



'''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 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   KandinskyInpaintPipeline
    lowercase__			=   ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    lowercase__			=   [
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    lowercase__			=   [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    lowercase__			=   False







    @property
    def       lowercase_	(				self			:  List[Any]     ):
      return 32







    @property
    def       lowercase_	(				self			:  Optional[int]     ):
      return 32







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







    @property
    def       lowercase_	(				self			:  Optional[Any]     ):
      return self.time_input_dim * 4







    @property
    def       lowercase_	(				self			:  List[str]     ):
      return 1_00







    @property
    def       lowercase_	(				self			:  List[str]     ):
      a						: Any					       =					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'     )
      return tokenizer







    @property
    def       lowercase_	(				self			:  Optional[int]     ):
      torch.manual_seed(0     )
      a						: List[str]					       =					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=10_05						,      )

      a						: Union[str, Any]					       =					MultilingualCLIP(__snake_case     )
      a						: Tuple					       =					text_encoder.eval()

      return text_encoder







    @property
    def       lowercase_	(				self			:  int     ):
      torch.manual_seed(0     )

      a						: Tuple					       =					{
          '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,
      }

      a						: str					       =					UNetaDConditionModel(**__snake_case     )
      return model







    @property
    def       lowercase_	(				self			:  Union[str, Any]     ):
      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			:  str     ):
      torch.manual_seed(0     )
      a						: Any					       =					VQModel(**self.dummy_movq_kwargs     )
      return model







    def       lowercase_	(				self			:  Tuple     ):
      a						: int					       =					self.dummy_text_encoder
      a						: Optional[int]					       =					self.dummy_tokenizer
      a						: str					       =					self.dummy_unet
      a						: Union[str, Any]					       =					self.dummy_movq

      a						: Tuple					       =					DDIMScheduler(
          num_train_timesteps=10_00						,      beta_schedule='linear'						,      beta_start=0.00085						,      beta_end=0.012						,      clip_sample=__snake_case						,      set_alpha_to_one=__snake_case						,      steps_offset=1						,      prediction_type='epsilon'						,      thresholding=__snake_case						,      )

      a						: List[Any]					       =					{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[str]						,      __snake_case			:  int=0     ):
      a						: List[str]					       =					floats_tensor((1, self.cross_attention_dim)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: Tuple					       =					floats_tensor((1, self.cross_attention_dim)						,      rng=random.Random(seed + 1     )     ).to(__snake_case     )
      # create init_image
      a						: List[Any]					       =					floats_tensor((1, 3, 64, 64)						,      rng=random.Random(__snake_case     )     ).to(__snake_case     )
      a						: List[Any]					       =					image.cpu().permute(0						,      2						,      3						,      1     )[0]
      a						: Optional[int]					       =					Image.fromarray(np.uinta(__snake_case     )     ).convert('RGB'     ).resize((2_56, 2_56)     )
      # create mask
      a						: Any					       =					np.ones((64, 64)						,      dtype=np.floataa     )
      a						: Dict					       =					0

      if str(__snake_case     ).startswith('mps'     ):
        a						: Optional[Any]					       =					torch.manual_seed(__snake_case     )
      else:
        a						: Optional[Any]					       =					torch.Generator(device=__snake_case     ).manual_seed(__snake_case     )
      a						: Tuple					       =					{
          '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			:  Any     ):
      a						: List[str]					       =					'cpu'

      a						: List[Any]					       =					self.get_dummy_components()

      a						: Optional[int]					       =					self.pipeline_class(**__snake_case     )
      a						: Optional[int]					       =					pipe.to(__snake_case     )

      pipe.set_progress_bar_config(disable=__snake_case     )

      a						: Dict					       =					pipe(**self.get_dummy_inputs(__snake_case     )     )
      a						: List[Any]					       =					output.images

      a						: Any					       =					pipe(
          **self.get_dummy_inputs(__snake_case     )						,      return_dict=__snake_case						,      )[0]

      a						: int					       =					image[0, -3:, -3:, -1]
      a						: List[str]					       =					image_from_tuple[0, -3:, -3:, -1]

