



from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase_									=   logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCamelCase_									=   '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     UNetaDModel  ,lowerCamelCase_:     CMStochasticIterativeScheduler							)	->      None:
       super().__init__()

       self.register_modules(
           unet=lowerCamelCase_  ,scheduler=lowerCamelCase_  ,)

       UpperCAmelCase_     :       List[str]  		=					None


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     Dict=0							)	->      Any:
       if is_accelerate_available() and is_accelerate_version(""">="""  ,"""0.14.0"""							):
           from accelerate import cpu_offload
       else:
           raise ImportError("""`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"""							)

       UpperCAmelCase_     :       Tuple  		=					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)

       for cpu_offloaded_model in [self.unet]:
           cpu_offload(lowerCamelCase_  ,lowerCamelCase_							)

       if self.safety_checker is not None:
           cpu_offload(self.safety_checker  ,execution_device=lowerCamelCase_  ,offload_buffers=lowerCamelCase_							)


   def       A__    (    self:     int  ,lowerCamelCase_:     Any=0							)	->      List[Any]:
       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."""							)

       UpperCAmelCase_     :       Any  		=					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)

       UpperCAmelCase_     :       Optional[Any]  		=					None
       for cpu_offloaded_model in [self.unet]:
           UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					cpu_offload_with_hook(lowerCamelCase_  ,lowerCamelCase_  ,prev_module_hook=lowerCamelCase_							)

       if self.safety_checker is not None:
           UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cpu_offload_with_hook(self.safety_checker  ,lowerCamelCase_  ,prev_module_hook=lowerCamelCase_							)

       # We'll offload the last model manually.
       UpperCAmelCase_     :       Union[str, Any]  		=					hook


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def       A__    (    self:     Optional[int]							)	->      Optional[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


   def       A__    (    self:     Dict  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     int  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str=None							)	->      Optional[int]:
       UpperCAmelCase_     :       int  		=					(batch_size, num_channels, height, width)
       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.'''							)

       if latents is None:
           UpperCAmelCase_     :       Dict  		=					randn_tensor(lowerCamelCase_  ,generator=lowerCamelCase_  ,device=lowerCamelCase_  ,dtype=lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       List[Any]  		=					latents.to(device=lowerCamelCase_  ,dtype=lowerCamelCase_							)

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_     :       Optional[int]  		=					latents * self.scheduler.init_noise_sigma
       return latents


   def       A__    (    self:     int  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     str = "pil"							)	->      Optional[Any]:
       if output_type not in ["pt", "np", "pil"]:
           raise ValueError(
               F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''							)

       # Equivalent to diffusers.VaeImageProcessor.denormalize
       UpperCAmelCase_     :       List[Any]  		=					(sample / 2 + 0.5).clamp(0  ,1							)
       if output_type == "pt":
           return sample

       # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
       UpperCAmelCase_     :       Optional[Any]  		=					sample.cpu().permute(0  ,2  ,3  ,1							).numpy()
       if output_type == "np":
           return sample

       # Output_type must be 'pil'
       UpperCAmelCase_     :       Any  		=					self.numpy_to_pil(lowerCamelCase_							)
       return sample


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[Any]=None							)	->      List[Any]:
       if self.unet.config.num_class_embeds is not None:
           if isinstance(lowerCamelCase_  ,lowerCamelCase_							):
               UpperCAmelCase_     :       Tuple  		=					torch.tensor(lowerCamelCase_  ,dtype=torch.int							)
           elif isinstance(lowerCamelCase_  ,lowerCamelCase_							):
               assert batch_size == 1, "Batch size must be 1 if classes is an int"
               UpperCAmelCase_     :       Optional[Any]  		=					torch.tensor([class_labels]  ,dtype=torch.int							)
           elif class_labels is None:
               # Randomly generate batch_size class labels
               # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
               UpperCAmelCase_     :       Tuple  		=					torch.randint(0  ,self.unet.config.num_class_embeds  ,size=(batch_size,)							)
           UpperCAmelCase_     :       Optional[int]  		=					class_labels.to(lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Dict  		=					None
       return class_labels


