
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE     :				List[Any]             =       '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       List[Any]							,					__magic_name__  :       List[str]=8					):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					lowerCAmelCase						:   Optional[Any]							=       width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor
def       UpperCAmelCase__							(    __magic_name__  :       Dict							,					__magic_name__  :       List[str]=5_12							,					__magic_name__  :       Any=5_12					):


					'''simple docstring'''

					lowerCAmelCase						:   str							=       pil_image.resize((w, h)							,					resample=Image.BICUBIC							,					reducing_gap=1					)
					lowerCAmelCase						:   List[Any]							=       np.array(pil_image.convert('''RGB'''					)					)
					lowerCAmelCase						:   Dict							=       arr.astype(np.floataa					) / 127.5 - 1
					lowerCAmelCase						:   Any							=       np.transpose(SCREAMING_SNAKE_CASE_							,					[2, 0, 1]					)
					lowerCAmelCase						:   Any							=       torch.from_numpy(SCREAMING_SNAKE_CASE_					).unsqueeze(0					)
					return image







class 						__magic_name__				(  snake_case_      ):





		def __init__(							self      :		int  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		str  ,					):
							super().__init__()

							self.register_modules(
							    unet=lowerCamelCase__  ,					scheduler=lowerCamelCase__  ,					movq=lowerCamelCase__  ,					)
							lowerCAmelCase						:   Union[str, Any]							=       2 ** (len(self.movq.config.block_out_channels			) - 1)





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		List[Any]			):
							lowerCAmelCase						:   int							=       min(int(num_inference_steps * strength			)  ,					lowerCamelCase__			)

							lowerCAmelCase						:   Optional[Any]							=       max(num_inference_steps - init_timestep  ,					0			)
							lowerCAmelCase						:   Dict							=       self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		List[Any]=None			):


							if not isinstance(lowerCamelCase__  ,					(torch.Tensor, PIL.Image.Image, list)			):
												raise ValueError(
												    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase__			)}'''			)

							lowerCAmelCase						:   List[str]							=       image.to(device=lowerCamelCase__  ,					dtype=lowerCamelCase__			)

							lowerCAmelCase						:   Optional[Any]							=       batch_size * num_images_per_prompt

							if image.shape[1] == 4:
												lowerCAmelCase						:   Optional[Any]							=       image

							else:
												if isinstance(lowerCamelCase__  ,					lowerCamelCase__			) and len(lowerCamelCase__			) != batch_size:
																	raise ValueError(
																	    f'''You have passed a list of generators of length {len(lowerCamelCase__			)}, but requested an effective batch'''
																	    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

												elif isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
																	lowerCAmelCase						:   Dict							=       [
																	    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(lowerCamelCase__			)
																	]
																	lowerCAmelCase						:   Union[str, Any]							=       torch.cat(lowerCamelCase__  ,					dim=0			)
												else:
																	lowerCAmelCase						:   Optional[int]							=       self.movq.encode(lowerCamelCase__			).latent_dist.sample(lowerCamelCase__			)

												lowerCAmelCase						:   List[str]							=       self.movq.config.scaling_factor * init_latents

							lowerCAmelCase						:   Dict							=       torch.cat([init_latents]  ,					dim=0			)

							lowerCAmelCase						:   Any							=       init_latents.shape
							lowerCAmelCase						:   str							=       randn_tensor(lowerCamelCase__  ,					generator=lowerCamelCase__  ,					device=lowerCamelCase__  ,					dtype=lowerCamelCase__			)

							# get latents
							lowerCAmelCase						:   Tuple							=       self.scheduler.add_noise(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)

							lowerCAmelCase						:   Optional[Any]							=       init_latents

							return latents





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		List[Any]=0			):


