


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =KandinskyVaaControlnetPipeline
				snake_case	    =['image_embeds', 'negative_image_embeds', 'hint']
				snake_case	    =['image_embeds', 'negative_image_embeds', 'hint']
				snake_case	    =[
				    'generator',
				    'height',
				    'width',
				    'latents',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				snake_case	    =False





				@property
				def 						__UpperCamelCase    (				self)  -> Any:
											return 32





				@property
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											return 32





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.time_input_dim





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





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return 100





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											torch.manual_seed(0)

											a__      ={
											    'in_channels': 8,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 8,
											    'addition_embed_type': 'image_hint',
											    '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,
											}

											a__      =UNetaDConditionModel(**lowercase_)
											return model





				@property
				def 						__UpperCamelCase    (				self)  -> Tuple:
											return {
											    "block_out_channels": [32, 32, 64, 64],
											    "down_block_types": [
											        "DownEncoderBlock2D",
											        "DownEncoderBlock2D",
											        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
											    "vq_embed_dim": 4,
											}





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											torch.manual_seed(0)
											a__      =VQModel(**self.dummy_movq_kwargs)
											return model





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.dummy_unet
											a__      =self.dummy_movq

											a__      =DDIMScheduler(
											    num_train_timesteps=1000       ,  beta_schedule='linear'       ,  beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  clip_sample=lowercase_       ,  set_alpha_to_one=lowercase_       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=lowercase_       ,  )

											a__      ={
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}
											return components





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=0)  -> Optional[int]:
											a__      =floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(lowercase_)).to(lowercase_)
											a__      =floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1)).to(
											    lowercase_)

											# create hint
											a__      =floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(lowercase_)).to(lowercase_)

											if str(lowercase_).startswith('mps'):
																		a__      =torch.manual_seed(lowercase_)
											else:
																		a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      ={
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'hint': hint,
											    'generator': generator,
											    'height': 64,
											    'width': 64,
											    'guidance_scale': 4.0,
											    'num_inference_steps': 2,
											    'output_type': 'np',
											}
											return inputs





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      ='cpu'

											a__      =self.get_dummy_components()

											a__      =self.pipeline_class(**lowercase_)
											a__      =pipe.to(lowercase_)

											pipe.set_progress_bar_config(disable=lowercase_)

											a__      =pipe(**self.get_dummy_inputs(lowercase_))
											a__      =output.images

											a__      =pipe(
											    **self.get_dummy_inputs(lowercase_)       ,  return_dict=lowercase_       ,  )[0]

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

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

											a__      =np.array(
											    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95])

											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()}"""







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





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy')

											a__      =load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/hint_image_cat.png')
											a__      =torch.from_numpy(np.array(lowercase_)).float() / 2_55.0
											a__      =hint.permute(2       ,  0       ,  1).unsqueeze(0)

											a__      =KandinskyVaaPriorPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-prior'       ,  torch_dtype=torch.floataa)
											pipe_prior.to(lowercase_)

											a__      =KandinskyVaaControlnetPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-controlnet-depth'       ,  torch_dtype=torch.floataa)
											a__      =pipeline.to(lowercase_)
											pipeline.set_progress_bar_config(disable=lowercase_)

											a__      ='A robot, 4k photo'

											a__      =torch.Generator(device='cuda').manual_seed(0)
											a__    ,     a__      =pipe_prior(
											    lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  negative_prompt=''       ,  ).to_tuple()

											a__      =torch.Generator(device='cuda').manual_seed(0)
											a__      =pipeline(
											    image_embeds=lowercase_       ,  negative_image_embeds=lowercase_       ,  hint=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=100       ,  output_type='np'       ,  )

											a__      =output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(lowercase_       ,  lowercase_)



from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
 import PIL


_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					b.T
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=1			)
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=0			)
     UpperCamelCase						    =					np.matmul(_lowercase						,     _lowercase			)
     UpperCamelCase						    =					aa[:, None] - 2 * ab + ba[None, :]
     return d

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					x.reshape(-1						,     3			)
     UpperCamelCase						    =					squared_euclidean_distance(_lowercase						,     _lowercase			)
     return np.argmin(_lowercase						,     axis=1			)







class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			bool = True					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'height': 2_56, 'width': 2_56}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   ) if clusters is not None else None
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					do_color_quantize

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      if "height" not in size or "width" not in size:
           raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'   )
      return resize(
          SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, resample=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, ):






      """simple docstring"""



      UpperCamelCase						    =					rescale(image=SCREAMING_SNAKE_CASE__					, scale=1 / 127.5					, data_format=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					image - 1
      return image






 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			ImageInput					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = None					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, TensorType]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



      UpperCamelCase						    =					do_resize if do_resize is not None else self.do_resize
      UpperCamelCase						    =					size if size is not None else self.size
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					resample if resample is not None else self.resample
      UpperCamelCase						    =					do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase						    =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCamelCase						    =					clusters if clusters is not None else self.clusters
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					make_list_of_images(SCREAMING_SNAKE_CASE__   )

      if not valid_images(SCREAMING_SNAKE_CASE__   ):
           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_color_quantize and clusters is None:
           raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

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

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

      if do_normalize:
           UpperCamelCase						    =					[self.normalize(image=SCREAMING_SNAKE_CASE__   ) for image in images]

      if do_color_quantize:
           UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, ChannelDimension.LAST   ) for image in images]
           # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
           UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					color_quantize(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).reshape(images.shape[:-1]   )

           # flatten to (batch_size, height*width)
           UpperCamelCase						    =					images.shape[0]
           UpperCamelCase						    =					images.reshape(SCREAMING_SNAKE_CASE__					, -1   )

           # We need to convert back to a list of images to keep consistent behaviour across processors.
           UpperCamelCase						    =					list(SCREAMING_SNAKE_CASE__   )
      else:
           UpperCamelCase						    =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for image in images]

      UpperCamelCase						    =					{'input_ids': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )