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      Tuple:
       if num_inference_steps is None and timesteps is None:
           raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

       if num_inference_steps is not None and timesteps is not None:
           logger.warning(
               F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
               """ `timesteps` will be used over `num_inference_steps`."""							)

       if latents is not None:
           UpperCAmelCase_     :       int  		=					(batch_size, 3, img_size, img_size)
           if latents.shape != expected_shape:
               raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''							)

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(lowerCamelCase_  ,lowerCamelCase_							) or callback_steps <= 0)
       ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(lowerCamelCase_							)}.'''							)






   @torch.no_grad()
   @replace_example_docstring(lowerCamelCase_							)
   def __call__(    self:     Tuple  ,lowerCamelCase_:     int = 1  ,lowerCamelCase_:     Optional[Union[torch.Tensor, List[int], int]] = None  ,lowerCamelCase_:     int = 1  ,lowerCamelCase_:     List[int] = None  ,lowerCamelCase_:     Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowerCamelCase_:     Optional[torch.FloatTensor] = None  ,lowerCamelCase_:     Optional[str] = "pil"  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,lowerCamelCase_:     int = 1  ,)	->      int:
       UpperCAmelCase_     :       List[Any]  		=					self.unet.config.sample_size
       UpperCAmelCase_     :       Optional[int]  		=					self._execution_device

       # 1. Check inputs
       self.check_inputs(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

       # 2. Prepare image latents
       # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
       UpperCAmelCase_     :       List[str]  		=					self.prepare_latents(
           batch_size=lowerCamelCase_  ,num_channels=self.unet.config.in_channels  ,height=lowerCamelCase_  ,width=lowerCamelCase_  ,dtype=self.unet.dtype  ,device=lowerCamelCase_  ,generator=lowerCamelCase_  ,latents=lowerCamelCase_  ,)

       # 3. Handle class_labels for class-conditional models
       UpperCAmelCase_     :       List[Any]  		=					self.prepare_class_labels(lowerCamelCase_  ,lowerCamelCase_  ,class_labels=lowerCamelCase_							)

       # 4. Prepare timesteps
       if timesteps is not None:
           self.scheduler.set_timesteps(timesteps=lowerCamelCase_  ,device=lowerCamelCase_							)
           UpperCAmelCase_     :       Tuple  		=					self.scheduler.timesteps
           UpperCAmelCase_     :       Tuple  		=					len(lowerCamelCase_							)
       else:
           self.scheduler.set_timesteps(lowerCamelCase_							)
           UpperCAmelCase_     :       Tuple  		=					self.scheduler.timesteps

       # 5. Denoising loop
       # Multistep sampling: implements Algorithm 1 in the paper
       with self.progress_bar(total=lowerCamelCase_							) as progress_bar:
           for i, t in enumerate(lowerCamelCase_							):
               UpperCAmelCase_     :       Optional[Any]  		=					self.scheduler.scale_model_input(lowerCamelCase_  ,lowerCamelCase_							)
               UpperCAmelCase_     :       Tuple  		=					self.unet(lowerCamelCase_  ,lowerCamelCase_  ,class_labels=lowerCamelCase_  ,return_dict=lowerCamelCase_							)[0]

               UpperCAmelCase_     :       Union[str, Any]  		=					self.scheduler.step(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,generator=lowerCamelCase_							)[0]

               # call the callback, if provided
               progress_bar.update()
               if callback is not None and i % callback_steps == 0:
                   callback(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_							)

        # 6. Post-process image sample
       UpperCAmelCase_     :       Tuple  		=					self.postprocess_image(lowerCamelCase_  ,output_type=lowerCamelCase_							)

       # Offload last model to CPU
       if hasattr(self  ,"""final_offload_hook"""							) and self.final_offload_hook is not None:
           self.final_offload_hook.offload()

       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      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       A__   		=     (JukeboxModel,) if is_torch_available() else ()
       A__   		=     "openai/jukebox-1b-lyrics"
       A__   		=     {
           "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
       A__   		=     [
           18_64, 15_36, 12_13, 18_70, 13_57, 15_36, 5_19, 8_80, 13_23, 7_89, 10_82, 5_34,
           10_00, 14_45, 11_05, 11_30, 9_67, 5_15, 14_34, 16_20, 5_34, 14_95, 2_83, 14_45,
           3_33, 13_07, 5_39, 16_31, 15_28, 3_75, 14_34, 6_73, 6_27, 7_10, 7_78, 18_83,
           14_05, 12_76, 14_55, 12_28
       ]

       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53
       ]

       A__   		=     [
           11_25, 17_51, 6_97, 17_76, 11_41, 14_76, 3_91, 6_97, 11_25, 6_84, 8_67, 4_16,
           8_44, 13_72, 12_74, 7_17, 12_74, 8_44, 12_99, 14_19, 6_97, 13_70, 3_17, 11_25,
           1_91, 14_40, 13_70, 14_40, 13_70, 2_82, 16_21, 13_70, 3_68, 3_49, 8_67, 18_72,
           12_62, 8_69, 17_28, 7_47
       ]
       A__   		=     [
           4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16
       ]

       A__   		=     [
           17_55, 8_42, 3_07, 18_43, 10_22, 13_95, 2_34, 15_54, 8_06, 7_39, 10_22, 4_42,
           6_16, 5_56, 2_68, 14_99, 9_33, 4_57, 14_40, 18_37, 7_55, 9_85, 3_08, 9_02,
           2_93, 14_43, 16_71, 11_41, 15_33, 5_55, 15_62, 10_61, 2_87, 4_17, 10_22, 20_08,
           11_86, 10_15, 17_77, 2_68
       ]
       A__   		=     [
           8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42,
           1_85, 4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 3_07, 8_42,
           5_91, 8_42, 13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42,
           1_85, 8_42, 5_91, 89
       ]

       A__   		=     [1_05_83_04, 0, 78_64_32, 71_69, 5_07, 76, 27, 40, 30, 76]

       A__   		=     [
           3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
           10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
           15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
           19_07, 17_88, 5_96, 16_26
       ]
       A__   		=     [
           12_36, 16_68, 14_84, 19_20, 18_48, 14_09, 1_39, 8_64, 18_28, 12_72, 15_99, 8_24,
           16_72, 1_39, 5_55, 14_84, 8_24, 19_20, 5_55, 5_96, 15_79, 15_99, 12_31, 15_99,
           16_37, 14_07, 2_12, 8_24, 15_99, 1_16, 14_33, 8_24, 2_58, 15_99, 14_33, 18_95,
           10_63, 14_33, 14_33, 15_99
       ]
       A__   		=     [
           16_84, 18_73, 11_19, 11_89, 3_95, 6_11, 19_01, 9_72, 8_90, 13_37, 13_92, 19_27,
           96, 9_72, 6_72, 7_80, 11_19, 8_90, 1_58, 7_71, 10_73, 19_27, 3_53, 13_31,
           12_69, 14_59, 13_33, 16_45, 8_12, 15_77, 13_37, 6_06, 3_53, 9_81, 14_66, 6_19,
           1_97, 3_91, 3_02, 19_30
       ]
       A__   		=     [
           3_90, 11_60, 10_02, 19_07, 17_88, 17_88, 17_88, 19_07, 10_02, 10_02, 18_54, 10_02,
           10_02, 10_02, 10_02, 10_02, 10_02, 11_60, 11_60, 16_06, 5_96, 5_96, 11_60, 10_02,
           15_16, 5_96, 10_02, 10_02, 10_02, 19_07, 17_88, 17_88, 17_88, 18_54, 17_88, 19_07,
           19_07, 17_88, 5_96, 16_26
       ]
       A__   		=     [
           -0.0_492, -0.0_524, -0.0_565, -0.0_640, -0.0_686, -0.0_684, -0.0_677, -0.0_664,
           -0.0_605, -0.0_490, -0.0_330, -0.0_168, -0.0_083, -0.0_075, -0.0_051, 0.0_025,
           0.0_136, 0.0_261, 0.0_386, 0.0_497, 0.0_580, 0.0_599, 0.0_583, 0.0_614,
           0.0_740, 0.0_889, 0.1_023, 0.1_162, 0.1_211, 0.1_212, 0.1_251, 0.1_336,
           0.1_502, 0.1_686, 0.1_883, 0.2_148, 0.2_363, 0.2_458, 0.2_507, 0.2_531
       ]
       A__   		=     [
           0.0_256, -0.0_544, 0.1_600, -0.0_032, 0.1_066, 0.0_825, -0.0_013, 0.3_440,
           0.0_210, 0.0_412, -0.1_777, -0.0_892, -0.0_164, 0.0_285, -0.0_613, -0.0_617,
           -0.0_137, -0.0_201, -0.0_175, 0.0_215, -0.0_627, 0.0_520, -0.0_730, 0.0_970,
           -0.0_100, 0.0_442, -0.0_586, 0.0_207, -0.0_015, -0.0_082
       ]
       A__   		=     [
           0.0_415, 0.0_877, 0.0_022, -0.0_055, 0.0_751, 0.0_334, 0.0_324, -0.0_068,
           0.0_011, 0.0_017, -0.0_676, 0.0_655, -0.0_143, 0.0_399, 0.0_303, 0.0_743,
           -0.0_168, -0.0_394, -0.1_113, 0.0_124, 0.0_442, 0.0_267, -0.0_003, -0.1_536,
           -0.0_116, -0.1_837, -0.0_180, -0.1_026, -0.0_777, -0.0_456
       ]
       A__   		=     [
           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 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    JukeboxTokenizer.from_pretrained(self.model_id			)
             lowerCamelCase__						:							Dict       =    tokenizer(**self.metas			)["input_ids"]
             return tokens