							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

							lowerCAmelCase						:   str							=       torch.device(f'''cuda:{gpu_id}'''			)

							lowerCAmelCase						:   List[str]							=       [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(lowerCamelCase__  ,					lowerCamelCase__			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Tuple=0			):


							if is_accelerate_available() and is_accelerate_version('''>='''  ,					'''0.17.0.dev0'''			):
												from accelerate import cpu_offload_with_hook
							else:
												raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''			)

							lowerCAmelCase						:   int							=       torch.device(f'''cuda:{gpu_id}'''			)

							if self.device.type != "cpu":
												self.to('''cpu'''  ,					silence_dtype_warnings=lowerCamelCase__			)
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowerCAmelCase						:   Union[str, Any]							=       None
							for cpu_offloaded_model in [self.unet, self.movq]:
												lowerCAmelCase						:   List[str]							=       cpu_offload_with_hook(lowerCamelCase__  ,					lowerCamelCase__  ,					prev_module_hook=lowerCamelCase__			)

							# We'll offload the last model manually.
							lowerCAmelCase						:   Tuple							=       hook





		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def  _A							(							self      :		List[Any]			):


							if not hasattr(self.unet  ,					'''_hf_hook'''			):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(lowerCamelCase__  ,					'''_hf_hook'''			)
												    and hasattr(module._hf_hook  ,					'''execution_device'''			)
												    and module._hf_hook.execution_device is not None
												):
																	return torch.device(module._hf_hook.execution_device			)
							return self.device





		@torch.no_grad()
		@replace_example_docstring(lowerCamelCase__			)
		def __call__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Dict = 5_1_2  ,					lowerCamelCase__      :		str = 5_1_2  ,					lowerCamelCase__      :		List[Any] = 1_0_0  ,					lowerCamelCase__      :		Union[str, Any] = 4.0  ,					lowerCamelCase__      :		Optional[Any] = 0.3  ,					lowerCamelCase__      :		Tuple = 1  ,					lowerCamelCase__      :		Dict = None  ,					lowerCamelCase__      :		int = "pil"  ,					lowerCamelCase__      :		Dict = True  ,					):
							lowerCAmelCase						:   List[str]							=       self._execution_device

							lowerCAmelCase						:   str							=       guidance_scale > 1.0

							if isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
												lowerCAmelCase						:   List[Any]							=       torch.cat(lowerCamelCase__  ,					dim=0			)
							if isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
												lowerCAmelCase						:   Union[str, Any]							=       torch.cat(lowerCamelCase__  ,					dim=0			)
							if isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
												lowerCAmelCase						:   Union[str, Any]							=       torch.cat(lowerCamelCase__  ,					dim=0			)

							lowerCAmelCase						:   str							=       image_embeds.shape[0]

							if do_classifier_free_guidance:
												lowerCAmelCase						:   Optional[int]							=       image_embeds.repeat_interleave(lowerCamelCase__  ,					dim=0			)
												lowerCAmelCase						:   Union[str, Any]							=       negative_image_embeds.repeat_interleave(lowerCamelCase__  ,					dim=0			)
												lowerCAmelCase						:   Tuple							=       hint.repeat_interleave(lowerCamelCase__  ,					dim=0			)

							lowerCAmelCase						:   int							=       torch.cat([negative_image_embeds, image_embeds]  ,					dim=0			).to(dtype=self.unet.dtype  ,					device=lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       torch.cat([hint, hint]  ,					dim=0			).to(dtype=self.unet.dtype  ,					device=lowerCamelCase__			)

							if not isinstance(lowerCamelCase__  ,					lowerCamelCase__			):
												lowerCAmelCase						:   Union[str, Any]							=       [image]
							if not all(isinstance(lowerCamelCase__  ,					(PIL.Image.Image, torch.Tensor)			) for i in image			):
												raise ValueError(
												    f'''Input is in incorrect format: {[type(lowerCamelCase__			) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''			)

							lowerCAmelCase						:   Dict							=       torch.cat([prepare_image(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			) for i in image]  ,					dim=0			)
							lowerCAmelCase						:   List[str]							=       image.to(dtype=image_embeds.dtype  ,					device=lowerCamelCase__			)

							lowerCAmelCase						:   Optional[int]							=       self.movq.encode(lowerCamelCase__			)["latents"]
							lowerCAmelCase						:   List[Any]							=       latents.repeat_interleave(lowerCamelCase__  ,					dim=0			)
							self.scheduler.set_timesteps(lowerCamelCase__  ,					device=lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       self.get_timesteps(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			)
							lowerCAmelCase						:   Union[str, Any]							=       timesteps[:1].repeat(batch_size * num_images_per_prompt			)
							lowerCAmelCase						:   List[str]							=       downscale_height_and_width(lowerCamelCase__  ,					lowerCamelCase__  ,					self.movq_scale_factor			)
							lowerCAmelCase						:   Union[str, Any]							=       self.prepare_latents(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					image_embeds.dtype  ,					lowerCamelCase__  ,					lowerCamelCase__			)
							for i, t in enumerate(self.progress_bar(lowerCamelCase__			)			):
												# expand the latents if we are doing classifier free guidance
												lowerCAmelCase						:   Union[str, Any]							=       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

												lowerCAmelCase						:   Tuple							=       {"image_embeds": image_embeds, "hint": hint}
												lowerCAmelCase						:   Any							=       self.unet(
												    sample=lowerCamelCase__  ,					timestep=lowerCamelCase__  ,					encoder_hidden_states=lowerCamelCase__  ,					added_cond_kwargs=lowerCamelCase__  ,					return_dict=lowerCamelCase__  ,					)[0]

												if do_classifier_free_guidance:
																	lowerCAmelCase						:   List[str]							=       noise_pred.split(latents.shape[1]  ,					dim=1			)
																	lowerCAmelCase						:   Any							=       noise_pred.chunk(2			)
																	lowerCAmelCase						:   List[str]							=       variance_pred.chunk(2			)
																	lowerCAmelCase						:   List[Any]							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	lowerCAmelCase						:   Optional[Any]							=       torch.cat([noise_pred, variance_pred_text]  ,					dim=1			)

												if not (
												    hasattr(self.scheduler.config  ,					'''variance_type'''			)
												    and self.scheduler.config.variance_type in ["learned", "learned_range"]
												):
																	lowerCAmelCase						:   Tuple							=       noise_pred.split(latents.shape[1]  ,					dim=1			)

												# compute the previous noisy sample x_t -> x_t-1

												lowerCAmelCase						:   Optional[Any]							=       self.scheduler.step(
												    lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					generator=lowerCamelCase__  ,					)[0]

							# post-processing
							lowerCAmelCase						:   Tuple							=       self.movq.decode(lowerCamelCase__  ,					force_not_quantize=lowerCamelCase__			)["sample"]

							if output_type not in ["pt", "np", "pil"]:
												raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''			)

							if output_type in ["np", "pil"]:
												lowerCAmelCase						:   Union[str, Any]							=       image * 0.5 + 0.5
												lowerCAmelCase						:   List[Any]							=       image.clamp(0  ,					1			)
												lowerCAmelCase						:   str							=       image.cpu().permute(0  ,					2  ,					3  ,					1			).float().numpy()

							if output_type == "pil":
												lowerCAmelCase						:   List[str]							=       self.numpy_to_pil(lowerCamelCase__			)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=lowerCamelCase__			)


import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
		import torch

		from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer


@require_torch
class 						__magic_name__				(  unittest.TestCase      ):
		_lowerCAmelCase						=		(JukeboxModel,) if is_torch_available() else ()
		_lowerCAmelCase						=		"openai/jukebox-1b-lyrics"
		_lowerCAmelCase						=		{
		    "artist": "Zac Brown Band",
		    "genres": "Country",
		    "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
		}
		# fmt: off
		_lowerCAmelCase						=		[
		    1_864, 1_536, 1_213, 1_870, 1_357, 1_536, 519, 880, 1_323, 789, 1_082, 534,
		    1_000, 1_445, 1_105, 1_130, 967, 515, 1_434, 1_620, 534, 1_495, 283, 1_445,
		    333, 1_307, 539, 1_631, 1_528, 375, 1_434, 673, 627, 710, 778, 1_883,
		    1_405, 1_276, 1_455, 1_228
		]

		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653
		]

		_lowerCAmelCase						=		[
		    1_125, 1_751, 697, 1_776, 1_141, 1_476, 391, 697, 1_125, 684, 867, 416,
		    844, 1_372, 1_274, 717, 1_274, 844, 1_299, 1_419, 697, 1_370, 317, 1_125,
		    191, 1_440, 1_370, 1_440, 1_370, 282, 1_621, 1_370, 368, 349, 867, 1_872,
		    1_262, 869, 1_728, 747
		]
		_lowerCAmelCase						=		[
		    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416
		]

		_lowerCAmelCase						=		[
		    1_755, 842, 307, 1_843, 1_022, 1_395, 234, 1_554, 806, 739, 1_022, 442,
		    616, 556, 268, 1_499, 933, 457, 1_440, 1_837, 755, 985, 308, 902,
		    293, 1_443, 1_671, 1_141, 1_533, 555, 1_562, 1_061, 287, 417, 1_022, 2_008,
		    1_186, 1_015, 1_777, 268
		]
		_lowerCAmelCase						=		[
		    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842,
		    185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
		    591, 842, 1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
		    185, 842, 591, 89
		]

		_lowerCAmelCase						=		[1_058_304, 0, 786_432, 7_169, 507, 76, 27, 40, 30, 76]

		_lowerCAmelCase						=		[
		    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
		    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
		    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
		    1_907, 1_788, 596, 1_626
		]
		_lowerCAmelCase						=		[
		    1_236, 1_668, 1_484, 1_920, 1_848, 1_409, 139, 864, 1_828, 1_272, 1_599, 824,
		    1_672, 139, 555, 1_484, 824, 1_920, 555, 596, 1_579, 1_599, 1_231, 1_599,
		    1_637, 1_407, 212, 824, 1_599, 116, 1_433, 824, 258, 1_599, 1_433, 1_895,
		    1_063, 1_433, 1_433, 1_599
		]
		_lowerCAmelCase						=		[
		    1_684, 1_873, 1_119, 1_189, 395, 611, 1_901, 972, 890, 1_337, 1_392, 1_927,
		    96, 972, 672, 780, 1_119, 890, 158, 771, 1_073, 1_927, 353, 1_331,
		    1_269, 1_459, 1_333, 1_645, 812, 1_577, 1_337, 606, 353, 981, 1_466, 619,
		    197, 391, 302, 1_930
		]
		_lowerCAmelCase						=		[
		    390, 1_160, 1_002, 1_907, 1_788, 1_788, 1_788, 1_907, 1_002, 1_002, 1_854, 1_002,
		    1_002, 1_002, 1_002, 1_002, 1_002, 1_160, 1_160, 1_606, 596, 596, 1_160, 1_002,
		    1_516, 596, 1_002, 1_002, 1_002, 1_907, 1_788, 1_788, 1_788, 1_854, 1_788, 1_907,
		    1_907, 1_788, 596, 1_626
		]
		_lowerCAmelCase						=		[
		    -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
		    -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
		    0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
		    0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
		    0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
		]
		_lowerCAmelCase						=		[
		    0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
		    0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
		    -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
		    -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
		]
		_lowerCAmelCase						=		[
		    0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
		    0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
		    -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
		    -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
		]
		_lowerCAmelCase						=		[
		    76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
		    45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
		]





		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   int							=       JukeboxTokenizer.from_pretrained(self.model_id			)
							lowerCAmelCase						:   Union[str, Any]							=       tokenizer(**self.metas			)['''input_ids''']
							return tokens





