
"""simple docstring"""






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__					(						A__   ,							unittest.TestCase       ):
     UpperCAmelCase__         =      KandinskyVaaInpaintPipeline
     UpperCAmelCase__         =      ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
     UpperCAmelCase__         =      [
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
         '''mask_image''',
     ]
     UpperCAmelCase__         =      [
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     UpperCAmelCase__         =      False



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







      '''simple docstring'''



      return 32



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







      '''simple docstring'''



      return 32



     @property
     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return self.time_input_dim



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







      '''simple docstring'''



      return self.time_input_dim * 4



     @property
     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return 100



     @property
     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      torch.manual_seed(0   )

      UpperCamelCase_     :					Optional[int]   	={
          '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,
      }

      UpperCamelCase_     :					Dict   	=UNetaDConditionModel(**_lowerCamelCase   )
      return model



     @property
     def  lowerCamelCase_						(							self :Optional[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  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      torch.manual_seed(0   )
      UpperCamelCase_     :					List[str]   	=VQModel(**self.dummy_movq_kwargs   )
      return model



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=self.dummy_unet
      UpperCamelCase_     :					List[Any]   	=self.dummy_movq

      UpperCamelCase_     :					Union[str, Any]   	=DDIMScheduler(
          num_train_timesteps=1_000      ,  beta_schedule='linear'      ,  beta_start=0.00085      ,  beta_end=0.012      ,  clip_sample=_lowerCamelCase      ,  set_alpha_to_one=_lowerCamelCase      ,  steps_offset=1      ,  prediction_type='epsilon'      ,  thresholding=_lowerCamelCase      ,  )

      UpperCamelCase_     :					Optional[int]   	={
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :Optional[Any]=0   ):







      '''simple docstring'''



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

      if str(_lowerCamelCase   ).startswith('mps'   ):
       UpperCamelCase_     :					Optional[Any]   	=torch.manual_seed(_lowerCamelCase   )
      else:
       UpperCamelCase_     :					Optional[int]   	=torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
      UpperCamelCase_     :					str   	={
          '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  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	='cpu'

      UpperCamelCase_     :					Optional[int]   	=self.get_dummy_components()

      UpperCamelCase_     :					Any   	=self.pipeline_class(**_lowerCamelCase   )
      UpperCamelCase_     :					Dict   	=pipe.to(_lowerCamelCase   )

      pipe.set_progress_bar_config(disable=_lowerCamelCase   )

      UpperCamelCase_     :					Any   	=pipe(**self.get_dummy_inputs(_lowerCamelCase   )   )
      UpperCamelCase_     :					int   	=output.images

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

      UpperCamelCase_     :					Any   	=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_     :					Union[str, Any]   	=np.array(
          [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]   )

      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  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



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





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



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'   )

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

      UpperCamelCase_     :					Tuple   	='a hat'

      UpperCamelCase_     :					Optional[int]   	=KandinskyVaaPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-prior'      ,  torch_dtype=torch.floataa   )
      pipe_prior.to(_lowerCamelCase   )

      UpperCamelCase_     :					List[Any]   	=KandinskyVaaInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,  torch_dtype=torch.floataa   )
      UpperCamelCase_     :					Dict   	=pipeline.to(_lowerCamelCase   )
      pipeline.set_progress_bar_config(disable=_lowerCamelCase   )

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

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

      UpperCamelCase_     :					Tuple   	=output.images[0]

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

      assert_mean_pixel_difference(_lowerCamelCase      ,  _lowerCamelCase   )


"""simple docstring"""






from manim import *





class 				a__					(						A__       ):



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=Rectangle(height=0.5      ,  width=0.5   )
      UpperCamelCase_     :					Any   	=Rectangle(height=0.46      ,  width=0.46   ).set_stroke(width=0   )

