
"""simple docstring"""




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
lowerCAmelCase__							=				logging.get_logger('transformers.models.encodec')

lowerCAmelCase__							=				{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
lowerCAmelCase__							=				{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
lowerCAmelCase__							=				{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
lowerCAmelCase__							=				{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
lowerCAmelCase__							=				{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
lowerCAmelCase__							=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCAmelCase__							=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCAmelCase__							=				[]
lowerCAmelCase__							=				[]



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):
		for attribute in key.split('''.'''    ):
				SCREAMING_SNAKE_CASE_						 = getattr(__a,							__a    )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE_						 = getattr(__a,							__a    ).shape
		else:
				SCREAMING_SNAKE_CASE_						 = hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
				    F' {value.shape} for {full_name}'    )

		if weight_type == "weight":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "running_mean":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "running_var":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "num_batches_tracked":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_ih_l0":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_hh_l0":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "bias_ih_l0":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "bias_hh_l0":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_ih_l1":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_hh_l1":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "bias_ih_l1":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "bias_hh_l1":
				SCREAMING_SNAKE_CASE_						 = value
		else:
				SCREAMING_SNAKE_CASE_						 = value

		logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'    )



def        _lowerCamelCase ( __a,							__a    ):
		for key in ignore_keys:
				if key.endswith('''.*'''    ):
						if name.startswith(key[:-1]    ):
								return True
				elif ".*." in key:
						SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = key.split('''.*.'''    )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False



def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = []

		if model_name == "encodec_24khz" or "encodec_32khz":
				SCREAMING_SNAKE_CASE_						 = MAPPING_24K
		elif model_name == "encodec_48khz":
				SCREAMING_SNAKE_CASE_						 = MAPPING_48K
		else:
				raise ValueError(F'Unsupported model: {model_name}'    )

		for name, value in orig_dict.items():
				if should_ignore(__a,							__a    ):
						logger.info(F'{name} was ignored'    )
						continue

				SCREAMING_SNAKE_CASE_						 = False
				for key, mapped_key in MAPPING.items():
						if "*" in key:
								SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = key.split('''.*.'''    )
								if prefix in name and suffix in name:
										SCREAMING_SNAKE_CASE_						 = suffix

						if key in name:
								# HACK otherwise .embed gets initialized with .embed_avg too
								if key.endswith('''embed'''    ) and name.endswith('''embed_avg'''    ):
										continue

								SCREAMING_SNAKE_CASE_						 = True
								if "*" in mapped_key:
										SCREAMING_SNAKE_CASE_						 = name.split(__a    )[0].split('''.'''    )[-2]
										SCREAMING_SNAKE_CASE_						 = mapped_key.replace('''*''',							__a    )
								if "weight_g" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight_g'''
								elif "weight_v" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight_v'''
								elif "weight_ih_l0" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight_ih_l0'''
								elif "weight_hh_l0" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight_hh_l0'''
								elif "bias_ih_l0" in name:
										SCREAMING_SNAKE_CASE_						 = '''bias_ih_l0'''
								elif "bias_hh_l0" in name:
										SCREAMING_SNAKE_CASE_						 = '''bias_hh_l0'''
								elif "weight_ih_l1" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight_ih_l1'''
								elif "weight_hh_l1" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight_hh_l1'''
								elif "bias_ih_l1" in name:
										SCREAMING_SNAKE_CASE_						 = '''bias_ih_l1'''
								elif "bias_hh_l1" in name:
										SCREAMING_SNAKE_CASE_						 = '''bias_hh_l1'''
								elif "bias" in name:
										SCREAMING_SNAKE_CASE_						 = '''bias'''
								elif "weight" in name:
										SCREAMING_SNAKE_CASE_						 = '''weight'''
								elif "running_mean" in name:
										SCREAMING_SNAKE_CASE_						 = '''running_mean'''
								elif "running_var" in name:
										SCREAMING_SNAKE_CASE_						 = '''running_var'''
								elif "num_batches_tracked" in name:
										SCREAMING_SNAKE_CASE_						 = '''num_batches_tracked'''
								else:
										SCREAMING_SNAKE_CASE_						 = None
								set_recursively(__a,							__a,							__a,							__a,							__a    )
						continue
				if not is_used:
						unused_weights.append(__a    )

		logger.warning(F'Unused weights: {unused_weights}'    )



