


"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    KandinskyVaaImgaImgPipeline
     __UpperCamelCase     =    ['''image_embeds''', '''negative_image_embeds''', '''image''']
     __UpperCamelCase     =    [
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     __UpperCamelCase     =    [
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __UpperCamelCase     =    False






     @property
     def    lowerCamelCase__   (		self     :    str ):
           return 3_2






     @property
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           return 3_2






     @property
     def    lowerCamelCase__   (		self     :    List[Any] ):
           return self.time_input_dim






     @property
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           return self.time_input_dim * 4






     @property
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           return 1_0_0






     @property
     def    lowerCamelCase__   (		self     :    Any ):
           torch.manual_seed(0 )

           lowerCAmelCase    :		Optional[Any]				=      {
               '''in_channels''': 4,
               # 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,
           }

           lowerCAmelCase    :		List[str]				=      UNetaDConditionModel(**UpperCamelCase_ )
           return model






     @property
     def    lowerCamelCase__   (		self     :    str ):
           return {
               "block_out_channels": [3_2, 6_4],
               "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": 1_2,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }






     @property
     def    lowerCamelCase__   (		self     :    List[Any] ):
           torch.manual_seed(0 )
           lowerCAmelCase    :		int				=      VQModel(**self.dummy_movq_kwargs )
           return model






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Dict				=      self.dummy_unet
           lowerCAmelCase    :		Any				=      self.dummy_movq

           lowerCAmelCase    :		Dict				=      {
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.00_085,
               '''beta_end''': 0.012,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           lowerCAmelCase    :		Optional[int]				=      DDIMScheduler(**UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Dict=0 ):
           lowerCAmelCase    :		Dict				=      floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(seed + 1 ) ).to(
               UpperCamelCase_ )
           # create init_image
           lowerCAmelCase    :		List[Any]				=      floats_tensor((1, 3, 6_4, 6_4)    , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      image.cpu().permute(0    , 2    , 3    , 1 )[0]
           lowerCAmelCase    :		Tuple				=      Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )

           if str(UpperCamelCase_ ).startswith('''mps''' ):
                 lowerCAmelCase    :		Optional[Any]				=      torch.manual_seed(UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		int				=      torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      {
               '''image''': init_image,
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 6_4,
               '''width''': 6_4,
               '''num_inference_steps''': 1_0,
               '''guidance_scale''': 7.0,
               '''strength''': 0.2,
               '''output_type''': '''np''',
           }
           return inputs






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		int				=      '''cpu'''

           lowerCAmelCase    :		int				=      self.get_dummy_components()

           lowerCAmelCase    :		Optional[Any]				=      self.pipeline_class(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      pipe.to(UpperCamelCase_ )

           pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           lowerCAmelCase    :		Any				=      pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
           lowerCAmelCase    :		Tuple				=      output.images

           lowerCAmelCase    :		str				=      pipe(
               **self.get_dummy_inputs(UpperCamelCase_ )    , return_dict=UpperCamelCase_    , )[0]

           lowerCAmelCase    :		Union[str, Any]				=      image[0, -3:, -3:, -1]
           lowerCAmelCase    :		Dict				=      image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 6_4, 6_4, 3)

           lowerCAmelCase    :		Tuple				=      np.array(
               [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
           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()}'''



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






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






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		List[str]				=      load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )

           lowerCAmelCase    :		List[str]				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
           lowerCAmelCase    :		Union[str, Any]				=      '''A red cartoon frog, 4k'''

           lowerCAmelCase    :		Any				=      KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''    , torch_dtype=torch.floataa )
           pipe_prior.to(UpperCamelCase_ )

           lowerCAmelCase    :		Union[str, Any]				=      KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''    , torch_dtype=torch.floataa )
           lowerCAmelCase    :		Dict				=      pipeline.to(UpperCamelCase_ )

           pipeline.set_progress_bar_config(disable=UpperCamelCase_ )

           lowerCAmelCase    :		Optional[Any]				=      torch.Generator(device='''cpu''' ).manual_seed(0 )
           lowerCAmelCase,					lowerCAmelCase    :		Dict				=      pipe_prior(
               UpperCamelCase_    , generator=UpperCamelCase_    , num_inference_steps=5    , negative_prompt=''''''    , ).to_tuple()

           lowerCAmelCase    :		List[str]				=      pipeline(
               image=UpperCamelCase_    , image_embeds=UpperCamelCase_    , negative_image_embeds=UpperCamelCase_    , generator=UpperCamelCase_    , num_inference_steps=1_0_0    , height=7_6_8    , width=7_6_8    , strength=0.2    , output_type='''np'''    , )

           lowerCAmelCase    :		Dict				=      output.images[0]

           assert image.shape == (7_6_8, 7_6_8, 3)

           assert_mean_pixel_difference(UpperCamelCase_    , UpperCamelCase_ )






"""simple docstring"""





from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return math.sqrt(sum(pow(a - b       ,      2	) for a, b in zip(_snake_case       ,      _snake_case	)	)	)


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):

      if dataset.ndim != value_array.ndim:
            lowerCAmelCase    :		List[Any]				=      (
                '''Wrong input data\'s dimensions... '''
                f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
            )
            raise ValueError(_snake_case	)

      try:
            if dataset.shape[1] != value_array.shape[1]:
                  lowerCAmelCase    :		Dict				=      (
                      '''Wrong input data\'s shape... '''
                      f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                  )
                  raise ValueError(_snake_case	)
      except IndexError:
            if dataset.ndim != value_array.ndim:
                  raise TypeError('''Wrong shape'''	)

      if dataset.dtype != value_array.dtype:
            lowerCAmelCase    :		Optional[Any]				=      (
                '''Input data have different datatype... '''
                f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
            )
            raise TypeError(_snake_case	)

      lowerCAmelCase    :		str				=      []

      for value in value_array:
            lowerCAmelCase    :		int				=      euclidean(_snake_case       ,      dataset[0]	)
            lowerCAmelCase    :		Union[str, Any]				=      dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  lowerCAmelCase    :		Any				=      euclidean(_snake_case       ,      _snake_case	)

                  if dist > temp_dist:
                        lowerCAmelCase    :		List[Any]				=      temp_dist
                        lowerCAmelCase    :		Tuple				=      dataset_value.tolist()

            answer.append([vector, dist]	)

      return answer


def    _snake_case  (  _snake_case :	np.ndarray       ,      _snake_case :	np.ndarray	):
      return np.dot(_snake_case       ,      _snake_case	) / (norm(_snake_case	) * norm(_snake_case	))


if __name__ == "__main__":
 import doctest

 doctest.testmod()


