
'''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__					:						str											=     logging.get_logger(__name__)


UpperCamelCase__					:						int											=     {
    "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__					:						Tuple											=     "\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):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'bark_module'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']

       SCREAMING_SNAKE_CASE					=     {
           'num_attention_heads': 'num_heads',
           'num_hidden_layers': 'num_layers',
           'vocab_size': 'input_vocab_size',
           'window_size': 'block_size',
       }






       def __init__(							self				,							A__=10_24				,							A__=1_00_48				,							A__=1_00_48				,							A__=12				,							A__=12				,							A__=7_68				,							A__=0.0				,							A__=True				,							A__=0.02				,							A__=True				,							**A__				,							)			->	Dict:
          _SCREAMING_SNAKE_CASE          = block_size
          _SCREAMING_SNAKE_CASE          = input_vocab_size
          _SCREAMING_SNAKE_CASE          = output_vocab_size
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = hidden_size
          _SCREAMING_SNAKE_CASE          = dropout
          _SCREAMING_SNAKE_CASE          = bias
          _SCREAMING_SNAKE_CASE          = use_cache
          _SCREAMING_SNAKE_CASE          = initializer_range

          super().__init__(**A__					)






       @classmethod
       def 				UpperCamelCase (							cls				,							A__				,							A__ = None				,							A__ = False				,							A__ = False				,							A__ = None				,							A__ = "main"				,							**A__				,							)			->	"PretrainedConfig":
          _SCREAMING_SNAKE_CASE          = cache_dir
          _SCREAMING_SNAKE_CASE          = force_download
          _SCREAMING_SNAKE_CASE          = local_files_only
          _SCREAMING_SNAKE_CASE          = revision

          cls._set_token_in_kwargs(A__				,							A__					)

          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = cls.get_config_dict(A__				,							**A__					)

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






@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):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     '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):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     '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):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'fine_acoustics'






       def __init__(							self				,							A__=True				,							A__=8				,							A__=1				,							**A__					)			->	int:
          _SCREAMING_SNAKE_CASE          = n_codes_total
          _SCREAMING_SNAKE_CASE          = n_codes_given

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






class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'bark'
       SCREAMING_SNAKE_CASE					=     True






       def __init__(							self				,							A__ = None				,							A__ = None				,							A__ = None				,							A__ = None				,							A__=0.02				,							**A__				,							)			->	Optional[Any]:
          if semantic_config is None:
             _SCREAMING_SNAKE_CASE          = {}
             logger.info("""semantic_config is None. initializing the semantic model with default values."""					)

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

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

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

          _SCREAMING_SNAKE_CASE          = BarkSemanticConfig(**A__					)
          _SCREAMING_SNAKE_CASE          = BarkCoarseConfig(**A__					)
          _SCREAMING_SNAKE_CASE          = BarkFineConfig(**A__					)
          _SCREAMING_SNAKE_CASE          = AutoConfig.for_model(**A__					)

          _SCREAMING_SNAKE_CASE          = initializer_range

          super().__init__(**A__					)






       @classmethod
       def 				UpperCamelCase (							cls				,							A__				,							A__				,							A__				,							A__				,							**A__				,							)			->	str:
          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()				,							**A__				,							)






       def 				UpperCamelCase (							self					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = copy.deepcopy(self.__dict__					)

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

          _SCREAMING_SNAKE_CASE          = self.__class__.model_type
          return output








'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

          # set step values
          self.scheduler.set_timesteps(A__					)

          for t in self.progress_bar(self.scheduler.timesteps					):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE          = self.unet(A__				,							A__					).sample

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

          _SCREAMING_SNAKE_CASE          = (image / 2 + 0.5).clamp(0				,							1					)
          _SCREAMING_SNAKE_CASE          = image.cpu().permute(0				,							2				,							3				,							1					).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE          = self.numpy_to_pil(A__					)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