      UpperCamelCase_     :					Any   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					Optional[int]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					List[str]   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					Optional[int]   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					Union[str, Any]   	=VGroup(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					str   	=Text('CPU'      ,  font_size=24   )
      UpperCamelCase_     :					str   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      cpu.move_to([-2.5, -0.5, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[mem.copy() for i in range(4   )]
      UpperCamelCase_     :					Any   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )
      UpperCamelCase_     :					List[Any]   	=Text('GPU'      ,  font_size=24   )
      UpperCamelCase_     :					Any   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      gpu.move_to([-1, -1, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					str   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )

      UpperCamelCase_     :					Optional[int]   	=Text('Model'      ,  font_size=24   )
      UpperCamelCase_     :					List[Any]   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0.5      ,  aligned_edge=_lowerCamelCase   )
      model.move_to([3, -1.0, 0]   )
      self.add(_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[]
      for i, rect in enumerate(_lowerCamelCase   ):
       rect.set_stroke(_lowerCamelCase   )
       # target = fill.copy().set_fill(YELLOW, opacity=0.7)
       # target.move_to(rect)
       # self.add(target)

       UpperCamelCase_     :					str   	=Rectangle(height=0.46 / 4      ,  width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(_lowerCamelCase      ,  opacity=0.7   )

       if i == 0:
        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )      ,  buff=0.02      ,  direction=_lowerCamelCase   )
        cpu_target.set_x(cpu_target.get_x() + 0.1   )
       elif i == 3:
        cpu_target.next_to(cpu_targs[0]      ,  direction=_lowerCamelCase      ,  buff=0.0   )
       else:
        cpu_target.next_to(cpu_targs[i - 1]      ,  direction=_lowerCamelCase      ,  buff=0.0   )
       self.add(_lowerCamelCase   )
       cpu_targs.append(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=[mem.copy() for i in range(6   )]
      UpperCamelCase_     :					Any   	=VGroup(*_lowerCamelCase   ).arrange(_lowerCamelCase      ,  buff=0   )

      UpperCamelCase_     :					Optional[int]   	=Text('Loaded Checkpoint'      ,  font_size=24   )
      UpperCamelCase_     :					List[Any]   	=Group(_lowerCamelCase      ,  _lowerCamelCase   ).arrange(_lowerCamelCase      ,  aligned_edge=_lowerCamelCase      ,  buff=0.4   )
      checkpoint.move_to([3, 0.5, 0]   )

      UpperCamelCase_     :					int   	=Square(side_length=2.2   )
      key.move_to([-5, 2, 0]   )

      UpperCamelCase_     :					Optional[Any]   	=MarkupText(
          f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''      ,  font_size=18      ,  )

      key_text.move_to([-5, 2.4, 0]   )

      self.add(_lowerCamelCase      ,  _lowerCamelCase   )

      UpperCamelCase_     :					Any   	=MarkupText(
          f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''      ,  font_size=18      ,  )

      blue_text.next_to(_lowerCamelCase      ,  DOWN * 2.4      ,  aligned_edge=key_text.get_left()   )

      UpperCamelCase_     :					Dict   	=MarkupText(
          f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'''      ,  font_size=24      ,  )
      step_a.move_to([2, 2, 0]   )
      self.play(Write(_lowerCamelCase   )      ,  Write(_lowerCamelCase   )   )

      self.play(Write(_lowerCamelCase      ,  run_time=1   )      ,  Create(_lowerCamelCase      ,  run_time=1   )   )

      UpperCamelCase_     :					Union[str, Any]   	=[]
      UpperCamelCase_     :					List[Any]   	=[]
      for i, rect in enumerate(_lowerCamelCase   ):
       UpperCamelCase_     :					str   	=fill.copy().set_fill(_lowerCamelCase      ,  opacity=0.7   )
       target.move_to(_lowerCamelCase   )
       first_animations.append(GrowFromCenter(_lowerCamelCase      ,  run_time=1   )   )

       UpperCamelCase_     :					Any   	=target.copy()
       cpu_target.generate_target()
       if i < 5:
        cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
       else:
        cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
       second_animations.append(MoveToTarget(_lowerCamelCase      ,  run_time=1.5   )   )

      self.play(*_lowerCamelCase   )
      self.play(*_lowerCamelCase   )
      self.wait()