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a,							__a=None,							__a=None,							):
		if config_path is not None:
				SCREAMING_SNAKE_CASE_						 = EncodecConfig.from_pretrained(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = EncodecConfig()

		if model_name == "encodec_24khz":
				pass  # config is already correct
		elif model_name == "encodec_32khz":
				SCREAMING_SNAKE_CASE_						 = [8, 5, 4, 4]
				SCREAMING_SNAKE_CASE_						 = [2.2]
				SCREAMING_SNAKE_CASE_						 = 64
				SCREAMING_SNAKE_CASE_						 = 32_000
				SCREAMING_SNAKE_CASE_						 = 2_048
				SCREAMING_SNAKE_CASE_						 = False
				SCREAMING_SNAKE_CASE_						 = False
				SCREAMING_SNAKE_CASE_						 = False
		elif model_name == "encodec_48khz":
				SCREAMING_SNAKE_CASE_						 = [8, 5, 4, 2]
				SCREAMING_SNAKE_CASE_						 = [3.0, 6.0, 1_2.0, 2_4.0]
				SCREAMING_SNAKE_CASE_						 = 48_000
				SCREAMING_SNAKE_CASE_						 = 2
				SCREAMING_SNAKE_CASE_						 = False
				SCREAMING_SNAKE_CASE_						 = '''time_group_norm'''
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = 1.0
				SCREAMING_SNAKE_CASE_						 = 0.0_1
		else:
				raise ValueError(F'Unknown model name: {model_name}'    )

		SCREAMING_SNAKE_CASE_						 = EncodecModel(__a    )

		SCREAMING_SNAKE_CASE_						 = EncodecFeatureExtractor(
		    feature_size=config.audio_channels,							sampling_rate=config.sampling_rate,							chunk_length_s=config.chunk_length_s,							overlap=config.overlap,							)
		feature_extractor.save_pretrained(__a    )

		SCREAMING_SNAKE_CASE_						 = torch.load(__a    )
		if "best_state" in original_checkpoint:
				# we might have a training state saved, in which case discard the yaml results and just retain the weights
				SCREAMING_SNAKE_CASE_						 = original_checkpoint['''best_state''']
		recursively_load_weights(__a,							__a,							__a    )
		model.save_pretrained(__a    )

		if repo_id:
				print('''Pushing to the hub...'''    )
				feature_extractor.push_to_hub(__a    )
				model.push_to_hub(__a    )


if __name__ == "__main__":
						lowerCAmelCase__							=				argparse.ArgumentParser()
						parser.add_argument(
						    '--model',
						    default='encodec_24khz',
						    type=str,
						    help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
						)
						parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument(
						    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
						)

						lowerCAmelCase__							=				parser.parse_args()
						convert_checkpoint(
						    args.model,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)

"""simple docstring"""




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  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      KandinskyVaaControlnetPipeline
							UpperCAmelCase__						     =      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
							UpperCAmelCase__						     =      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
							UpperCAmelCase__						     =      [
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCAmelCase__						     =      False

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 32

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 32

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.time_input_dim

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 1_00

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = {
									    '''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,
									}

									SCREAMING_SNAKE_CASE_						 = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_    )
									return model

							@property
							def _lowercase					(self    ):

									"""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 _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.dummy_unet
									SCREAMING_SNAKE_CASE_						 = self.dummy_movq

									SCREAMING_SNAKE_CASE_						 = DDIMScheduler(
									    num_train_timesteps=10_00       ,       beta_schedule='''linear'''       ,       beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       clip_sample=SCREAMING_SNAKE_CASE_       ,       set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       steps_offset=1       ,       prediction_type='''epsilon'''       ,       thresholding=SCREAMING_SNAKE_CASE_       ,       )

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=0    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(seed + 1    )    ).to(
									    SCREAMING_SNAKE_CASE_    )

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

									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											SCREAMING_SNAKE_CASE_						 = torch.manual_seed(SCREAMING_SNAKE_CASE_    )
									else:
											SCREAMING_SNAKE_CASE_						 = torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = {
									    '''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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''cpu'''

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_components()

									SCREAMING_SNAKE_CASE_						 = self.pipeline_class(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = pipe.to(SCREAMING_SNAKE_CASE_    )

									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = output.images

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

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

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

									SCREAMING_SNAKE_CASE_						 = 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  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







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







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''    )

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

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

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

									SCREAMING_SNAKE_CASE_						 = '''A robot, 4k photo'''

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

									SCREAMING_SNAKE_CASE_						 = torch.Generator(device='''cuda'''    ).manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipeline(
									    image_embeds=SCREAMING_SNAKE_CASE_       ,       negative_image_embeds=SCREAMING_SNAKE_CASE_       ,       hint=SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=1_00       ,       output_type='''np'''       ,       )

									SCREAMING_SNAKE_CASE_						 = output.images[0]

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

									assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )