





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     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		Tuple			 =	KandinskyInpaintPipeline
   _UpperCamelCase   :		Optional[Any]			 =	['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   _UpperCamelCase   :		Optional[int]			 =	[
       'prompt',
       'negative_prompt',
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   _UpperCamelCase   :		Tuple			 =	[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   _UpperCamelCase   :		Any			 =	False


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Union[str, Any]:





     """simple docstring"""







     return 32


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       int:





     """simple docstring"""







     return 32


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Dict:





     """simple docstring"""







     return self.time_input_dim


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[Any]:





     """simple docstring"""







     return self.time_input_dim * 4


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[Any]:





     """simple docstring"""







     return 100


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       str:





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Dict:





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  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      ,					)

     lowercase__           =  MultilingualCLIP(a	)
     lowercase__           =  text_encoder.eval()

     return text_encoder


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       List[Any]:





     """simple docstring"""







     torch.manual_seed(0	)

     lowercase__           =  {
         '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,
     }

     lowercase__           =  UNetaDConditionModel(**a	)
     return model


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Any:





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       List[str]:





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  VQModel(**self.dummy_movq_kwargs	)
     return model


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       str:





     """simple docstring"""







     lowercase__           =  self.dummy_text_encoder
     lowercase__           =  self.dummy_tokenizer
     lowercase__           =  self.dummy_unet
     lowercase__           =  self.dummy_movq

     lowercase__           =  DDIMScheduler(
         num_train_timesteps=1_000      ,					beta_schedule='linear'      ,					beta_start=0.00085      ,					beta_end=0.012      ,					clip_sample=a      ,					set_alpha_to_one=a      ,					steps_offset=1      ,					prediction_type='epsilon'      ,					thresholding=a      ,					)

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

     return components


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]      ,					a      :    List[str]=0	)->       str:





     """simple docstring"""







     lowercase__           =  floats_tensor((1, self.cross_attention_dim)      ,					rng=random.Random(a	)	).to(a	)
     lowercase__           =  floats_tensor((1, self.cross_attention_dim)      ,					rng=random.Random(seed + 1	)	).to(a	)
     # create init_image
     lowercase__           =  floats_tensor((1, 3, 64, 64)      ,					rng=random.Random(a	)	).to(a	)
     lowercase__           =  image.cpu().permute(0      ,					2      ,					3      ,					1	)[0]
     lowercase__           =  Image.fromarray(np.uinta(a	)	).convert('RGB'	).resize((256, 256)	)
     # create mask
     lowercase__           =  np.ones((64, 64)      ,					dtype=np.floataa	)
     lowercase__           =  0

     if str(a	).startswith('mps'	):
       lowercase__           =  torch.manual_seed(a	)
     else:
       lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
     lowercase__           =  {
         '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 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       int:





     """simple docstring"""







     lowercase__           =  'cpu'

     lowercase__           =  self.get_dummy_components()

     lowercase__           =  self.pipeline_class(**a	)
     lowercase__           =  pipe.to(a	)

     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  pipe(**self.get_dummy_inputs(a	)	)
     lowercase__           =  output.images

     lowercase__           =  pipe(
         **self.get_dummy_inputs(a	)      ,					return_dict=a      ,					)[0]

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

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

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

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Dict:





     """simple docstring"""







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






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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Dict:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[Any]:





     """simple docstring"""







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

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

     lowercase__           =  'a hat'

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

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

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

     lowercase__           =  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'      ,					)

     lowercase__           =  output.images[0]

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

     assert_mean_pixel_difference(a      ,					a	)









import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple[int, int]:
  def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=0		,						_SCREAMING_SNAKE_CASE=None		):
    lowercase__           =  round(val / multiple		) * multiple

    if max_val is not None and x > max_val:
      lowercase__           =  math.floor(val / multiple		) * multiple

    if x < min_val:
      lowercase__           =  math.ceil(val / multiple		) * multiple

    return x

  lowercase__           =  (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) else output_size

  lowercase__      ,   lowercase__           =  get_image_size(_SCREAMING_SNAKE_CASE		)
  lowercase__      ,   lowercase__           =  output_size

  # determine new height and width
  lowercase__           =  output_height / input_height
  lowercase__           =  output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width		) < abs(1 - scale_height		):
      # fit width
      lowercase__           =  scale_width
    else:
      # fit height
      lowercase__           =  scale_height

  lowercase__           =  constraint_to_multiple_of(scale_height * input_height		,						multiple=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  constraint_to_multiple_of(scale_width * input_width		,						multiple=_SCREAMING_SNAKE_CASE		)

  return (new_height, new_width)


class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Dict			 =	['pixel_values']


   def __init__(   self      :    Any      ,					a      :    bool = True      ,					a      :    Dict[str, int] = None      ,					a      :    PILImageResampling = PILImageResampling.BILINEAR      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    bool = True      ,					a      :    Union[int, float] = 1 / 255      ,					a      :    bool = True      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					**a      :    Tuple      ,					)->       None:





     """simple docstring"""







     super().__init__(**a	)
     lowercase__           =  size if size is not None else {'height': 384, 'width': 384}
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  do_resize
     lowercase__           =  size
     lowercase__           =  keep_aspect_ratio
     lowercase__           =  ensure_multiple_of
     lowercase__           =  resample
     lowercase__           =  do_rescale
     lowercase__           =  rescale_factor
     lowercase__           =  do_normalize
     lowercase__           =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowercase__           =  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    np.ndarray      ,					a      :    Dict[str, int]      ,					a      :    bool = False      ,					a      :    int = 1      ,					a      :    PILImageResampling = PILImageResampling.BICUBIC      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[Any]      ,					)->       np.ndarray:





     """simple docstring"""







     lowercase__           =  get_size_dict(a	)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""	)
     lowercase__           =  get_resize_output_image_size(
         a      ,					output_size=(size['height'], size['width'])      ,					keep_aspect_ratio=a      ,					multiple=a      ,					)
     return resize(a      ,					size=a      ,					resample=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    np.ndarray      ,					a      :    Union[int, float]      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Dict      ,					)->       str:





     """simple docstring"""







     return rescale(a      ,					scale=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    np.ndarray      ,					a      :    Union[float, List[float]]      ,					a      :    Union[float, List[float]]      ,					a      :    Optional[Union[str, ChannelDimension]] = None      ,					**a      :    Optional[int]      ,					)->       np.ndarray:





     """simple docstring"""







     return normalize(a      ,					mean=a      ,					std=a      ,					data_format=a      ,					**a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    ImageInput      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    bool = None      ,					a      :    int = None      ,					a      :    PILImageResampling = None      ,					a      :    bool = None      ,					a      :    float = None      ,					a      :    bool = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[float, List[float]]] = None      ,					a      :    Optional[Union[str, TensorType]] = None      ,					a      :    ChannelDimension = ChannelDimension.FIRST      ,					**a      :    str      ,					)->       PIL.Image.Image:





     """simple docstring"""







     lowercase__           =  do_resize if do_resize is not None else self.do_resize
     lowercase__           =  size if size is not None else self.size
     lowercase__           =  get_size_dict(a	)
     lowercase__           =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowercase__           =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowercase__           =  resample if resample is not None else self.resample
     lowercase__           =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__           =  rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__           =  do_normalize if do_normalize is not None else self.do_normalize
     lowercase__           =  image_mean if image_mean is not None else self.image_mean
     lowercase__           =  image_std if image_std is not None else self.image_std

     lowercase__           =  make_list_of_images(a	)

     if not valid_images(a	):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'	)

     if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

     # All transformations expect numpy arrays.
     lowercase__           =  [to_numpy_array(a	) for image in images]

     if do_resize:
       lowercase__           =  [self.resize(image=a      ,					size=a      ,					resample=a	) for image in images]

     if do_rescale:
       lowercase__           =  [self.rescale(image=a      ,					scale=a	) for image in images]

     if do_normalize:
       lowercase__           =  [self.normalize(image=a      ,					mean=a      ,					std=a	) for image in images]

     lowercase__           =  [to_channel_dimension_format(a      ,					a	) for image in images]

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    str      ,					a      :    List[Tuple] = None	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
       if len(a	) != len(a	):
         raise ValueError(
             'Make sure that you pass in as many target sizes as the batch dimension of the logits'	)

       if is_torch_tensor(a	):
         lowercase__           =  target_sizes.numpy()

       lowercase__           =  []

       for idx in range(len(a	)	):
         lowercase__           =  torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0	)      ,					size=target_sizes[idx]      ,					mode='bilinear'      ,					align_corners=a	)
         lowercase__           =  resized_logits[0].argmax(dim=0	)
         semantic_segmentation.append(a	)
     else:
       lowercase__           =  logits.argmax(dim=1	)
       lowercase__           =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

     return semantic_segmentation


