




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   __a(					_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				KandinskyInpaintPipeline
    lowerCAmelCase        =				['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
    lowerCAmelCase        =				[
        '''prompt''',
        '''negative_prompt''',
        '''image_embeds''',
        '''negative_image_embeds''',
        '''image''',
        '''mask_image''',
    ]
    lowerCAmelCase        =				[
        '''generator''',
        '''height''',
        '''width''',
        '''latents''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''num_inference_steps''',
        '''return_dict''',
        '''guidance_scale''',
        '''num_images_per_prompt''',
        '''output_type''',
        '''return_dict''',
    ]
    lowerCAmelCase        =				False





    @property
    def 							a__				(				self  )			->		Union[str, Any]:
         return 32





    @property
    def 							a__				(				self  )			->		Any:
         return 32





    @property
    def 							a__				(				self  )			->		Dict:
         return self.time_input_dim





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





    @property
    def 							a__				(				self  )			->		List[Any]:
         return 100





    @property
    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[Any]       =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''  )
         return tokenizer





    @property
    def 							a__				(				self  )			->		Optional[int]:
         torch.manual_seed(0  )
         UpperCAmelCase_      :							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=1_005       ,)

         UpperCAmelCase_      :							Dict       =      MultilingualCLIP(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      text_encoder.eval()

         return text_encoder





    @property
    def 							a__				(				self  )			->		Dict:
         torch.manual_seed(0  )

         UpperCAmelCase_      :							List[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,
         }

         UpperCAmelCase_      :							Tuple       =      UNetaDConditionModel(**_SCREAMING_SNAKE_CASE  )
         return model





    @property
    def 							a__				(				self  )			->		Optional[int]:
         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 							a__				(				self  )			->		Union[str, Any]:
         torch.manual_seed(0  )
         UpperCAmelCase_      :							Dict       =      VQModel(**self.dummy_movq_kwargs  )
         return model





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							int       =      self.dummy_text_encoder
         UpperCAmelCase_      :							Any       =      self.dummy_tokenizer
         UpperCAmelCase_      :							Optional[Any]       =      self.dummy_unet
         UpperCAmelCase_      :							Dict       =      self.dummy_movq

         UpperCAmelCase_      :							Optional[int]       =      DDIMScheduler(
             num_train_timesteps=1_000       ,beta_schedule='''linear'''       ,beta_start=0.0_00_85       ,beta_end=0.0_12       ,clip_sample=_SCREAMING_SNAKE_CASE       ,set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							Tuple       =      {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=0  )			->		str:
         UpperCAmelCase_      :							str       =      floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[Any]       =      floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1  )  ).to(_SCREAMING_SNAKE_CASE  )
         # create init_image
         UpperCAmelCase_      :							Optional[Any]       =      floats_tensor((1, 3, 64, 64)       ,rng=random.Random(_SCREAMING_SNAKE_CASE  )  ).to(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      image.cpu().permute(0       ,2       ,3       ,1  )[0]
         UpperCAmelCase_      :							List[Any]       =      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE  )  ).convert('''RGB'''  ).resize((256, 256)  )
         # create mask
         UpperCAmelCase_      :							str       =      np.ones((64, 64)       ,dtype=np.floataa  )
         UpperCAmelCase_      :							List[str]       =      0

         if str(_SCREAMING_SNAKE_CASE  ).startswith('''mps'''  ):
              UpperCAmelCase_      :							int       =      torch.manual_seed(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							int       =      torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      {
             '''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 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[Any]       =      '''cpu'''

         UpperCAmelCase_      :							Tuple       =      self.get_dummy_components()

         UpperCAmelCase_      :							Optional[int]       =      self.pipeline_class(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      pipe.to(_SCREAMING_SNAKE_CASE  )

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							List[str]       =      output.images

         UpperCAmelCase_      :							str       =      pipe(
             **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )       ,return_dict=_SCREAMING_SNAKE_CASE       ,)[0]

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

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

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

         UpperCAmelCase_      :							Dict       =      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 							a__				(				self  )			->		List[Any]:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3  )


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






    """simple docstring"""





    def 							a__				(				self  )			->		Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''  )

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

         UpperCAmelCase_      :							Tuple       =      '''a hat'''

         UpperCAmelCase_      :							Any       =      KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa  )
         pipe_prior.to(_SCREAMING_SNAKE_CASE  )

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

         UpperCAmelCase_      :							str       =      torch.Generator(device='''cpu'''  ).manual_seed(0  )
         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      pipe_prior(
             _SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

         UpperCAmelCase_      :							Tuple       =      pipeline(
             _SCREAMING_SNAKE_CASE       ,image=_SCREAMING_SNAKE_CASE       ,mask_image=_SCREAMING_SNAKE_CASE       ,image_embeds=_SCREAMING_SNAKE_CASE       ,negative_image_embeds=_SCREAMING_SNAKE_CASE       ,generator=_SCREAMING_SNAKE_CASE       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

         UpperCAmelCase_      :							Any       =      output.images[0]

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

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def lowerCamelCase__       (      _lowercase			,						_lowercase=0				):

     '''simple docstring'''


     return sorted(_lowercase			,						key=lambda _lowercase				: x[column]				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(points_counts - 1				):
          for j in range(i + 1			,						_lowercase				):
               UpperCAmelCase_      :							Optional[int]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[Any]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=float('''inf'''				)				):

     '''simple docstring'''


     for i in range(min(6			,						points_counts - 1				)			,						_lowercase				):
          for j in range(max(0			,						i - 6				)			,						_lowercase				):
               UpperCAmelCase_      :							List[str]       =      euclidean_distance_sqr(points[i]			,						points[j]				)
               if current_dis < min_dis:
                    UpperCAmelCase_      :							Optional[int]       =      current_dis
     return min_dis







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase			,						_lowercase				)

     # recursion
     UpperCAmelCase_      :							Optional[int]       =      points_counts // 2
     UpperCAmelCase_      :							List[Any]       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[:mid]			,						_lowercase				)
     UpperCAmelCase_      :							Dict       =      closest_pair_of_points_sqr(
         _lowercase			,						points_sorted_on_y[mid:]			,						points_counts - mid				)
     UpperCAmelCase_      :							Union[str, Any]       =      min(_lowercase			,						_lowercase				)
     UpperCAmelCase_      :							str       =      []
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0]				) < closest_pair_dis:
               cross_strip.append(_lowercase				)

     UpperCAmelCase_      :							Optional[Any]       =      dis_between_closest_in_strip(
         _lowercase			,						len(_lowercase				)			,						_lowercase				)
     return min(_lowercase			,						_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      column_based_sort(_lowercase			,						column=0				)
     UpperCAmelCase_      :							List[Any]       =      column_based_sort(_lowercase			,						column=1				)
     return (
         closest_pair_of_points_sqr(
             _lowercase			,						_lowercase			,						_lowercase				)
     ) ** 0.5


if __name__ == "__main__":
     __a          =			[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))