






"""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


lowercase_								=	logging.get_logger(__name__)


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

lowercase_								=	'\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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Tuple							=					"bark_module"
   A_		:      int							=					["past_key_values"]

   A_		:      List[str]							=					{
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
       "vocab_size": "input_vocab_size",
       "window_size": "block_size",
   }


   def __init__(      self					:	str,			_lowerCamelCase					:	Union[str, Any]=10_24,			_lowerCamelCase					:	Optional[Any]=1_00_48,			_lowerCamelCase					:	Dict=1_00_48,			_lowerCamelCase					:	str=12,			_lowerCamelCase					:	Union[str, Any]=12,			_lowerCamelCase					:	Dict=7_68,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Union[str, Any]=True,			_lowerCamelCase					:	str=0.02,			_lowerCamelCase					:	Tuple=True,			**_lowerCamelCase					:	Optional[Any],			):

     '''simple docstring'''
     __A		     =					block_size
     __A		     =					input_vocab_size
     __A		     =					output_vocab_size
     __A		     =					num_layers
     __A		     =					num_heads
     __A		     =					hidden_size
     __A		     =					dropout
     __A		     =					bias
     __A		     =					use_cache
     __A		     =					initializer_range

     super().__init__(**_lowerCamelCase    )






   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	str,			_lowerCamelCase					:	Union[str, os.PathLike],			_lowerCamelCase					:	Optional[Union[str, os.PathLike]] = None,			_lowerCamelCase					:	bool = False,			_lowerCamelCase					:	bool = False,			_lowerCamelCase					:	Optional[Union[str, bool]] = None,			_lowerCamelCase					:	str = "main",			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     __A		     =					cache_dir
     __A		     =					force_download
     __A		     =					local_files_only
     __A		     =					revision

     cls._set_token_in_kwargs(_lowerCamelCase,			_lowerCamelCase    )

     __A     ,       __A		     =					cls.get_config_dict(_lowerCamelCase,			**_lowerCamelCase    )

     # get the config dict if we are loading from Bark
     if config_dict.get('''model_type'''    ) == "bark":
       __A		     =					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(_lowerCamelCase,			**_lowerCamelCase    )





@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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Union[str, Any]							=					"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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      str							=					"fine_acoustics"


   def __init__(      self					:	Dict,			_lowerCamelCase					:	Union[str, Any]=True,			_lowerCamelCase					:	List[str]=8,			_lowerCamelCase					:	Optional[int]=1,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     __A		     =					n_codes_total
     __A		     =					n_codes_given

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





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"bark"
   A_		:      Tuple							=					True


   def __init__(      self					:	Union[str, Any],			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Dict = None,			_lowerCamelCase					:	Tuple=0.02,			**_lowerCamelCase					:	List[str],			):

     '''simple docstring'''
     if semantic_config is None:
       __A		     =					{}
       logger.info('''semantic_config is None. initializing the semantic model with default values.'''    )

     if coarse_acoustics_config is None:
       __A		     =					{}
       logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''    )

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

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

     __A		     =					BarkSemanticConfig(**_lowerCamelCase    )
     __A		     =					BarkCoarseConfig(**_lowerCamelCase    )
     __A		     =					BarkFineConfig(**_lowerCamelCase    )
     __A		     =					AutoConfig.for_model(**_lowerCamelCase    )

     __A		     =					initializer_range

     super().__init__(**_lowerCamelCase    )


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	List[Any],			_lowerCamelCase					:	BarkSemanticConfig,			_lowerCamelCase					:	BarkCoarseConfig,			_lowerCamelCase					:	BarkFineConfig,			_lowerCamelCase					:	AutoConfig,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     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(),			**_lowerCamelCase,			)






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					copy.deepcopy(self.__dict__    )

     __A		     =					self.semantic_config.to_dict()
     __A		     =					self.coarse_acoustics_config.to_dict()
     __A		     =					self.fine_acoustics_config.to_dict()
     __A		     =					self.codec_config.to_dict()

