


'''simple docstring'''






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        __snake_case	(       _UpperCAmelCase     ,unittest.TestCase):







				"""simple docstring"""



				lowercase       			= KandinskyVaaControlnetImgaImgPipeline
				lowercase       			= ['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowercase       			= ['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowercase       			= [
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowercase       			= False

				@property
				def __lowercase       (       self   :  Optional[int]		)       ->  Any:
						return 32

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

				@property
				def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
						return self.time_input_dim

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

				@property
				def __lowercase       (       self   :  Dict		)       ->  Optional[int]:
						return 1_00

				@property
				def __lowercase       (       self   :  str		)       ->  int:
						torch.manual_seed(0		)

						lowerCAmelCase_      :				Tuple      =       {
						    """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,
						}

						lowerCAmelCase_      :				Optional[Any]      =       UNetaDConditionModel(**SCREAMING_SNAKE_CASE_		)
						return model

				@property
				def __lowercase       (       self   :  Optional[int]		)       ->  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 __lowercase       (       self   :  List[str]		)       ->  Any:
						torch.manual_seed(0		)
						lowerCAmelCase_      :				str      =       VQModel(**self.dummy_movq_kwargs		)
						return model

				def __lowercase       (       self   :  Dict		)       ->  Union[str, Any]:
						lowerCAmelCase_      :				Tuple      =       self.dummy_unet
						lowerCAmelCase_      :				Optional[int]      =       self.dummy_movq

						lowerCAmelCase_      :				List[str]      =       {
						    """num_train_timesteps""": 10_00,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.00_085,
						    """beta_end""": 0.012,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						lowerCAmelCase_      :				str      =       DDIMScheduler(**SCREAMING_SNAKE_CASE_		)

						lowerCAmelCase_      :				Any      =       {
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components

				def __lowercase       (       self   :  str   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Any=0		)       ->  List[str]:
						lowerCAmelCase_      :				Optional[Any]      =       floats_tensor((1, self.text_embedder_hidden_size)   ,						rng=random.Random(SCREAMING_SNAKE_CASE_		)		).to(SCREAMING_SNAKE_CASE_		)
						lowerCAmelCase_      :				List[Any]      =       floats_tensor((1, self.text_embedder_hidden_size)   ,						rng=random.Random(seed + 1		)		).to(
						    SCREAMING_SNAKE_CASE_		)
						# create init_image
						lowerCAmelCase_      :				Tuple      =       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(SCREAMING_SNAKE_CASE_		)		).to(SCREAMING_SNAKE_CASE_		)
						lowerCAmelCase_      :				List[str]      =       image.cpu().permute(0   ,						2   ,						3   ,						1		)[0]
						lowerCAmelCase_      :				List[str]      =       Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_		)		).convert("""RGB"""		).resize((2_56, 2_56)		)
						# create hint
						lowerCAmelCase_      :				List[str]      =       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(SCREAMING_SNAKE_CASE_		)		).to(SCREAMING_SNAKE_CASE_		)

						if str(SCREAMING_SNAKE_CASE_		).startswith("""mps"""		):
								lowerCAmelCase_      :				List[Any]      =       torch.manual_seed(SCREAMING_SNAKE_CASE_		)
						else:
								lowerCAmelCase_      :				Optional[Any]      =       torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(SCREAMING_SNAKE_CASE_		)
						lowerCAmelCase_      :				Union[str, Any]      =       {
						    """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 __lowercase       (       self   :  List[str]		)       ->  List[Any]:
						lowerCAmelCase_      :				Dict      =       """cpu"""

						lowerCAmelCase_      :				Tuple      =       self.get_dummy_components()

						lowerCAmelCase_      :				Optional[Any]      =       self.pipeline_class(**SCREAMING_SNAKE_CASE_		)
						lowerCAmelCase_      :				List[str]      =       pipe.to(SCREAMING_SNAKE_CASE_		)

						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

						lowerCAmelCase_      :				Optional[Any]      =       pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_		)		)
						lowerCAmelCase_      :				int      =       output.images

						lowerCAmelCase_      :				Dict      =       pipe(
						    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_		)   ,						return_dict=SCREAMING_SNAKE_CASE_   ,						)[0]

						lowerCAmelCase_      :				Union[str, Any]      =       image[0, -3:, -3:, -1]
						lowerCAmelCase_      :				List[Any]      =       image_from_tuple[0, -3:, -3:, -1]

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

						lowerCAmelCase_      :				Tuple      =       np.array(
						    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]		)
						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        __snake_case	(       unittest.TestCase):







				"""simple docstring"""



				def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





				def __lowercase       (       self   :  List[str]		)       ->  Optional[int]:
						lowerCAmelCase_      :				Dict      =       load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""		)

						lowerCAmelCase_      :				str      =       load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""		)
						lowerCAmelCase_      :				List[Any]      =       init_image.resize((5_12, 5_12)		)

						lowerCAmelCase_      :				Dict      =       load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/hint_image_cat.png"""		)
						lowerCAmelCase_      :				int      =       torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_		)		).float() / 255.0
						lowerCAmelCase_      :				List[str]      =       hint.permute(2   ,						0   ,						1		).unsqueeze(0		)

						lowerCAmelCase_      :				Any      =       """A robot, 4k photo"""

						lowerCAmelCase_      :				List[str]      =       KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,						torch_dtype=torch.floataa		)
						pipe_prior.to(SCREAMING_SNAKE_CASE_		)

						lowerCAmelCase_      :				Tuple      =       KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-controlnet-depth"""   ,						torch_dtype=torch.floataa		)
						lowerCAmelCase_      :				int      =       pipeline.to(SCREAMING_SNAKE_CASE_		)

						pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

						lowerCAmelCase_      :				List[Any]      =       torch.Generator(device="""cpu"""		).manual_seed(0		)

						lowerCAmelCase_      :				str      =       pipe_prior(
						    SCREAMING_SNAKE_CASE_   ,						image=SCREAMING_SNAKE_CASE_   ,						strength=0.85   ,						generator=SCREAMING_SNAKE_CASE_   ,						negative_prompt=""""""   ,						).to_tuple()

						lowerCAmelCase_      :				Tuple      =       pipeline(
						    image=SCREAMING_SNAKE_CASE_   ,						image_embeds=SCREAMING_SNAKE_CASE_   ,						negative_image_embeds=SCREAMING_SNAKE_CASE_   ,						hint=SCREAMING_SNAKE_CASE_   ,						generator=SCREAMING_SNAKE_CASE_   ,						num_inference_steps=1_00   ,						height=5_12   ,						width=5_12   ,						strength=0.5   ,						output_type="""np"""   ,						)

						lowerCAmelCase_      :				Any      =       output.images[0]

						assert image.shape == (5_12, 5_12, 3)

						assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_   ,						SCREAMING_SNAKE_CASE_		)







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



