

'''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 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[int]     	= KandinskyInpaintPipeline
  __lowerCAmelCase       :			Any     	= ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
  __lowerCAmelCase       :			List[Any]     	= [
      "prompt",
      "negative_prompt",
      "image_embeds",
      "negative_image_embeds",
      "image",
      "mask_image",
  ]
  __lowerCAmelCase       :			List[str]     	= [
      "generator",
      "height",
      "width",
      "latents",
      "guidance_scale",
      "negative_prompt",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  __lowerCAmelCase       :			Any     	= False
  @property
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    return 3_2
  @property
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    return 3_2
  @property
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    return self.time_input_dim
  @property
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    return self.time_input_dim * 4
  @property
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    return 1_0_0
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
    return tokenizer
  @property
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    torch.manual_seed(0  )
    UpperCAmelCase    				=						MCLIPConfig(
        numDims=self.cross_attention_dim ,			transformerDimensions=self.text_embedder_hidden_size ,			hidden_size=self.text_embedder_hidden_size ,			intermediate_size=3_7 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			vocab_size=1_0_0_5 ,			)

    UpperCAmelCase    				=						MultilingualCLIP(a_  )
    UpperCAmelCase    				=						text_encoder.eval()

    return text_encoder
  @property
  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    torch.manual_seed(0  )

    UpperCAmelCase    				=						{
        '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    				=						UNetaDConditionModel(**a_  )
    return model
  @property
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    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     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    torch.manual_seed(0  )
    UpperCAmelCase    				=						VQModel(**self.dummy_movq_kwargs  )
    return model
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						self.dummy_text_encoder
    UpperCAmelCase    				=						self.dummy_tokenizer
    UpperCAmelCase    				=						self.dummy_unet
    UpperCAmelCase    				=						self.dummy_movq

    UpperCAmelCase    				=						DDIMScheduler(
        num_train_timesteps=1_0_0_0 ,			beta_schedule='linear' ,			beta_start=0.0_0085 ,			beta_end=0.012 ,			clip_sample=a_ ,			set_alpha_to_one=a_ ,			steps_offset=1 ,			prediction_type='epsilon' ,			thresholding=a_ ,			)

    UpperCAmelCase    				=						{
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'unet': unet,
        'scheduler': scheduler,
        'movq': movq,
    }

    return components
  def     snake_case_      (  self ,			a_ ,			a_=0  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						floats_tensor((1, self.cross_attention_dim) ,			rng=random.Random(a_  )  ).to(a_  )
    UpperCAmelCase    				=						floats_tensor((1, self.cross_attention_dim) ,			rng=random.Random(seed + 1  )  ).to(a_  )
    # create init_image
    UpperCAmelCase    				=						floats_tensor((1, 3, 6_4, 6_4) ,			rng=random.Random(a_  )  ).to(a_  )
    UpperCAmelCase    				=						image.cpu().permute(0 ,			2 ,			3 ,			1  )[0]
    UpperCAmelCase    				=						Image.fromarray(np.uinta(a_  )  ).convert('RGB'  ).resize((2_5_6, 2_5_6)  )
    # create mask
    UpperCAmelCase    				=						np.ones((6_4, 6_4) ,			dtype=np.floataa  )
    UpperCAmelCase    				=						0

    if str(a_  ).startswith('mps'  ):
      UpperCAmelCase    				=						torch.manual_seed(a_  )
    else:
      UpperCAmelCase    				=						torch.Generator(device=a_  ).manual_seed(a_  )
    UpperCAmelCase    				=						{
        'prompt': 'horse',
        'image': init_image,
        'mask_image': mask,
        'image_embeds': image_embeds,
        'negative_image_embeds': negative_image_embeds,
        'generator': generator,
        'height': 6_4,
        'width': 6_4,
        'num_inference_steps': 2,
        'guidance_scale': 4.0,
        'output_type': 'np',
    }
    return inputs
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						'cpu'

    UpperCAmelCase    				=						self.get_dummy_components()

    UpperCAmelCase    				=						self.pipeline_class(**a_  )
    UpperCAmelCase    				=						pipe.to(a_  )

    pipe.set_progress_bar_config(disable=a_  )

    UpperCAmelCase    				=						pipe(**self.get_dummy_inputs(a_  )  )
    UpperCAmelCase    				=						output.images

    UpperCAmelCase    				=						pipe(
        **self.get_dummy_inputs(a_  ) ,			return_dict=a_ ,			)[0]

    UpperCAmelCase    				=						image[0, -3:, -3:, -1]
    UpperCAmelCase    				=						image_from_tuple[0, -3:, -3:, -1]

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

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

    UpperCAmelCase    				=						np.array(
        [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]  )

    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     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    super().test_inference_batch_single_identical(expected_max_diff=3E-3  )




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

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()





  def     snake_case_      (  self  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

    UpperCAmelCase    				=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
    UpperCAmelCase    				=						np.ones((7_6_8, 7_6_8) ,			dtype=np.floataa  )
    UpperCAmelCase    				=						0

    UpperCAmelCase    				=						'a hat'

    UpperCAmelCase    				=						KandinskyPriorPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-1-prior' ,			torch_dtype=torch.floataa  )
    pipe_prior.to(a_  )

    UpperCAmelCase    				=						KandinskyInpaintPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-1-inpaint' ,			torch_dtype=torch.floataa  )
    UpperCAmelCase    				=						pipeline.to(a_  )
    pipeline.set_progress_bar_config(disable=a_  )

    UpperCAmelCase    				=						torch.Generator(device='cpu'  ).manual_seed(0  )
    UpperCAmelCase				,   UpperCAmelCase    				=						pipe_prior(
        a_ ,			generator=a_ ,			num_inference_steps=5 ,			negative_prompt='' ,			).to_tuple()

    UpperCAmelCase    				=						pipeline(
        a_ ,			image=a_ ,			mask_image=a_ ,			image_embeds=a_ ,			negative_image_embeds=a_ ,			generator=a_ ,			num_inference_steps=1_0_0 ,			height=7_6_8 ,			width=7_6_8 ,			output_type='np' ,			)

    UpperCAmelCase    				=						output.images[0]

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

    assert_mean_pixel_difference(a_ ,			a_  )





'''simple docstring'''




from ..utils import DummyObject, requires_backends



class 							lowercase_						(		metaclass=a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= ["keras_nlp"]
  def __init__(  self ,			*a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    requires_backends(self ,			['keras_nlp']  )


