


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	KandinskyVaaInpaintPipeline
 _lowerCAmelCase 					=	["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 _lowerCAmelCase 					=	[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 _lowerCAmelCase 					=	[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCAmelCase 					=	False
 @property
 def 							__UpperCAmelCase		( self						)					->							Dict:
      return 32
 @property
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return 32
 @property
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      return self.time_input_dim
 @property
 def 							__UpperCAmelCase		( self						)					->							Any:
      return self.time_input_dim * 4
 @property
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      return 1_00
 @property
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      torch.manual_seed(0						)

      _a			      =							{
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      _a			      =							UNetaDConditionModel(**__magic_name__						)
      return model
 @property
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }
 @property
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      torch.manual_seed(0						)
      _a			      =							VQModel(**self.dummy_movq_kwargs						)
      return model
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      _a			      =							self.dummy_unet
      _a			      =							self.dummy_movq

      _a			      =							DDIMScheduler(
          num_train_timesteps=10_00      ,				beta_schedule='linear'      ,				beta_start=0.0_0_0_8_5      ,				beta_end=0.0_1_2      ,				clip_sample=__magic_name__      ,				set_alpha_to_one=__magic_name__      ,				steps_offset=1      ,				prediction_type='epsilon'      ,				thresholding=__magic_name__      ,				)

      _a			      =							{
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=0						)					->							Tuple:
      _a			      =							floats_tensor((1, self.text_embedder_hidden_size)      ,				rng=random.Random(__magic_name__						)						).to(__magic_name__						)
      _a			      =							floats_tensor((1, self.text_embedder_hidden_size)      ,				rng=random.Random(seed + 1						)						).to(
          __magic_name__						)
      # create init_image
      _a			      =							floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(__magic_name__						)						).to(__magic_name__						)
      _a			      =							image.cpu().permute(0      ,				2      ,				3      ,				1						)[0]
      _a			      =							Image.fromarray(np.uinta(__magic_name__						)						).convert('RGB'						).resize((2_56, 2_56)						)
      # create mask
      _a			      =							np.ones((64, 64)      ,				dtype=np.floataa						)
      _a			      =							0

      if str(__magic_name__						).startswith('mps'						):
           _a			      =							torch.manual_seed(__magic_name__						)
      else:
           _a			      =							torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
      _a			      =							{
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							'cpu'

      _a			      =							self.get_dummy_components()

      _a			      =							self.pipeline_class(**__magic_name__						)
      _a			      =							pipe.to(__magic_name__						)

      pipe.set_progress_bar_config(disable=__magic_name__						)

      _a			      =							pipe(**self.get_dummy_inputs(__magic_name__						)						)
      _a			      =							output.images

      _a			      =							pipe(
          **self.get_dummy_inputs(__magic_name__						)      ,				return_dict=__magic_name__      ,				)[0]

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

      print(f'image.shape {image.shape}'						)

      assert image.shape == (1, 64, 64, 3)

      _a			      =							np.array(
          [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]						)

      assert (
          np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
      ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
      ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'




 def 							__UpperCAmelCase		( self						)					->							str:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3						)





@slow
@require_torch_gpu
class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'						)

      _a			      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
      _a			      =							np.ones((7_68, 7_68)      ,				dtype=np.floataa						)
      _a			      =							0

      _a			      =							'a hat'

      _a			      =							KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior'      ,				torch_dtype=torch.floataa						)
      pipe_prior.to(__magic_name__						)

      _a			      =							KandinskyVaaInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,				torch_dtype=torch.floataa						)
      _a			      =							pipeline.to(__magic_name__						)
      pipeline.set_progress_bar_config(disable=__magic_name__						)

      _a			      =							torch.Generator(device='cpu'						).manual_seed(0						)
      _a      ,   _a			      =							pipe_prior(
          __magic_name__      ,				generator=__magic_name__      ,				num_inference_steps=5      ,				negative_prompt=''      ,				).to_tuple()

      _a			      =							pipeline(
          image=__magic_name__      ,				mask_image=__magic_name__      ,				image_embeds=__magic_name__      ,				negative_image_embeds=__magic_name__      ,				generator=__magic_name__      ,				num_inference_steps=1_00      ,				height=7_68      ,				width=7_68      ,				output_type='np'      ,				)

      _a			      =							output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(__magic_name__      ,				__magic_name__						)





'''simple docstring'''






import logging

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

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






def 	_A				(lowerCAmelCase__  :np.ndarray		)   ->  np.ndarray:




     '''simple docstring'''



     return input_array.reshape((input_array.size, 1)		)






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



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

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



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

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



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

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






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     assert classes > dimensions

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

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






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
     _a			      =							np.array([0, 0, 0, 1, 1]		)
     _a			      =							2
     _a			      =							2

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






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
     _a			      =							2
     _a			      =							np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]		)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()

