




import logging

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

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray				)  ->							np.ndarray:
    return input_array.reshape((input_array.size, 1)				)




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

    return covariance_sum / features.shape[1]




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

    return covariance_sum / features.shape[1]




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						int				)  ->							np.ndarray:

    # Check if the features have been loaded
    if features.any():
        UpperCAmelCase_										=					features.mean(1				)
        # Center the dataset
        UpperCAmelCase_										=					features - np.reshape(__UpperCamelCase	,					(data_mean.size, 1)				)
        UpperCAmelCase_										=					np.dot(__UpperCamelCase	,					centered_data.T				) / features.shape[1]
        UpperCAmelCase_			,							UpperCAmelCase_										=					np.linalg.eigh(__UpperCamelCase				)
        # Take all the columns in the reverse order (-1), and then takes only the first
        UpperCAmelCase_										=					eigenvectors[:, ::-1][:, 0:dimensions]
        # Project the database on the new space
        UpperCAmelCase_										=					np.dot(filtered_eigenvectors.T	,					__UpperCamelCase				)
        logging.info('''Principal Component Analysis computed'''				)

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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						np.ndarray	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							np.ndarray:
    assert classes > dimensions

    # Check if features have been already loaded
    if features.any:
        UpperCAmelCase_			,							UpperCAmelCase_										=					eigh(
            covariance_between_classes(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)	,					covariance_within_classes(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)	,					)
        UpperCAmelCase_										=					eigenvectors[:, ::-1][:, :dimensions]
        UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					np.linalg.svd(__UpperCamelCase				)
        UpperCAmelCase_										=					svd_matrix[:, 0:dimensions]
        UpperCAmelCase_										=					np.dot(filtered_svd_matrix.T	,					__UpperCamelCase				)
        logging.info('''Linear Discriminant Analysis computed'''				)

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




def  SCREAMING_SNAKE_CASE     (	)  ->							None:
    # Create dummy dataset with 2 classes and 3 features
    UpperCAmelCase_										=					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]				)
    UpperCAmelCase_										=					np.array([0, 0, 0, 1, 1]				)
    UpperCAmelCase_										=					2
    UpperCAmelCase_										=					2

    # Assert that the function raises an AssertionError if dimensions > classes
    with pytest.raises(__UpperCamelCase				) as error_info:
        UpperCAmelCase_										=					linear_discriminant_analysis(
            __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
        if isinstance(__UpperCamelCase	,					np.ndarray				):
            raise AssertionError(
                '''Did not raise AssertionError for dimensions > classes'''				)
        assert error_info.type is AssertionError




def  SCREAMING_SNAKE_CASE     (	)  ->							None:
    UpperCAmelCase_										=					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]				)
    UpperCAmelCase_										=					2
    UpperCAmelCase_										=					np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]				)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()











import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase      			=						False



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      str    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     @property
     def   lowerCamelCase_       (     self       :      Any    ):
         return 12
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return 12
     @property
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         return 32
     @property
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					VQModel(
             block_out_channels=[32, 64] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=3 ,   num_vq_embeddings=self.num_embed ,   vq_embed_dim=3 ,   )
         return model
     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
         return tokenizer
     @property
     def   lowerCamelCase_       (     self       :      int    ):
         torch.manual_seed(0    )
         UpperCAmelCase_										=					CLIPTextConfig(
             bos_token_id=0 ,   eos_token_id=2 ,   hidden_size=self.text_embedder_hidden_size ,   intermediate_size=37 ,   layer_norm_eps=1E-05 ,   num_attention_heads=4 ,   num_hidden_layers=5 ,   pad_token_id=1 ,   vocab_size=10_00 ,   )
         return CLIPTextModel(__snake_case    )
     @property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         torch.manual_seed(0    )

         UpperCAmelCase_										=					12
         UpperCAmelCase_										=					12

         UpperCAmelCase_										=					{
             '''attention_bias''': True,
             '''cross_attention_dim''': 32,
             '''attention_head_dim''': height * width,
             '''num_attention_heads''': 1,
             '''num_vector_embeds''': self.num_embed,
             '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
             '''norm_num_groups''': 32,
             '''sample_size''': width,
             '''activation_fn''': '''geglu-approximate''',
         }

         UpperCAmelCase_										=					TransformeraDModel(**__snake_case    )
         return model
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					'''cpu'''

         UpperCAmelCase_										=					self.dummy_vqvae
         UpperCAmelCase_										=					self.dummy_text_encoder
         UpperCAmelCase_										=					self.dummy_tokenizer
         UpperCAmelCase_										=					self.dummy_transformer
         UpperCAmelCase_										=					VQDiffusionScheduler(self.num_embed    )
         UpperCAmelCase_										=					LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case    )

         UpperCAmelCase_										=					VQDiffusionPipeline(
             vqvae=__snake_case ,   text_encoder=__snake_case ,   tokenizer=__snake_case ,   transformer=__snake_case ,   scheduler=__snake_case ,   learned_classifier_free_sampling_embeddings=__snake_case ,   )
         UpperCAmelCase_										=					pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					'''teddy bear playing in the pool'''

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe([prompt] ,   generator=__snake_case ,   num_inference_steps=2 ,   output_type='''np'''    )
         UpperCAmelCase_										=					output.images

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             [prompt] ,   generator=__snake_case ,   output_type='''np''' ,   return_dict=__snake_case ,   num_inference_steps=2    )[0]

         UpperCAmelCase_										=					image[0, -3:, -3:, -1]
         UpperCAmelCase_										=					image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         UpperCAmelCase_										=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2




     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					'''cpu'''

         UpperCAmelCase_										=					self.dummy_vqvae
         UpperCAmelCase_										=					self.dummy_text_encoder
         UpperCAmelCase_										=					self.dummy_tokenizer
         UpperCAmelCase_										=					self.dummy_transformer
         UpperCAmelCase_										=					VQDiffusionScheduler(self.num_embed    )
         UpperCAmelCase_										=					LearnedClassifierFreeSamplingEmbeddings(
             learnable=__snake_case ,   hidden_size=self.text_embedder_hidden_size ,   length=tokenizer.model_max_length    )

         UpperCAmelCase_										=					VQDiffusionPipeline(
             vqvae=__snake_case ,   text_encoder=__snake_case ,   tokenizer=__snake_case ,   transformer=__snake_case ,   scheduler=__snake_case ,   learned_classifier_free_sampling_embeddings=__snake_case ,   )
         UpperCAmelCase_										=					pipe.to(__snake_case    )
         pipe.set_progress_bar_config(disable=__snake_case    )

         UpperCAmelCase_										=					'''teddy bear playing in the pool'''

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe([prompt] ,   generator=__snake_case ,   num_inference_steps=2 ,   output_type='''np'''    )
         UpperCAmelCase_										=					output.images

         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipe(
             [prompt] ,   generator=__snake_case ,   output_type='''np''' ,   return_dict=__snake_case ,   num_inference_steps=2    )[0]

         UpperCAmelCase_										=					image[0, -3:, -3:, -1]
         UpperCAmelCase_										=					image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 24, 24, 3)

         UpperCAmelCase_										=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2



@slow
@require_torch_gpu
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''    )

         UpperCAmelCase_										=					VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''    )
         UpperCAmelCase_										=					pipeline.to(__snake_case    )
         pipeline.set_progress_bar_config(disable=__snake_case    )

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(0    )
         UpperCAmelCase_										=					pipeline(
             '''teddy bear playing in the pool''' ,   num_images_per_prompt=1 ,   generator=__snake_case ,   output_type='''np''' ,   )

         UpperCAmelCase_										=					output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image    ).max() < 2.0