		@slow
		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							lowerCAmelCase						:   Union[str, Any]							=       self.prepare_inputs()

							set_seed(0			)
							lowerCAmelCase						:   Optional[int]							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			).cpu() for _ in range(3			)]
							lowerCAmelCase						:   Optional[Any]							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[0]  ,					sample_length=4_0 * model.priors[0].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							self.assertIn(zs[0][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)

							set_seed(0			)
							lowerCAmelCase						:   Tuple							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[1]  ,					sample_length=4_0 * model.priors[1].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							self.assertIn(zs[1][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]			)

							set_seed(0			)
							lowerCAmelCase						:   Tuple							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[2]  ,					sample_length=4_0 * model.priors[2].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							self.assertIn(zs[2][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)





		@slow
		def  _A							(							self      :		str			):
							lowerCAmelCase						:   str							=       False
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()

							lowerCAmelCase						:   int							=       self.prepare_inputs()
							set_seed(0			)
							lowerCAmelCase						:   str							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			) for _ in range(3			)]

							lowerCAmelCase						:   Optional[Any]							=       model.priors[0]
							lowerCAmelCase						:   Tuple							=       0
							lowerCAmelCase						:   Tuple							=       top_prior.get_music_tokens_conds(lowerCamelCase__  ,					start=lowerCamelCase__  ,					end=start + top_prior.n_ctx			)
							lowerCAmelCase						:   List[str]							=       top_prior.get_metadata(labels[0].clone()  ,					lowerCamelCase__  ,					1_0_5_8_3_0_4  ,					0			)

							self.assertIsNone(lowerCamelCase__			)
							self.assertListEqual(metadata.numpy()[0][:1_0].tolist()  ,					self.EXPECTED_Y_COND			)

							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Dict							=       top_prior.get_cond(lowerCamelCase__  ,					lowerCamelCase__			)
							torch.testing.assert_allclose(
							    audio_conditioning[0][0][:3_0].detach()  ,					torch.tensor(self.EXPECTED_AUDIO_COND			)  ,					atol=1E-4  ,					rtol=1E-4			)
							torch.testing.assert_allclose(
							    metadata_conditioning[0][0][:3_0].detach()  ,					torch.tensor(self.EXPECTED_META_COND			)  ,					atol=1E-4  ,					rtol=1E-4			)
							torch.testing.assert_allclose(
							    lyric_tokens[0, :3_0].detach()  ,					torch.tensor(self.EXPECTED_LYRIC_COND			)  ,					atol=1E-4  ,					rtol=1E-4			)





		@slow
		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Tuple							=       False

							lowerCAmelCase						:   Optional[int]							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							set_seed(0			)
							lowerCAmelCase						:   Union[str, Any]							=       torch.rand((1, 5_1_2_0, 1)			)
							lowerCAmelCase						:   Optional[int]							=       list(self.prepare_inputs()			)

							lowerCAmelCase						:   List[str]							=       [model.vqvae.encode(lowerCamelCase__  ,					start_level=2  ,					bs_chunks=waveform.shape[0]			)[0], None, None]
							lowerCAmelCase						:   List[Any]							=       model._sample(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					sample_levels=[0]  ,					save_results=lowerCamelCase__  ,					sample_length=4_0 * model.priors[0].raw_to_tokens			)
							torch.testing.assert_allclose(zs[0][0][:4_0]  ,					torch.tensor(self.EXPECTED_PRIMED_0			)			)

							lowerCAmelCase						:   List[Any]							=       torch.cat((zs[0], torch.zeros(1  ,					2_0_4_8 - zs[0].shape[-1]			))  ,					dim=-1			).long()
							lowerCAmelCase						:   List[str]							=       [upper_a, model.vqvae.encode(lowerCamelCase__  ,					start_level=1  ,					bs_chunks=waveform.shape[0]			)[0], None]
							lowerCAmelCase						:   List[Any]							=       model._sample(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					sample_levels=[1]  ,					save_results=lowerCamelCase__  ,					sample_length=4_0 * model.priors[1].raw_to_tokens			)
							torch.testing.assert_allclose(zs[1][0][:4_0]  ,					torch.tensor(self.EXPECTED_PRIMED_1			)			)

							lowerCAmelCase						:   Any							=       torch.cat((zs[1], torch.zeros(1  ,					2_0_4_8 - zs[1].shape[-1]			))  ,					dim=-1			).long()
							lowerCAmelCase						:   str							=       [upper_a, upper_a, model.vqvae.encode(lowerCamelCase__  ,					start_level=0  ,					bs_chunks=waveform.shape[0]			)[0]]
							lowerCAmelCase						:   List[Any]							=       model._sample(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					sample_levels=[2]  ,					save_results=lowerCamelCase__  ,					sample_length=4_0 * model.priors[2].raw_to_tokens			)
							torch.testing.assert_allclose(zs[2][0][:4_0].cpu()  ,					torch.tensor(self.EXPECTED_PRIMED_2			)			)





		@slow
		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							set_seed(0			)
							lowerCAmelCase						:   Tuple							=       torch.rand((1, 5_1_2_0, 1)			)
							with torch.no_grad():
												lowerCAmelCase						:   Tuple							=       model.vqvae.encode(lowerCamelCase__  ,					start_level=2  ,					bs_chunks=x.shape[0]			)
							torch.testing.assert_allclose(zs[0][0]  ,					torch.tensor(self.EXPECTED_VQVAE_ENCODE			)			)

							with torch.no_grad():
												lowerCAmelCase						:   int							=       model.vqvae.decode(lowerCamelCase__  ,					start_level=2  ,					bs_chunks=x.shape[0]			)
							torch.testing.assert_allclose(x[0, :4_0, 0]  ,					torch.tensor(self.EXPECTED_VQVAE_DECODE			)  ,					atol=1E-4  ,					rtol=1E-4			)



@require_torch
class 						__magic_name__				(  unittest.TestCase      ):
		_lowerCAmelCase						=		(JukeboxModel,) if is_torch_available() else ()
		_lowerCAmelCase						=		"openai/jukebox-5b-lyrics"
		_lowerCAmelCase						=		{
		    "artist": "Zac Brown Band",
		    "genres": "Country",
		    "lyrics": "I met a traveller from an antique land,\n    Who said \"Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ",
		}

		# fmt: off
		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    1_489, 1_489, 1_489, 1_489, 1_150, 1_853, 1_509, 1_150, 1_357, 1_509, 6, 1_272
		]
		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
		]

		_lowerCAmelCase						=		[
		    1_125, 416, 1_125, 1_125, 1_125, 1_125, 1_125, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
		]
		_lowerCAmelCase						=		[
		    416, 416, 1_125, 1_125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416
		]

		_lowerCAmelCase						=		[
		    1_755, 1_061, 234, 1_755, 1_061, 1_755, 185, 290, 307, 307, 616, 616,
		    616, 616, 616, 616, 307, 290, 417, 1_755, 234, 1_755, 185, 290,
		    290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1_755,
		    234, 234, 1_755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
		    1_755, 1_755, 1_755, 234, 234, 1_755, 1_572, 290, 307, 616, 34, 616
		]
		_lowerCAmelCase						=		[
		    854, 842, 1_353, 114, 1_353, 842, 185, 842, 185, 114, 591, 842, 185,
		    417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
		    1_353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
		    89, 591, 842, 591, 842, 591, 417, 1_372, 842, 1_372, 842, 34, 842,
		    185, 89, 591, 842, 185, 842, 591, 632
		]

		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
		]
		_lowerCAmelCase						=		[
		    1_489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
		    653, 653, 653, 653, 653, 653, 653, 1_853, 1_177, 1_536, 1_228,
		    710, 475, 1_489, 1_229, 1_224, 231, 1_224, 252, 1_434, 653, 475,
		    1_106, 1_877, 1_599, 1_228, 1_600, 1_683, 1_182, 1_853, 475, 1_864,
		    252, 1_229, 1_434, 2_001
		]

