






'''simple docstring'''







import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCamelCase_					=				logging.get_logger(__name__)


UpperCamelCase_					=				{
    "suno/bark-small": "https://huggingface.co/suno/bark-small/resolve/main/config.json",
    "suno/bark": "https://huggingface.co/suno/bark/resolve/main/config.json",
}

UpperCamelCase_					=				"\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n"




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Any				       =					"bark_module"
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					["past_key_values"]

      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					{
          "num_attention_heads": "num_heads",
          "num_hidden_layers": "num_layers",
          "vocab_size": "input_vocab_size",
          "window_size": "block_size",
      }




      def __init__(					self  ,		_UpperCAmelCase=1024  ,		_UpperCAmelCase=1_0048  ,		_UpperCAmelCase=1_0048  ,		_UpperCAmelCase=12  ,		_UpperCAmelCase=12  ,		_UpperCAmelCase=768  ,		_UpperCAmelCase=0.0  ,		_UpperCAmelCase=True  ,		_UpperCAmelCase=0.02  ,		_UpperCAmelCase=True  ,		**_UpperCAmelCase  ,		):
             UpperCAmelCase__	:   Tuple												=	block_size
             UpperCAmelCase__	:   int												=	input_vocab_size
             UpperCAmelCase__	:   Optional[Any]												=	output_vocab_size
             UpperCAmelCase__	:   List[Any]												=	num_layers
             UpperCAmelCase__	:   Optional[int]												=	num_heads
             UpperCAmelCase__	:   Union[str, Any]												=	hidden_size
             UpperCAmelCase__	:   List[str]												=	dropout
             UpperCAmelCase__	:   Dict												=	bias
             UpperCAmelCase__	:   Union[str, Any]												=	use_cache
             UpperCAmelCase__	:   Tuple												=	initializer_range

             super().__init__(**_UpperCAmelCase						)




      @classmethod
      def 							lowerCamelCase     (					cls  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = False  ,		_UpperCAmelCase = False  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = "main"  ,		**_UpperCAmelCase  ,		):
             UpperCAmelCase__	:   Dict												=	cache_dir
             UpperCAmelCase__	:   Optional[int]												=	force_download
             UpperCAmelCase__	:   Any												=	local_files_only
             UpperCAmelCase__	:   Optional[Any]												=	revision

             cls._set_token_in_kwargs(_UpperCAmelCase  ,		_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   Optional[Any]												=	cls.get_config_dict(_UpperCAmelCase  ,		**_UpperCAmelCase						)

             # get the config dict if we are loading from Bark
             if config_dict.get('''model_type'''						) == "bark":
                    UpperCAmelCase__	:   List[str]												=	config_dict[F"""{cls.model_type}_config"""]

             if "model_type" in config_dict and hasattr(cls  ,		'''model_type'''						) and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""						)

             return cls.from_dict(_UpperCAmelCase  ,		**_UpperCAmelCase						)




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"      ,				model="BarkSemanticModel"	)      ,				"\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      ,				)
class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    int				       =					"semantic"




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"      ,				model="BarkCoarseModel"	)      ,				"\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      ,				)
class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Tuple				       =					"coarse_acoustics"




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"      ,				model="BarkFineModel"	)      ,				"\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"      ,				)
class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					"fine_acoustics"




      def __init__(					self  ,		_UpperCAmelCase=True  ,		_UpperCAmelCase=8  ,		_UpperCAmelCase=1  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Optional[Any]												=	n_codes_total
             UpperCAmelCase__	:   Union[str, Any]												=	n_codes_given

             super().__init__(tie_word_embeddings=_UpperCAmelCase  ,		**_UpperCAmelCase						)




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					"bark"
      SCREAMING_SNAKE_CASE				:    List[str]				       =					True




      def __init__(					self  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase = None  ,		_UpperCAmelCase=0.02  ,		**_UpperCAmelCase  ,		):
             if semantic_config is None:
                    UpperCAmelCase__	:   Union[str, Any]												=	{}
                    logger.info('''semantic_config is None. initializing the semantic model with default values.'''						)

             if coarse_acoustics_config is None:
                    UpperCAmelCase__	:   Optional[int]												=	{}
                    logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''						)

             if fine_acoustics_config is None:
                    UpperCAmelCase__	:   Dict												=	{}
                    logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''						)

             if codec_config is None:
                    UpperCAmelCase__	:   Tuple												=	{}
                    logger.info('''codec_config is None. initializing the codec model with default values.'''						)

             UpperCAmelCase__	:   int												=	BarkSemanticConfig(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	BarkCoarseConfig(**_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	BarkFineConfig(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	AutoConfig.for_model(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Dict												=	initializer_range

             super().__init__(**_UpperCAmelCase						)




      @classmethod
      def 							lowerCamelCase     (					cls  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		**_UpperCAmelCase  ,		):
             return cls(
                 semantic_config=semantic_config.to_dict()  ,		coarse_acoustics_config=coarse_acoustics_config.to_dict()  ,		fine_acoustics_config=fine_acoustics_config.to_dict()  ,		codec_config=codec_config.to_dict()  ,		**_UpperCAmelCase  ,		)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	copy.deepcopy(self.__dict__						)

             UpperCAmelCase__	:   int												=	self.semantic_config.to_dict()
             UpperCAmelCase__	:   Any												=	self.coarse_acoustics_config.to_dict()
             UpperCAmelCase__	:   Union[str, Any]												=	self.fine_acoustics_config.to_dict()
             UpperCAmelCase__	:   List[str]												=	self.codec_config.to_dict()

             UpperCAmelCase__	:   Tuple												=	self.__class__.model_type
             return output







'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 					__UpperCAmelCase (		UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				unittest.TestCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					StableDiffusionModelEditingPipeline
      SCREAMING_SNAKE_CASE				:    str				       =					TEXT_TO_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					TEXT_TO_IMAGE_BATCH_PARAMS
      SCREAMING_SNAKE_CASE				:    Dict				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def 							lowerCamelCase     (					self						):
             torch.manual_seed(0						)
             UpperCAmelCase__	:   List[Any]												=	UNetaDConditionModel(
                 block_out_channels=(32, 64)  ,		layers_per_block=2  ,		sample_size=32  ,		in_channels=4  ,		out_channels=4  ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,		cross_attention_dim=32  ,		)
             UpperCAmelCase__	:   List[str]												=	DDIMScheduler()
             torch.manual_seed(0						)
             UpperCAmelCase__	:   str												=	AutoencoderKL(
                 block_out_channels=[32, 64]  ,		in_channels=3  ,		out_channels=3  ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,		latent_channels=4  ,		)
             torch.manual_seed(0						)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextConfig(
                 bos_token_id=0  ,		eos_token_id=2  ,		hidden_size=32  ,		intermediate_size=37  ,		layer_norm_eps=1E-05  ,		num_attention_heads=4  ,		num_hidden_layers=5  ,		pad_token_id=1  ,		vocab_size=1000  ,		)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextModel(_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

             UpperCAmelCase__	:   List[str]												=	{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Tuple												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 # Setting height and width to None to prevent OOMs on CPU.
                 '''height''': None,
                 '''width''': None,
                 '''num_inference_steps''': 2,
                 '''guidance_scale''': 6.0,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Optional[int]												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]
             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   int												=	np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

             assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	'''french fries'''
             UpperCAmelCase__	:   Any												=	sd_pipe(**_UpperCAmelCase  ,		negative_prompt=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	output.images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   str												=	np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

             assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   str												=	EulerAncestralDiscreteScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''						)
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[Any]												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   int												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   Tuple												=	np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

             assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   str												=	self.get_dummy_components()
             UpperCAmelCase__	:   List[Any]												=	PNDMScheduler()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Tuple												=	self.get_dummy_inputs(_UpperCAmelCase						)
             # the pipeline does not expect pndm so test if it raises error.
             with self.assertRaises(_UpperCAmelCase						):
                    UpperCAmelCase__	:   List[Any]												=	sd_pipe(**_UpperCAmelCase						).images




      def 							lowerCamelCase     (					self						):
             super().test_inference_batch_single_identical(expected_max_diff=5E-3						)




      def 							lowerCamelCase     (					self						):
             super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




@slow
@require_torch_gpu
class 					__UpperCAmelCase (		unittest.TestCase	):

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Dict												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   List[str]												=	StableDiffusionModelEditingPipeline.from_pretrained(_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing()

             UpperCAmelCase__	:   Union[str, Any]												=	self.get_inputs()
             UpperCAmelCase__	:   Union[str, Any]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Optional[int]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             UpperCAmelCase__	:   List[str]												=	np.array(
                 [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

             assert np.abs(expected_slice - image_slice						).max() < 1E-2

             # make sure image changes after editing
             pipe.edit_model('''A pack of roses'''  ,		'''A pack of blue roses'''						)

             UpperCAmelCase__	:   Optional[int]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             assert np.abs(expected_slice - image_slice						).max() > 1E-1




      def 							lowerCamelCase     (					self						):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase__	:   Dict												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   int												=	DDIMScheduler.from_pretrained(_UpperCAmelCase  ,		subfolder='''scheduler'''						)
             UpperCAmelCase__	:   int												=	StableDiffusionModelEditingPipeline.from_pretrained(
                 _UpperCAmelCase  ,		scheduler=_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             UpperCAmelCase__	:   Tuple												=	pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing(1						)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase__	:   Any												=	self.get_inputs()
             UpperCAmelCase__	:   Any												=	pipe(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	torch.cuda.max_memory_allocated()
             # make sure that less than 4.4 GB is allocated
             assert mem_bytes < 4.4 * 10**9