     __A		     =					self.__class__.model_type
     return output











import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							_a(     __A						, unittest.TestCase     ):
		lowerCamelCase__  :Optional[int] 					=		PaintByExamplePipeline
		lowerCamelCase__  :Union[str, Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		lowerCamelCase__  :Optional[Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCamelCase__  :int 					=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							torch.manual_seed(0					)
							_snake_case							:       Union[str, Any]												= UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=9			,    out_channels=4			,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,    cross_attention_dim=3_2			,    )
							_snake_case							:       int												= PNDMScheduler(skip_prk_steps=__snake_case					)
							torch.manual_seed(0					)
							_snake_case							:       Any												= AutoencoderKL(
							    block_out_channels=[3_2, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,    latent_channels=4			,    )
							torch.manual_seed(0					)
							_snake_case							:       Dict												= CLIPVisionConfig(
							    hidden_size=3_2			,    projection_dim=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1E-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=3_2			,    patch_size=4			,    )
							_snake_case							:       Any												= PaintByExampleImageEncoder(__snake_case			,    proj_size=3_2					)
							_snake_case							:       Dict												= CLIPImageProcessor(crop_size=3_2			,    size=3_2					)

							_snake_case							:       Any												= {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components







		def 							lowercase							(     self			,    __snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       List[str]												= np.array(image.convert("RGB"					)					)
							_snake_case							:       str												= image[None].transpose(0			,    3			,    1			,    2					)
							_snake_case							:       Dict												= torch.from_numpy(__snake_case					).to(dtype=torch.floataa					) / 1_27.5 - 1.0
							return image







		def 							lowercase							(     self			,    __snake_case="cpu"			,    __snake_case=0					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Tuple												= floats_tensor((1, 3, 3_2, 3_2)			,    rng=random.Random(__snake_case					)					).to(__snake_case					)
							_snake_case							:       Union[str, Any]												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((3_2, 3_2)					)

							if str(__snake_case					).startswith("mps"					):
												_snake_case							:       Union[str, Any]												= torch.manual_seed(__snake_case					)
							else:
												_snake_case							:       List[Any]												= torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
							_snake_case							:       Optional[int]												= {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							_snake_case							:       int												= PaintByExamplePipeline(**__snake_case					)
							_snake_case							:       List[Any]												= pipe.to("cpu"					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       List[str]												= self.get_dummy_inputs()
							_snake_case							:       int												= pipe(**__snake_case					)
							_snake_case							:       Dict												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							_snake_case							:       int												= np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]					)

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







		def 							lowercase							(     self					)   ->   List[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= "cpu"
							_snake_case							:       str												= self.get_dummy_inputs()
							inputs.pop("mask_image"					)
							_snake_case							:       Union[str, Any]												= self.convert_to_pt(inputs.pop("image"					)					)
							_snake_case							:       Union[str, Any]												= image.clamp(0			,    1					) / 2

							# make sure here that pndm scheduler skips prk
							_snake_case							:       Dict												= PaintByExamplePipeline(**self.get_dummy_components()					)
							_snake_case							:       Union[str, Any]												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       int												= pipe(image=__snake_case			,    mask_image=mask_image[:, 0]			,    **__snake_case					)
							_snake_case							:       Optional[int]												= output.images

							_snake_case							:       Any												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[str]												= mask_image.cpu().permute(0			,    2			,    3			,    1					)[0]

							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)
							_snake_case							:       int												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)

							_snake_case							:       Optional[int]												= pipe(**self.get_dummy_inputs()					)
							_snake_case							:       Dict												= output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







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


							'''simple docstring'''

							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






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







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


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Tuple												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"					)
							_snake_case							:       Optional[Any]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"					)
							_snake_case							:       Optional[int]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"					)

							_snake_case							:       Any												= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
							_snake_case							:       Any												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       Union[str, Any]												= torch.manual_seed(3_2_1					)
							_snake_case							:       int												= pipe(
							    image=__snake_case			,    mask_image=__snake_case			,    example_image=__snake_case			,    generator=__snake_case			,    guidance_scale=5.0			,    num_inference_steps=5_0			,    output_type="np"			,    )

							_snake_case							:       str												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							_snake_case							:       int												= np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]					)

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