

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 	__magic_name__    (			a_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						KandinskyVaaControlnetImgaImgPipeline
			SCREAMING_SNAKE_CASE							   =						["image_embeds", "negative_image_embeds", "image", "hint"]
			SCREAMING_SNAKE_CASE							   =						["image_embeds", "negative_image_embeds", "image", "hint"]
			SCREAMING_SNAKE_CASE							   =						[
			    "generator",
			    "height",
			    "width",
			    "strength",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			SCREAMING_SNAKE_CASE							   =						False





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




						'''simple docstring'''
						return 32





			@property
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						return 32





			@property
			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						return self.time_input_dim





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




						'''simple docstring'''
						return self.time_input_dim * 4





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




						'''simple docstring'''
						return 100





			@property
			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						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(**_lowerCamelCase					)
						return model





			@property
			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						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 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =VQModel(**self.dummy_movq_kwargs					)
						return model





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =self.dummy_unet
						__a        =self.dummy_movq

						__a        ={
						    'num_train_timesteps': 1000,
						    'beta_schedule': 'linear',
						    'beta_start': 0.0_0085,
						    'beta_end': 0.012,
						    'clip_sample': False,
						    'set_alpha_to_one': False,
						    'steps_offset': 0,
						    'prediction_type': 'epsilon',
						    'thresholding': False,
						}

						__a        =DDIMScheduler(**_lowerCamelCase					)

						__a        ={
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Any:




						'''simple docstring'''
						__a        =floats_tensor((1, self.text_embedder_hidden_size)					,  rng=random.Random(_lowerCamelCase					)					).to(_lowerCamelCase					)
						__a        =floats_tensor((1, self.text_embedder_hidden_size)					,  rng=random.Random(seed + 1					)					).to(
						    _lowerCamelCase					)
						# create init_image
						__a        =floats_tensor((1, 3, 64, 64)					,  rng=random.Random(_lowerCamelCase					)					).to(_lowerCamelCase					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					)[0]
						__a        =Image.fromarray(np.uinta(_lowerCamelCase					)					).convert('RGB'					).resize((256, 256)					)
						# create hint
						__a        =floats_tensor((1, 3, 64, 64)					,  rng=random.Random(_lowerCamelCase					)					).to(_lowerCamelCase					)

						if str(_lowerCamelCase					).startswith('mps'					):
									__a        =torch.manual_seed(_lowerCamelCase					)
						else:
									__a        =torch.Generator(device=_lowerCamelCase					).manual_seed(_lowerCamelCase					)
						__a        ={
						    'image': init_image,
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'hint': hint,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'num_inference_steps': 10,
						    'guidance_scale': 7.0,
						    'strength': 0.2,
						    'output_type': 'np',
						}
						return inputs





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()

						__a        =self.pipeline_class(**_lowerCamelCase					)
						__a        =pipe.to(_lowerCamelCase					)

						pipe.set_progress_bar_config(disable=_lowerCamelCase					)

						__a        =pipe(**self.get_dummy_inputs(_lowerCamelCase					)					)
						__a        =output.images

						__a        =pipe(
						    **self.get_dummy_inputs(_lowerCamelCase					)					,  return_dict=_lowerCamelCase					,  )[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(
						    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]					)
						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 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




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





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'					)

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
						__a        =init_image.resize((512, 512)					)

						__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(_lowerCamelCase					)					).float() / 255.0
						__a        =hint.permute(2					,  0					,  1					).unsqueeze(0					)

						__a        ='A robot, 4k photo'

						__a        =KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'					,  torch_dtype=torch.floataa					)
						pipe_prior.to(_lowerCamelCase					)

						__a        =KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-controlnet-depth'					,  torch_dtype=torch.floataa					)
						__a        =pipeline.to(_lowerCamelCase					)

						pipeline.set_progress_bar_config(disable=_lowerCamelCase					)

						__a        =torch.Generator(device='cpu'					).manual_seed(0					)

						__a      ,    __a        =pipe_prior(
						    _lowerCamelCase					,  image=_lowerCamelCase					,  strength=0.85					,  generator=_lowerCamelCase					,  negative_prompt=''					,  ).to_tuple()

						__a        =pipeline(
						    image=_lowerCamelCase					,  image_embeds=_lowerCamelCase					,  negative_image_embeds=_lowerCamelCase					,  hint=_lowerCamelCase					,  generator=_lowerCamelCase					,  num_inference_steps=100					,  height=512					,  width=512					,  strength=0.5					,  output_type='np'					,  )

						__a        =output.images[0]

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

						assert_mean_pixel_difference(_lowerCamelCase					,  _lowerCamelCase					)














"""simple docstring"""



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor

class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							    =       size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       min_resolution
      lowercase							    =       max_resolution
      lowercase							    =       do_resize
      lowercase							    =       size
      lowercase							    =       do_normalize
      lowercase							    =       image_mean
      lowercase							    =       image_std







    def 			UpperCamelCase_      (			self       ):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[int]	     =DPTImageProcessor if is_vision_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       DPTImageProcessingTester(self       )







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}       )

      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2       )
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image       )

      # Test not batched input
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray       )

      # Test not batched input
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor       )

      # Test not batched input
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


