

'''simple docstring'''




import logging

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

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




def 						UpperCamelCase	(			a			)				->     np.ndarray:






     '''simple docstring'''
     return input_array.reshape((input_array.size, 1)			)




def 						UpperCamelCase	(			a		,						a		,						a			)				->     np.ndarray:






     '''simple docstring'''
     __magic_name__						       =    np.nan
     for i in range(a			):
          __magic_name__						       =    features[:, labels == i]
          __magic_name__						       =    data.mean(1			)
          # Centralize the data of class i
          __magic_name__						       =    data - column_reshape(a			)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += np.dot(a		,						centered_data.T			)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               __magic_name__						       =    np.dot(a		,						centered_data.T			)

     return covariance_sum / features.shape[1]




def 						UpperCamelCase	(			a		,						a		,						a			)				->     np.ndarray:






     '''simple docstring'''
     __magic_name__						       =    features.mean(1			)
     __magic_name__						       =    np.nan
     for i in range(a			):
          __magic_name__						       =    features[:, labels == i]
          __magic_name__						       =    data.shape[1]
          __magic_name__						       =    data.mean(1			)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += device_data * np.dot(
                   column_reshape(a			) - column_reshape(a			)		,						(column_reshape(a			) - column_reshape(a			)).T		,						)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               __magic_name__						       =    device_data * np.dot(
                   column_reshape(a			) - column_reshape(a			)		,						(column_reshape(a			) - column_reshape(a			)).T		,						)

     return covariance_sum / features.shape[1]




def 						UpperCamelCase	(			a		,						a			)				->     np.ndarray:






     '''simple docstring'''

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

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




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     np.ndarray:






     '''simple docstring'''
     assert classes > dimensions

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

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




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     # Create dummy dataset with 2 classes and 3 features
     __magic_name__						       =    np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]			)
     __magic_name__						       =    np.array([0, 0, 0, 1, 1]			)
     __magic_name__						       =    2
     __magic_name__						       =    2

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




def 						UpperCamelCase	(			)				->     None:






     '''simple docstring'''
     __magic_name__						       =    np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]			)
     __magic_name__						       =    2
     __magic_name__						       =    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(a			) as error_info:
          __magic_name__						       =    principal_component_analysis(a		,						a			)
          if not np.allclose(a		,						a			):
               raise AssertionError
          assert error_info.type is AssertionError


if __name__ == "__main__":
      import doctest

      doctest.testmod()









'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					StableDiffusionInpaintPipeline
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE					:Tuple      	=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE					:str      	=					frozenset(
     []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					frozenset([]     )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    UNetaDConditionModel(
          block_out_channels=(32, 64)			,				layers_per_block=2			,				sample_size=32			,				in_channels=9			,				out_channels=4			,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,				cross_attention_dim=32			,				attention_head_dim=(2, 4)			,				use_linear_projection=a__			,				)
      __magic_name__						       =    PNDMScheduler(skip_prk_steps=a__      )
      torch.manual_seed(0      )
      __magic_name__						       =    AutoencoderKL(
          block_out_channels=[32, 64]			,				in_channels=3			,				out_channels=3			,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,				latent_channels=4			,				sample_size=128			,				)
      torch.manual_seed(0      )
      __magic_name__						       =    CLIPTextConfig(
          bos_token_id=0			,				eos_token_id=2			,				hidden_size=32			,				intermediate_size=37			,				layer_norm_eps=1E-05			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1000			,				hidden_act='''gelu'''			,				projection_dim=512			,				)
      __magic_name__						       =    CLIPTextModel(a__      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

      __magic_name__						       =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[int]			,				a__		:   List[Any]=0      ):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      __magic_name__						       =    floats_tensor((1, 3, 32, 32)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      )[0]
      __magic_name__						       =    Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )
      __magic_name__						       =    Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': init_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    StableDiffusionInpaintPipeline(**a__      )
      __magic_name__						       =    sd_pipe.to(a__      )
      sd_pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    self.get_dummy_inputs(a__      )
      __magic_name__						       =    sd_pipe(**a__      ).images
      __magic_name__						       =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __magic_name__						       =    np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]      )

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



 def 			snake_case__  (   self		:   List[Any]      ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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



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



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(a__			,				safety_checker=a__      )
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 9E-3



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				torch_dtype=torch.floataa			,				safety_checker=a__			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 5E-1



 def 			snake_case__  (   self		:   List[Any]      ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    PNDMScheduler.from_pretrained(a__			,				subfolder='''scheduler'''      )
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				safety_checker=a__			,				scheduler=a__			,				torch_dtype=torch.floataa			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing(1      )
      pipe.enable_sequential_cpu_offload()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				num_inference_steps=2			,				output_type='''np'''			,				)

      __magic_name__						       =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






