


"""simple docstring"""
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    'https://openaipublic.azureedge.net/jukebox/models/'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							if key.endswith(""".model.1.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.bias"""				, """.conv1d_1.bias"""				)
							elif key.endswith(""".model.1.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.weight"""				, """.conv1d_1.weight"""				)
							elif key.endswith(""".model.3.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.bias"""				, """.conv1d_2.bias"""				)
							elif key.endswith(""".model.3.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.weight"""				, """.conv1d_2.weight"""				)

							if "conditioner_blocks.0." in key:
														snake_case_           =		key.replace("""conditioner_blocks.0"""				, """conditioner_blocks"""				)

							if "prime_prior" in key:
														snake_case_           =		key.replace("""prime_prior"""				, """encoder"""				)

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														snake_case_           =		key.replace(""".emb."""				, """."""				)

							if key.endswith("""k"""				):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace(""".k"""				, """.codebook"""				)
							if "y_emb." in key:
														return key.replace("""y_emb."""				, """metadata_embedding."""				)

							if "x_emb.emb." in key:
														snake_case_           =		key.replace("""0.x_emb.emb"""				, """embed_tokens"""				)

							if "prime_state_ln" in key:
														return key.replace("""prime_state_ln"""				, """encoder.final_layer_norm"""				)
							if ".ln" in key:
														return key.replace(""".ln"""				, """.layer_norm"""				)
							if "_ln" in key:
														return key.replace("""_ln"""				, """_layer_norm"""				)

							if "prime_state_proj" in key:
														return key.replace("""prime_state_proj"""				, """encoder.proj_in"""				)
							if "prime_x_out" in key:
														return key.replace("""prime_x_out"""				, """encoder.lm_head"""				)
							if "prior.x_out" in key:
														return key.replace("""x_out"""				, """fc_proj_out"""				)
							if "x_emb" in key:
														return key.replace("""x_emb"""				, """embed_tokens"""				)

							return key







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							snake_case_           =		{}
							import re

							snake_case_           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

							snake_case_           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

							snake_case_           =		re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""				)

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				)
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_encoder_block_conv_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				)
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_encoder_block_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
																					snake_case_           =		re_encoder_block_proj_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				) - 2
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_decoder_block_conv_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				) - 2
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_decoder_block_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																					snake_case_           =		re_decoder_block_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[1]				) * 2 + int(groups[2]				) - 2
																					snake_case_           =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_conv_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[1]				) * 2 + int(groups[2]				) - 2
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_prior_cond_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# keep original key
														else:
																					snake_case_           =		original_key

														snake_case_           =		replace_key(_SCREAMING_SNAKE_CASE				)

														if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
																					print(f"""failed converting {original_key} to {key}, does not match"""				)

														# handle missmatched shape
														elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
																					snake_case_           =		model_state_dict[f"""{key_prefix}.{key}"""]
																					print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""				)
																					snake_case_           =		original_key

														snake_case_           =		original_key
														snake_case_           =		value

							return new_dict







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				)    ->				Optional[int]:
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				):
																					snake_case_           =		requests.get(f"""{PREFIX}{file}"""				, allow_redirects=_SCREAMING_SNAKE_CASE				)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""				, exist_ok=_SCREAMING_SNAKE_CASE				)
																					open(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				, """wb"""				).write(r.content				)

							snake_case_           =		MODEL_MAPPING[model_name.split("""/"""				)[-1]]

							snake_case_           =		JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		JukeboxModel(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		[]
							snake_case_           =		{}
							for i, dict_name in enumerate(_SCREAMING_SNAKE_CASE				):
														snake_case_           =		torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"				)[-1]}"""				)["""model"""]

														snake_case_           =		{}
														for k in old_dic.keys():
																					if k.endswith(""".b"""				):
																												snake_case_           =		old_dic[k]
																					elif k.endswith(""".w"""				):
																												snake_case_           =		old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												snake_case_           =		old_dic[k]
																					else:
																												snake_case_           =		old_dic[k]

														snake_case_           =		"""vqvae""" if i == 0 else f"""priors.{3 - i}"""
														snake_case_           =		fix_jukebox_keys(_SCREAMING_SNAKE_CASE				, model.state_dict()				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
														weight_dict.append(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		weight_dict.pop(0				)
							model.vqvae.load_state_dict(_SCREAMING_SNAKE_CASE				)
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														model.priors[i].load_state_dict(weight_dict[2 - i]				)

							Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							with open(f"""{pytorch_dump_folder_path}/mapping.json"""				, """w"""				) as txtfile:
														json.dump(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							return weight_dict


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='jukebox-5b-lyrics',
				    type=str,
				    help='Name of the model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='jukebox-5b-lyrics-converted',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				__SCREAMING_SNAKE_CASE							:    str		=    parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







"""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      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Dict							      =    KandinskyVaaControlnetPipeline
							__lowercase:		str							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		List[str]							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		Union[str, Any]							      =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__lowercase:		Tuple							      =    False



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



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



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



														"""simple docstring"""



														return self.time_input_dim



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



														"""simple docstring"""



														return self.time_input_dim * 4



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



														"""simple docstring"""



														return 100



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



														"""simple docstring"""



														torch.manual_seed(0       )

														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,
														}

														snake_case_           =		UNetaDConditionModel(**UpperCAmelCase_       )
														return model



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



														"""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 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Dict       )					->str:



														"""simple docstring"""



														snake_case_           =		self.dummy_unet
														snake_case_           =		self.dummy_movq

														snake_case_           =		DDIMScheduler(
														    num_train_timesteps=1_000		,       beta_schedule="""linear"""		,       beta_start=0.00_085		,       beta_end=0.012		,       clip_sample=UpperCAmelCase_		,       set_alpha_to_one=UpperCAmelCase_		,       steps_offset=1		,       prediction_type="""epsilon"""		,       thresholding=UpperCAmelCase_		,       )

														snake_case_           =		{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}
														return components



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]=0       )					->List[str]:



														"""simple docstring"""



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

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

														if str(UpperCAmelCase_       ).startswith("""mps"""       ):
																					snake_case_           =		torch.manual_seed(UpperCAmelCase_       )
														else:
																					snake_case_           =		torch.Generator(device=UpperCAmelCase_       ).manual_seed(UpperCAmelCase_       )
														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 		lowerCAmelCase       (				self							:      List[str]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		"""cpu"""

														snake_case_           =		self.get_dummy_components()

														snake_case_           =		self.pipeline_class(**UpperCAmelCase_       )
														snake_case_           =		pipe.to(UpperCAmelCase_       )

														pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		pipe(**self.get_dummy_inputs(UpperCAmelCase_       )       )
														snake_case_           =		output.images

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

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

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

														snake_case_           =		np.array(
														    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]       )

														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      __A     (unittest.TestCase):





							'''simple docstring'''

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



														"""simple docstring"""



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



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



														"""simple docstring"""



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

														snake_case_           =		load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/hint_image_cat.png"""       )
														snake_case_           =		torch.from_numpy(np.array(UpperCAmelCase_       )       ).float() / 255.0
														snake_case_           =		hint.permute(2		,       0		,       1       ).unsqueeze(0       )

														snake_case_           =		KandinskyVaaPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-prior"""		,       torch_dtype=torch.floataa       )
														pipe_prior.to(UpperCAmelCase_       )

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

														snake_case_           =		"""A robot, 4k photo"""

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_       ,						snake_case_           =		pipe_prior(
														    UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=5		,       negative_prompt=""""""		,       ).to_tuple()

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_           =		pipeline(
														    image_embeds=UpperCAmelCase_		,       negative_image_embeds=UpperCAmelCase_		,       hint=UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=100		,       output_type="""np"""		,       )

														snake_case_           =		output.images[0]

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

														assert_mean_pixel_difference(UpperCAmelCase_		,       UpperCAmelCase_       )