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             lowerCamelCase__						:							Optional[Any]       =    self.prepare_inputs()

             set_seed(0			)
             lowerCamelCase__						:							List[str]       =    [torch.zeros(1   , 0   , dtype=torch.long			).cpu() for _ in range(3			)]
             lowerCamelCase__						:							Union[str, Any]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [0]   , sample_length=40 * 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__						:							Optional[Any]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [1]   , sample_length=40 * 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__						:							List[str]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [2]   , sample_length=40 * 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 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    False
             lowerCamelCase__						:							Any       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()

             lowerCamelCase__						:							Optional[Any]       =    self.prepare_inputs()
             set_seed(0			)
             lowerCamelCase__						:							List[Any]       =    [torch.zeros(1   , 0   , dtype=torch.long			) for _ in range(3			)]

             lowerCamelCase__						:							Optional[int]       =    model.priors[0]
             lowerCamelCase__						:							List[str]       =    0
             lowerCamelCase__						:							List[str]       =    top_prior.get_music_tokens_conds(__lowerCamelCase   , start=__lowerCamelCase   , end=start + top_prior.n_ctx			)
             lowerCamelCase__						:							List[Any]       =    top_prior.get_metadata(labels[0].clone()   , __lowerCamelCase   , 1058304   , 0			)

             self.assertIsNone(__lowerCamelCase			)
             self.assertListEqual(metadata.numpy()[0][:10].tolist()   , self.EXPECTED_Y_COND			)

             lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__						:							str       =    top_prior.get_cond(__lowerCamelCase   , __lowerCamelCase			)
             torch.testing.assert_allclose(
                 audio_conditioning[0][0][:30].detach()   , torch.tensor(self.EXPECTED_AUDIO_COND			)   , atol=1E-4   , rtol=1E-4			)
             torch.testing.assert_allclose(
                 metadata_conditioning[0][0][:30].detach()   , torch.tensor(self.EXPECTED_META_COND			)   , atol=1E-4   , rtol=1E-4			)
             torch.testing.assert_allclose(
                 lyric_tokens[0, :30].detach()   , torch.tensor(self.EXPECTED_LYRIC_COND			)   , atol=1E-4   , rtol=1E-4			)





