





'''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


snake_case_  :							Tuple            =	logging.get_logger(__name__)


snake_case_  :							List[Any]            =	{
    "suno/bark-small": "https://huggingface.co/suno/bark-small/resolve/main/config.json",
    "suno/bark": "https://huggingface.co/suno/bark/resolve/main/config.json",
}

snake_case_  :							Optional[Any]            =	"\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        __a   (lowerCamelCase  ):
							__a       : str          =							"bark_module"
							__a       : int          =							["past_key_values"]

							__a       : str          =							{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							    "vocab_size": "input_vocab_size",
							    "window_size": "block_size",
							}

							def __init__(				self				:  Any							,  __magic_name__				:  Union[str, Any]=10_24							,  __magic_name__				:  Optional[int]=1_00_48							,  __magic_name__				:  Optional[int]=1_00_48							,  __magic_name__				:  Union[str, Any]=12							,  __magic_name__				:  Tuple=12							,  __magic_name__				:  List[Any]=7_68							,  __magic_name__				:  Tuple=0.0							,  __magic_name__				:  Any=True							,  __magic_name__				:  List[str]=0.0_2							,  __magic_name__				:  Union[str, Any]=True							,  **__magic_name__				:  str							,  )       ->							List[str]:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	block_size
													UpperCAmelCase_      :   str      =	input_vocab_size
													UpperCAmelCase_      :   Any      =	output_vocab_size
													UpperCAmelCase_      :   List[Any]      =	num_layers
													UpperCAmelCase_      :   Dict      =	num_heads
													UpperCAmelCase_      :   Optional[Any]      =	hidden_size
													UpperCAmelCase_      :   Optional[int]      =	dropout
													UpperCAmelCase_      :   List[Any]      =	bias
													UpperCAmelCase_      :   str      =	use_cache
													UpperCAmelCase_      :   Tuple      =	initializer_range

													super().__init__(**__magic_name__ )



							@classmethod
							def      UpperCAmelCase__						(				cls				:  Dict							,  __magic_name__				:  Union[str, os.PathLike]							,  __magic_name__				:  Optional[Union[str, os.PathLike]] = None							,  __magic_name__				:  bool = False							,  __magic_name__				:  bool = False							,  __magic_name__				:  Optional[Union[str, bool]] = None							,  __magic_name__				:  str = "main"							,  **__magic_name__				:  List[str]							,  )       ->							"PretrainedConfig":







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	cache_dir
													UpperCAmelCase_      :   Tuple      =	force_download
													UpperCAmelCase_      :   List[Any]      =	local_files_only
													UpperCAmelCase_      :   Tuple      =	revision

													cls._set_token_in_kwargs(__magic_name__							,  __magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_      :   str      =	cls.get_config_dict(__magic_name__							,  **__magic_name__ )

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





@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        __a   (lowerCamelCase  ):
							__a       : str          =							"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        __a   (lowerCamelCase  ):
							__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        __a   (lowerCamelCase  ):
							__a       : Optional[int]          =							"fine_acoustics"

							def __init__(				self				:  Dict							,  __magic_name__				:  Optional[Any]=True							,  __magic_name__				:  Any=8							,  __magic_name__				:  Any=1							,  **__magic_name__				:  str )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	n_codes_total
													UpperCAmelCase_      :   List[str]      =	n_codes_given

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





class        __a   (lowerCamelCase  ):
							__a       : str          =							"bark"
							__a       : Optional[int]          =							True

							def __init__(				self				:  Union[str, Any]							,  __magic_name__				:  Dict = None							,  __magic_name__				:  Dict = None							,  __magic_name__				:  Dict = None							,  __magic_name__				:  Dict = None							,  __magic_name__				:  Optional[int]=0.0_2							,  **__magic_name__				:  str							,  )       ->							Any:







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

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

													if fine_acoustics_config is None:
																			UpperCAmelCase_      :   Optional[Any]      =	{}
																			logger.info('''fine_acoustics_config is None. initializing the fine model with default values.''' )

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

													UpperCAmelCase_      :   str      =	BarkSemanticConfig(**__magic_name__ )
													UpperCAmelCase_      :   Optional[int]      =	BarkCoarseConfig(**__magic_name__ )
													UpperCAmelCase_      :   str      =	BarkFineConfig(**__magic_name__ )
													UpperCAmelCase_      :   Dict      =	AutoConfig.for_model(**__magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	initializer_range

													super().__init__(**__magic_name__ )

							@classmethod
							def      UpperCAmelCase__						(				cls				:  int							,  __magic_name__				:  BarkSemanticConfig							,  __magic_name__				:  BarkCoarseConfig							,  __magic_name__				:  BarkFineConfig							,  __magic_name__				:  AutoConfig							,  **__magic_name__				:  Optional[int]							,  )       ->							List[str]:







													"""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()							,  **__magic_name__							,  )



							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							List[str]:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	copy.deepcopy(self.__dict__ )

													UpperCAmelCase_      :   Dict      =	self.semantic_config.to_dict()
													UpperCAmelCase_      :   int      =	self.coarse_acoustics_config.to_dict()
													UpperCAmelCase_      :   Any      =	self.fine_acoustics_config.to_dict()
													UpperCAmelCase_      :   List[Any]      =	self.codec_config.to_dict()

													UpperCAmelCase_      :   str      =	self.__class__.model_type
													return output









import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )