


'''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 			lowerCamelCase_   (	__a     ,       unittest.TestCase      ):
      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 lowercase_							(					self				:    Any ):


          '''simple docstring'''


          return 32






      @property
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          return 32






      @property
      def lowercase_							(					self				:    Optional[Any] ):


          '''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[Any] ):


          '''simple docstring'''


          return 100






      @property
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
          return tokenizer






      @property
      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          torch.manual_seed(0 )
          UpperCAmelCase__			:		Dict													=				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__			:		int													=				MultilingualCLIP(_A )
          UpperCAmelCase__			:		Union[str, Any]													=				text_encoder.eval()

          return text_encoder






      @property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          torch.manual_seed(0 )

          UpperCAmelCase__			:		List[str]													=				{
              '''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__			:		Optional[int]													=				UNetaDConditionModel(**_A )
          return model






      @property
      def lowercase_							(					self				:    Tuple ):


          '''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[int] ):


          '''simple docstring'''


          torch.manual_seed(0 )
          UpperCAmelCase__			:		Tuple													=				VQModel(**self.dummy_movq_kwargs )
          return model






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.dummy_text_encoder
          UpperCAmelCase__			:		List[Any]													=				self.dummy_tokenizer
          UpperCAmelCase__			:		List[str]													=				self.dummy_unet
          UpperCAmelCase__			:		int													=				self.dummy_movq

          UpperCAmelCase__			:		str													=				DDIMScheduler(
              num_train_timesteps=1_000						,						beta_schedule='''linear'''						,						beta_start=0.0_0_0_8_5						,						beta_end=0.0_1_2						,						clip_sample=_A						,						set_alpha_to_one=_A						,						steps_offset=1						,						prediction_type='''epsilon'''						,						thresholding=_A						,						)

          UpperCAmelCase__			:		List[str]													=				{
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components






      def lowercase_							(					self				:    List[Any]						,						_A				:    Any						,						_A				:    Union[str, Any]=0 ):


          '''simple docstring'''


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

          if str(_A ).startswith('''mps''' ):
              UpperCAmelCase__			:		str													=				torch.manual_seed(_A )
          else:
              UpperCAmelCase__			:		Dict													=				torch.Generator(device=_A ).manual_seed(_A )
          UpperCAmelCase__			:		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				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				'''cpu'''

          UpperCAmelCase__			:		str													=				self.get_dummy_components()

          UpperCAmelCase__			:		Dict													=				self.pipeline_class(**_A )
          UpperCAmelCase__			:		List[Any]													=				pipe.to(_A )

          pipe.set_progress_bar_config(disable=_A )

          UpperCAmelCase__			:		str													=				pipe(**self.get_dummy_inputs(_A ) )
          UpperCAmelCase__			:		List[Any]													=				output.images

          UpperCAmelCase__			:		Optional[Any]													=				pipe(
              **self.get_dummy_inputs(_A )						,						return_dict=_A						,						)[0]

          UpperCAmelCase__			:		Tuple													=				image[0, -3:, -3:, -1]
          UpperCAmelCase__			:		Dict													=				image_from_tuple[0, -3:, -3:, -1]

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

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

          UpperCAmelCase__			:		List[Any]													=				np.array(
              [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6] )

          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				:    Tuple ):


          '''simple docstring'''


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




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






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )

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

          UpperCAmelCase__			:		Union[str, Any]													=				'''a hat'''

          UpperCAmelCase__			:		Optional[Any]													=				KandinskyPriorPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1-prior'''						,						torch_dtype=torch.floataa )
          pipe_prior.to(_A )

          UpperCAmelCase__			:		str													=				KandinskyInpaintPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1-inpaint'''						,						torch_dtype=torch.floataa )
          UpperCAmelCase__			:		List[str]													=				pipeline.to(_A )
          pipeline.set_progress_bar_config(disable=_A )

          UpperCAmelCase__			:		str													=				torch.Generator(device='''cpu''' ).manual_seed(0 )
          UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				pipe_prior(
              _A						,						generator=_A						,						num_inference_steps=5						,						negative_prompt=''''''						,						).to_tuple()

          UpperCAmelCase__			:		Any													=				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'''						,						)

          UpperCAmelCase__			:		List[Any]													=				output.images[0]

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

          assert_mean_pixel_difference(_A						,						_A )




'''simple docstring'''
def 					a__       (					lowerCAmelCase__						)    ->	int:
    UpperCAmelCase__			:		Tuple													=				0
    while num > 0:
        digit_sum += num % 10
        num //= 10
    return digit_sum





def 					a__       (					lowerCAmelCase__ = 1_00						)    ->	int:
    UpperCAmelCase__			:		Dict													=				1
    UpperCAmelCase__			:		str													=				2

    for i in range(2  ,					max_n + 1						):
        UpperCAmelCase__			:		Tuple													=				pre_numerator
        UpperCAmelCase__			:		Tuple													=				2 * i // 3 if i % 3 == 0 else 1
        UpperCAmelCase__			:		str													=				cur_numerator
        UpperCAmelCase__			:		List[str]													=				e_cont * pre_numerator + temp

    return sum_digits(lowerCAmelCase__						)


if __name__ == "__main__":
  print(F"""{solution() = }""")