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    False

             lowerCamelCase__						:							List[Any]       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             set_seed(0			)
             lowerCamelCase__						:							int       =    torch.rand((1, 5120, 1)			)
             lowerCamelCase__						:							int       =    list(self.prepare_inputs()			)

             lowerCamelCase__						:							List[str]       =    [model.vqvae.encode(__lowerCamelCase   , start_level=2   , bs_chunks=waveform.shape[0]			)[0], None, None]
             lowerCamelCase__						:							str       =    model._sample(
                 __lowerCamelCase   , __lowerCamelCase   , sample_levels=[0]   , save_results=__lowerCamelCase   , sample_length=40 * model.priors[0].raw_to_tokens			)
             torch.testing.assert_allclose(zs[0][0][:40]   , torch.tensor(self.EXPECTED_PRIMED_0			)			)

             lowerCamelCase__						:							Tuple       =    torch.cat((zs[0], torch.zeros(1   , 2048 - zs[0].shape[-1]			))   , dim=-1			).long()
             lowerCamelCase__						:							Dict       =    [upper_a, model.vqvae.encode(__lowerCamelCase   , start_level=1   , bs_chunks=waveform.shape[0]			)[0], None]
             lowerCamelCase__						:							str       =    model._sample(
                 __lowerCamelCase   , __lowerCamelCase   , sample_levels=[1]   , save_results=__lowerCamelCase   , sample_length=40 * model.priors[1].raw_to_tokens			)
             torch.testing.assert_allclose(zs[1][0][:40]   , torch.tensor(self.EXPECTED_PRIMED_1			)			)

             lowerCamelCase__						:							List[Any]       =    torch.cat((zs[1], torch.zeros(1   , 2048 - zs[1].shape[-1]			))   , dim=-1			).long()
             lowerCamelCase__						:							Union[str, Any]       =    [upper_a, upper_a, model.vqvae.encode(__lowerCamelCase   , start_level=0   , bs_chunks=waveform.shape[0]			)[0]]
             lowerCamelCase__						:							str       =    model._sample(
                 __lowerCamelCase   , __lowerCamelCase   , sample_levels=[2]   , save_results=__lowerCamelCase   , sample_length=40 * model.priors[2].raw_to_tokens			)
             torch.testing.assert_allclose(zs[2][0][:40].cpu()   , torch.tensor(self.EXPECTED_PRIMED_2			)			)





       @slow
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             set_seed(0			)
             lowerCamelCase__						:							Dict       =    torch.rand((1, 5120, 1)			)
             with torch.no_grad():
                   lowerCamelCase__						:							Optional[int]       =    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__						:							Tuple       =    model.vqvae.decode(__lowerCamelCase   , start_level=2   , bs_chunks=x.shape[0]			)
             torch.testing.assert_allclose(x[0, :40, 0]   , torch.tensor(self.EXPECTED_VQVAE_DECODE			)   , atol=1E-4   , rtol=1E-4			)







@require_torch
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       A__   		=     (JukeboxModel,) if is_torch_available() else ()
       A__   		=     "openai/jukebox-5b-lyrics"
       A__   		=     {
           "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
       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           14_89, 14_89, 14_89, 14_89, 11_50, 18_53, 15_09, 11_50, 13_57, 15_09, 6, 12_72
       ]
       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
       ]

       A__   		=     [
           11_25, 4_16, 11_25, 11_25, 11_25, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
       ]
       A__   		=     [
           4_16, 4_16, 11_25, 11_25, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
       ]

       A__   		=     [
           17_55, 10_61, 2_34, 17_55, 10_61, 17_55, 1_85, 2_90, 3_07, 3_07, 6_16, 6_16,
           6_16, 6_16, 6_16, 6_16, 3_07, 2_90, 4_17, 17_55, 2_34, 17_55, 1_85, 2_90,
           2_90, 2_90, 3_07, 6_16, 6_16, 6_16, 6_16, 6_16, 2_90, 2_34, 2_34, 17_55,
           2_34, 2_34, 17_55, 2_34, 1_85, 1_85, 3_07, 6_16, 6_16, 6_16, 6_16, 2_90,
           17_55, 17_55, 17_55, 2_34, 2_34, 17_55, 15_72, 2_90, 3_07, 6_16, 34, 6_16
       ]
       A__   		=     [
           8_54, 8_42, 13_53, 1_14, 13_53, 8_42, 1_85, 8_42, 1_85, 1_14, 5_91, 8_42, 1_85,
           4_17, 1_85, 8_42, 3_07, 8_42, 5_91, 8_42, 1_85, 8_42, 1_85, 8_42, 5_91, 8_42,
           13_53, 8_42, 1_85, 8_42, 5_91, 8_42, 5_91, 1_14, 5_91, 8_42, 1_85, 8_42, 5_91,
           89, 5_91, 8_42, 5_91, 8_42, 5_91, 4_17, 13_72, 8_42, 13_72, 8_42, 34, 8_42,
           1_85, 89, 5_91, 8_42, 1_85, 8_42, 5_91, 6_32
       ]