		_lowerCAmelCase						=		[
		    1_125, 1_125, 416, 1_125, 1_125, 416, 1_125, 1_125, 416, 416, 1_125, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
		    416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
		]
		_lowerCAmelCase						=		[
		    491, 1_755, 34, 1_613, 1_755, 417, 992, 1_613, 222, 842, 1_353, 1_613,
		    844, 632, 185, 1_613, 844, 632, 185, 1_613, 185, 842, 677, 1_613,
		    185, 114, 1_353, 1_613, 307, 89, 844, 1_613, 307, 1_332, 234, 1_979,
		    307, 89, 1_353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
		    307, 114, 185, 89, 34, 1_268, 185, 89, 34, 842, 185, 89
		]





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Any							=       JukeboxTokenizer.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       tokenizer(**self.metas			)['''input_ids''']
							return tokens





		@slow
		def  _A							(							self      :		int			):
							lowerCAmelCase						:   Any							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							lowerCAmelCase						:   Dict							=       self.prepare_inputs(self.model_id			)

							set_seed(0			)
							lowerCAmelCase						:   List[str]							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			).cpu() for _ in range(3			)]
							lowerCAmelCase						:   Tuple							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[0]  ,					sample_length=6_0 * model.priors[0].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							self.assertIn(zs[0][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]			)

							set_seed(0			)
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[1]  ,					sample_length=6_0 * model.priors[1].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							self.assertIn(zs[1][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]			)

							set_seed(0			)
							lowerCAmelCase						:   Dict							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[2]  ,					sample_length=6_0 * model.priors[2].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							self.assertIn(zs[2][0].detach().cpu().tolist()  ,					[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]			)





		@slow
		@skip('''Not enough GPU memory on CI runners'''			)
		def  _A							(							self      :		str			):
							lowerCAmelCase						:   int							=       JukeboxModel.from_pretrained(self.model_id  ,					min_duration=0			).eval()
							lowerCAmelCase						:   Optional[int]							=       [i.cuda() for i in self.prepare_inputs(self.model_id			)]

							set_seed(0			)
							model.priors[0].cuda()
							lowerCAmelCase						:   List[str]							=       [torch.zeros(1  ,					0  ,					dtype=torch.long			).cuda() for _ in range(3			)]
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[0]  ,					sample_length=6_0 * model.priors[0].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							torch.testing.assert_allclose(zs[0][0].cpu()  ,					torch.tensor(self.EXPECTED_GPU_OUTPUTS_2			)			)
							model.priors[0].cpu()

							set_seed(0			)
							model.priors[1].cuda()
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[1]  ,					sample_length=6_0 * model.priors[1].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							torch.testing.assert_allclose(zs[1][0].cpu()  ,					torch.tensor(self.EXPECTED_GPU_OUTPUTS_1			)			)
							model.priors[1].cpu()

							set_seed(0			)
							model.priors[2].cuda()
							lowerCAmelCase						:   int							=       model._sample(lowerCamelCase__  ,					lowerCamelCase__  ,					[2]  ,					sample_length=6_0 * model.priors[2].raw_to_tokens  ,					save_results=lowerCamelCase__			)
							torch.testing.assert_allclose(zs[2][0].cpu()  ,					torch.tensor(self.EXPECTED_GPU_OUTPUTS_0			)			)





		@slow
		def  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   Dict							=       '''ArthurZ/jukebox_prior_0'''
							lowerCAmelCase						:   Tuple							=       JukeboxPrior.from_pretrained(lowerCamelCase__  ,					min_duration=0			).eval().half().to('''cuda'''			)

							lowerCAmelCase						:   List[str]							=       self.prepare_inputs(lowerCamelCase__			)[0].cuda()
							lowerCAmelCase						:   int							=       model.get_metadata(lowerCamelCase__  ,					0  ,					7_6_8_0  ,					0			)
							set_seed(0			)
							lowerCAmelCase						:   str							=       model.sample(1  ,					metadata=lowerCamelCase__  ,					sample_tokens=6_0			)
							self.assertIn(outputs[0].cpu().tolist()  ,					[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]			)
