snake_case_			,   snake_case_							):



	'''simple docstring'''




	@register_to_config
	def __init__(			self			,     *,
	    a = 4			,     a = 7_68			,     a			,     a			,     )			->       Dict:
								super().__init__()

								snake_case_           =				nn.Parameter(torch.zeros(a   )   )

								# parameters for additional clip time embeddings
								snake_case_           =				nn.Linear(a			,     a   )
								snake_case_           =				nn.Linear(a			,     a   )

								# parameters for encoder hidden states
								snake_case_           =				clip_extra_context_tokens
								snake_case_           =				nn.Linear(
								    a			,     self.clip_extra_context_tokens * cross_attention_dim   )
								snake_case_           =				nn.Linear(a			,     a   )
								snake_case_           =				nn.LayerNorm(a   )






	def 				_UpperCamelCase     (			self			,     *, a			,     a			,     a			,     a   )			->       str:
								if do_classifier_free_guidance:
															# Add the classifier free guidance embeddings to the image embeddings
															snake_case_           =				image_embeddings.shape[0]
															snake_case_           =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0   )
															snake_case_           =				classifier_free_guidance_embeddings.expand(
															    a			,     -1   )
															snake_case_           =				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]

								snake_case_           =				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, ...
								snake_case_           =				self.embedding_proj(a   )
								snake_case_           =				self.clip_image_embeddings_project_to_time_embeddings(a   )
								snake_case_           =				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"
								snake_case_           =				self.clip_extra_context_tokens_proj(a   )
								snake_case_           =				clip_extra_context_tokens.reshape(a			,     -1			,     self.clip_extra_context_tokens   )
								snake_case_           =				clip_extra_context_tokens.permute(0			,     2			,     1   )

								snake_case_           =				self.encoder_hidden_states_proj(a   )
								snake_case_           =				self.text_encoder_hidden_states_norm(a   )
								snake_case_           =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]			,     dim=1   )

								return text_encoder_hidden_states, additive_clip_time_embeddings













import unittest

import numpy as np





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = None				,	):
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)

							if shape_a[0] != shape_b[0]:
														snake_case_           =				(
														    'Expected the same number of rows for A and B. '
														    f'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(a_)

							if shape_b[1] != shape_c[1]:
														snake_case_           =				(
														    'Expected the same number of columns for B and C. '
														    f'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(a_)

							snake_case_           =				pseudo_inv
							if a_inv is None:
														try:
																					snake_case_           =				np.linalg.inv(a_)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    'Input matrix A is not invertible. Cannot compute Schur complement.')

							return mat_c - mat_b.T @ a_inv @ mat_b




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								snake_case_           =				schur_complement(a			,     a			,     a   )

								snake_case_           =				np.block([[a, b], [b.T, c]]   )

								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )

								self.assertAlmostEqual(a			,     det_a * det_s   )




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )






	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1, 3], [6, 3, 5]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	unittest.main()