       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53
       ]
       A__   		=     [
           14_89, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53,
           6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 6_53, 18_53, 11_77, 15_36, 12_28,
           7_10, 4_75, 14_89, 12_29, 12_24, 2_31, 12_24, 2_52, 14_34, 6_53, 4_75,
           11_06, 18_77, 15_99, 12_28, 16_00, 16_83, 11_82, 18_53, 4_75, 18_64,
           2_52, 12_29, 14_34, 20_01
       ]

       A__   		=     [
           11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 11_25, 11_25, 4_16, 4_16, 11_25, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16,
           4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16, 4_16
       ]
       A__   		=     [
           4_91, 17_55, 34, 16_13, 17_55, 4_17, 9_92, 16_13, 2_22, 8_42, 13_53, 16_13,
           8_44, 6_32, 1_85, 16_13, 8_44, 6_32, 1_85, 16_13, 1_85, 8_42, 6_77, 16_13,
           1_85, 1_14, 13_53, 16_13, 3_07, 89, 8_44, 16_13, 3_07, 13_32, 2_34, 19_79,
           3_07, 89, 13_53, 6_16, 34, 8_42, 1_85, 8_42, 34, 8_42, 1_85, 8_42,
           3_07, 1_14, 1_85, 89, 34, 12_68, 1_85, 89, 34, 8_42, 1_85, 89
       ]





       def 		lowerCAmelCase	(  self				:     Tuple   , __lowerCamelCase				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    JukeboxTokenizer.from_pretrained(__lowerCamelCase			)
             lowerCamelCase__						:							List[Any]       =    tokenizer(**self.metas			)["input_ids"]
             return tokens





       @slow
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    JukeboxModel.from_pretrained(self.model_id   , min_duration=0			).eval()
             lowerCamelCase__						:							Dict       =    self.prepare_inputs(self.model_id			)

             set_seed(0			)
             lowerCamelCase__						:							int       =    [torch.zeros(1   , 0   , dtype=torch.long			).cpu() for _ in range(3			)]
             lowerCamelCase__						:							Union[str, Any]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [0]   , sample_length=60 * 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__						:							List[Any]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [1]   , sample_length=60 * 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=60 * 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 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    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__						:							Tuple       =    [torch.zeros(1   , 0   , dtype=torch.long			).cuda() for _ in range(3			)]
             lowerCamelCase__						:							List[Any]       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [0]   , sample_length=60 * 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__						:							Dict       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [1]   , sample_length=60 * 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__						:							Tuple       =    model._sample(__lowerCamelCase   , __lowerCamelCase   , [2]   , sample_length=60 * 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 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    "ArthurZ/jukebox_prior_0"
             lowerCamelCase__						:							Optional[Any]       =    JukeboxPrior.from_pretrained(__lowerCamelCase   , min_duration=0			).eval().half().to("cuda"			)

             lowerCamelCase__						:							Dict       =    self.prepare_inputs(__lowerCamelCase			)[0].cuda()
             lowerCamelCase__						:							Union[str, Any]       =    model.get_metadata(__lowerCamelCase   , 0   , 7680   , 0			)
             set_seed(0			)
             lowerCamelCase__						:							Optional[int]       =    model.sample(1   , metadata=__lowerCamelCase   , sample_tokens=60			)
             self.assertIn(outputs[0].cpu().tolist()   , [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]			)