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

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

      a						: Any					       =					np.array(
          [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]     )

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

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







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







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

      a						: Tuple					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'     )
      a						: str					       =					np.ones((7_68, 7_68)						,      dtype=np.floataa     )
      a						: str					       =					0

      a						: Any					       =					'a hat'

      a						: Union[str, Any]					       =					KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'						,      torch_dtype=torch.floataa     )
      pipe_prior.to(__snake_case     )

      a						: str					       =					KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'						,      torch_dtype=torch.floataa     )
      a						: Optional[int]					       =					pipeline.to(__snake_case     )
      pipeline.set_progress_bar_config(disable=__snake_case     )

      a						: str					       =					torch.Generator(device='cpu'     ).manual_seed(0     )
      a						: List[Any]					       =					pipe_prior(
          __snake_case						,      generator=__snake_case						,      num_inference_steps=5						,      negative_prompt=''						,      ).to_tuple()

      a						: Union[str, Any]					       =					pipeline(
          __snake_case						,      image=__snake_case						,      mask_image=__snake_case						,      image_embeds=__snake_case						,      negative_image_embeds=__snake_case						,      generator=__snake_case						,      num_inference_steps=1_00						,      height=7_68						,      width=7_68						,      output_type='np'						,      )

      a						: List[str]					       =					output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(__snake_case						,      __snake_case     )




'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""pixel_values"""]







    def __init__(				self			:  List[str]						,      __snake_case			:  bool = True						,      __snake_case			:  int = 32						,      __snake_case			:  Union[str, Any]=PILImageResampling.BILINEAR						,      __snake_case			:  bool = True						,      **__snake_case			:  List[Any]						,      ):
      a						: Optional[Any]					       =					do_resize
      a						: Union[str, Any]					       =					do_rescale
      a						: Union[str, Any]					       =					size_divisor
      a						: List[Any]					       =					resample
      super().__init__(**__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  np.ndarray						,      __snake_case			:  int						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Tuple     ):
      a       ,     a						: Optional[int]					       =					get_image_size(__snake_case     )
      # Rounds the height and width down to the closest multiple of size_divisor
      a						: int					       =					height // size_divisor * size_divisor
      a						: int					       =					width // size_divisor * size_divisor
      a						: Any					       =					resize(__snake_case						,      (new_h, new_w)						,      resample=__snake_case						,      data_format=__snake_case						,      **__snake_case     )
      return image







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  np.ndarray						,      __snake_case			:  float						,      __snake_case			:  Optional[ChannelDimension] = None						,      **__snake_case			:  Optional[Any]     ):
      return rescale(image=__snake_case						,      scale=__snake_case						,      data_format=__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[Union[TensorType, str]] = None						,      __snake_case			:  ChannelDimension = ChannelDimension.FIRST						,      **__snake_case			:  Any						,      ):
      a						: List[str]					       =					do_resize if do_resize is not None else self.do_resize
      a						: Tuple					       =					do_rescale if do_rescale is not None else self.do_rescale
      a						: Optional[Any]					       =					size_divisor if size_divisor is not None else self.size_divisor
      a						: Union[str, Any]					       =					resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'     )

      a						: Tuple					       =					make_list_of_images(__snake_case     )

      if not valid_images(__snake_case     ):
        raise ValueError('Invalid image(s)'     )

      # All transformations expect numpy arrays.
      a						: str					       =					[to_numpy_array(__snake_case     ) for img in images]

      if do_resize:
        a						: int					       =					[self.resize(__snake_case						,      size_divisor=__snake_case						,      resample=__snake_case     ) for image in images]

      if do_rescale:
        a						: List[str]					       =					[self.rescale(__snake_case						,      scale=1 / 2_55     ) for image in images]

      a						: Any					       =					[to_channel_dimension_format(__snake_case						,      __snake_case     ) for image in images]

      a						: Any					       =					{'pixel_values': images}
      return BatchFeature(data=__snake_case						,      tensor_type=__snake_case     )