
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		KandinskyInpaintPipeline
       UpperCamelCase_   =		['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
       UpperCamelCase_   =		[
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
           '''mask_image''',
       ]
       UpperCamelCase_   =		[
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase_   =		False
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         return 32
       @property
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         return 32
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         return self.time_input_dim
       @property
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         return self.time_input_dim * 4
       @property
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         return 100
       @property
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Tuple       =     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''       )
         return tokenizer
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         torch.manual_seed(0       )
         _lowercase:			Union[str, Any]       =     MCLIPConfig(
             numDims=self.cross_attention_dim ,				transformerDimensions=self.text_embedder_hidden_size ,				hidden_size=self.text_embedder_hidden_size ,				intermediate_size=37 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				vocab_size=1005 ,				)

         _lowercase:			Union[str, Any]       =     MultilingualCLIP(A_       )
         _lowercase:			Union[str, Any]       =     text_encoder.eval()

         return text_encoder
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         torch.manual_seed(0       )

         _lowercase:			Union[str, Any]       =     {
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_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''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         _lowercase:			int       =     UNetaDConditionModel(**A_       )
         return model
       @property
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         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 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         torch.manual_seed(0       )
         _lowercase:			Optional[int]       =     VQModel(**self.dummy_movq_kwargs       )
         return model
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Dict       =     self.dummy_text_encoder
         _lowercase:			List[str]       =     self.dummy_tokenizer
         _lowercase:			List[Any]       =     self.dummy_unet
         _lowercase:			Optional[Any]       =     self.dummy_movq

         _lowercase:			Dict       =     DDIMScheduler(
             num_train_timesteps=1000 ,				beta_schedule='''linear''' ,				beta_start=0.0_00_85 ,				beta_end=0.0_12 ,				clip_sample=A_ ,				set_alpha_to_one=A_ ,				steps_offset=1 ,				prediction_type='''epsilon''' ,				thresholding=A_ ,				)

         _lowercase:			List[Any]       =     {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components
       def 							lowercase_							(     self ,				A_ ,				A_=0       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(A_       )       ).to(A_       )
         _lowercase:			Tuple       =     floats_tensor((1, self.cross_attention_dim) ,				rng=random.Random(seed + 1       )       ).to(A_       )
         # create init_image
         _lowercase:			int       =     floats_tensor((1, 3, 64, 64) ,				rng=random.Random(A_       )       ).to(A_       )
         _lowercase:			int       =     image.cpu().permute(0 ,				2 ,				3 ,				1       )[0]
         _lowercase:			Tuple       =     Image.fromarray(np.uinta(A_       )       ).convert('''RGB'''       ).resize((256, 256)       )
         # create mask
         _lowercase:			Optional[Any]       =     np.ones((64, 64) ,				dtype=np.floataa       )
         _lowercase:			Any       =     0

         if str(A_       ).startswith('''mps'''       ):
           _lowercase:			str       =     torch.manual_seed(A_       )
         else:
           _lowercase:			Tuple       =     torch.Generator(device=A_       ).manual_seed(A_       )
         _lowercase:			Optional[Any]       =     {
             '''prompt''': '''horse''',
             '''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 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Optional[int]       =     '''cpu'''

         _lowercase:			Optional[Any]       =     self.get_dummy_components()

         _lowercase:			Optional[Any]       =     self.pipeline_class(**A_       )
         _lowercase:			Dict       =     pipe.to(A_       )

         pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			Dict       =     pipe(**self.get_dummy_inputs(A_       )       )
         _lowercase:			Tuple       =     output.images

         _lowercase:			List[Any]       =     pipe(
             **self.get_dummy_inputs(A_       ) ,				return_dict=A_ ,				)[0]

         _lowercase:			int       =     image[0, -3:, -3:, -1]
         _lowercase:			str       =     image_from_tuple[0, -3:, -3:, -1]

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

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

         _lowercase:			Union[str, Any]       =     np.array(
             [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]       )

         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 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )


@slow
@require_torch_gpu
class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''       )

         _lowercase:			int       =     load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
         _lowercase:			Optional[Any]       =     np.ones((768, 768) ,				dtype=np.floataa       )
         _lowercase:			Any       =     0

         _lowercase:			Optional[int]       =     '''a hat'''

         _lowercase:			Tuple       =     KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior''' ,				torch_dtype=torch.floataa       )
         pipe_prior.to(A_       )

         _lowercase:			Any       =     KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint''' ,				torch_dtype=torch.floataa       )
         _lowercase:			Optional[int]       =     pipeline.to(A_       )
         pipeline.set_progress_bar_config(disable=A_       )

         _lowercase:			str       =     torch.Generator(device='''cpu'''       ).manual_seed(0       )
         _lowercase ,				_lowercase:			List[Any]       =     pipe_prior(
             A_ ,				generator=A_ ,				num_inference_steps=5 ,				negative_prompt='''''' ,				).to_tuple()

         _lowercase:			List[str]       =     pipeline(
             A_ ,				image=A_ ,				mask_image=A_ ,				image_embeds=A_ ,				negative_image_embeds=A_ ,				generator=A_ ,				num_inference_steps=100 ,				height=768 ,				width=768 ,				output_type='''np''' ,				)

         _lowercase:			Dict       =     output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(A_ ,				A_       )


"""simple docstring"""




from typing import Any

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return np.array_equal(_UpperCamelCase ,				matrix.conjugate().T						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     v.conjugate().T
  _lowercase:			List[Any]       =     v_star.dot(_UpperCamelCase						)
  assert isinstance(_UpperCamelCase ,				np.ndarray						)
  return (v_star_dot.dot(_UpperCamelCase						)) / (v_star.dot(_UpperCamelCase						))




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]						)
  _lowercase:			str       =     np.array([[1], [2], [3]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  print(rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						)						)

  _lowercase:			Any       =     np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  assert rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						) == float(3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()
