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Grounded-Segment-Anything / transformers_4_35_0 /models /bark /configuration_bark.py

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	# coding=utf-8
	# Copyright 2023 The Suno AI Authors and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" BARK model configuration"""

	import os
	from typing import Dict, Optional, Union

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


	logger = logging.get_logger(__name__)


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

	BARK_SUBMODELCONFIG_START_DOCSTRING = """
	This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
	according to the specified arguments, defining the model architecture. Instantiating a configuration with the
	defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
	architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.

	Args:
	block_size (`int`, optional, defaults to 1024):
	The maximum sequence length that this model might ever be used with. Typically set this to something large
	just in case (e.g., 512 or 1024 or 2048).
	input_vocab_size (`int`, optional, defaults to 10_048):
	Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
	regards to the chosen sub-model.
	output_vocab_size (`int`, optional, defaults to 10_048):
	Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
	by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
	with regards to the chosen sub-model.
	num_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the given sub-model.
	num_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer architecture.
	hidden_size (`int`, optional, defaults to 768):
	Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
	dropout (`float`, optional, defaults to 0.0):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	bias (`bool`, optional, defaults to `True`):
	Whether or not to use bias in the linear layers and layer norm layers.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models).
	"""


	class BarkSubModelConfig(PretrainedConfig):
	model_type = "bark_module"
	keys_to_ignore_at_inference = ["past_key_values"]

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

	def __init__(
	self,
	block_size=1024,
	input_vocab_size=10_048,
	output_vocab_size=10_048,
	num_layers=12,
	num_heads=12,
	hidden_size=768,
	dropout=0.0,
	bias=True, # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
	initializer_range=0.02,
	use_cache=True,
	**kwargs,
	):
	self.block_size = block_size
	self.input_vocab_size = input_vocab_size
	self.output_vocab_size = output_vocab_size
	self.num_layers = num_layers
	self.num_heads = num_heads
	self.hidden_size = hidden_size
	self.dropout = dropout
	self.bias = bias
	self.use_cache = use_cache
	self.initializer_range = initializer_range

	super().__init__(**kwargs)

	@classmethod
	def from_pretrained(
	cls,
	pretrained_model_name_or_path: Union[str, os.PathLike],
	cache_dir: Optional[Union[str, os.PathLike]] = None,
	force_download: bool = False,
	local_files_only: bool = False,
	token: Optional[Union[str, bool]] = None,
	revision: str = "main",
	**kwargs,
	) -> "PretrainedConfig":
	kwargs["cache_dir"] = cache_dir
	kwargs["force_download"] = force_download
	kwargs["local_files_only"] = local_files_only
	kwargs["revision"] = revision

	cls._set_token_in_kwargs(kwargs, token)

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

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


	@add_start_docstrings(
	BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig", model="BarkSemanticModel"),
	"""
	Example:

	```python
	>>> from transformers import BarkSemanticConfig, BarkSemanticModel

	>>> # Initializing a Bark sub-module style configuration
	>>> configuration = BarkSemanticConfig()

	>>> # Initializing a model (with random weights) from the suno/bark style configuration
	>>> model = BarkSemanticModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```""",
	)
	class BarkSemanticConfig(BarkSubModelConfig):
	model_type = "semantic"


	@add_start_docstrings(
	BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig", model="BarkCoarseModel"),
	"""
	Example:

	```python
	>>> from transformers import BarkCoarseConfig, BarkCoarseModel

	>>> # Initializing a Bark sub-module style configuration
	>>> configuration = BarkCoarseConfig()

	>>> # Initializing a model (with random weights) from the suno/bark style configuration
	>>> model = BarkCoarseModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```""",
	)
	class BarkCoarseConfig(BarkSubModelConfig):
	model_type = "coarse_acoustics"


	@add_start_docstrings(
	BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig", model="BarkFineModel"),
	"""
	n_codes_total (`int`, optional, defaults to 8):
	The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
	n_codes_given (`int`, optional, defaults to 1):
	The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
	sub-models.
	Example:

	```python
	>>> from transformers import BarkFineConfig, BarkFineModel

	>>> # Initializing a Bark sub-module style configuration
	>>> configuration = BarkFineConfig()

	>>> # Initializing a model (with random weights) from the suno/bark style configuration
	>>> model = BarkFineModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```""",
	)
	class BarkFineConfig(BarkSubModelConfig):
	model_type = "fine_acoustics"

	def __init__(self, tie_word_embeddings=True, n_codes_total=8, n_codes_given=1, **kwargs):
	self.n_codes_total = n_codes_total
	self.n_codes_given = n_codes_given

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


	class BarkConfig(PretrainedConfig):
	"""
	This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark
	model according to the specified sub-models configurations, defining the model architecture.

	Instantiating a configuration with the defaults will yield a similar configuration to that of the Bark
	[suno/bark](https://huggingface.co/suno/bark) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.

	Args:
	semantic_config ([`BarkSemanticConfig`], optional):
	Configuration of the underlying semantic sub-model.
	coarse_acoustics_config ([`BarkCoarseConfig`], optional):
	Configuration of the underlying coarse acoustics sub-model.
	fine_acoustics_config ([`BarkFineConfig`], optional):
	Configuration of the underlying fine acoustics sub-model.
	codec_config ([`AutoConfig`], optional):
	Configuration of the underlying codec sub-model.

	Example:

	```python
	>>> from transformers import (
	... BarkSemanticConfig,
	... BarkCoarseConfig,
	... BarkFineConfig,
	... BarkModel,
	... BarkConfig,
	... AutoConfig,
	... )

	>>> # Initializing Bark sub-modules configurations.
	>>> semantic_config = BarkSemanticConfig()
	>>> coarse_acoustics_config = BarkCoarseConfig()
	>>> fine_acoustics_config = BarkFineConfig()
	>>> codec_config = AutoConfig.from_pretrained("facebook/encodec_24khz")


	>>> # Initializing a Bark module style configuration
	>>> configuration = BarkConfig.from_sub_model_configs(
	... semantic_config, coarse_acoustics_config, fine_acoustics_config, codec_config
	... )

	>>> # Initializing a model (with random weights)
	>>> model = BarkModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```
	"""

	model_type = "bark"

	def __init__(
	self,
	semantic_config: Dict = None,
	coarse_acoustics_config: Dict = None,
	fine_acoustics_config: Dict = None,
	codec_config: Dict = None,
	initializer_range=0.02,
	**kwargs,
	):
	if semantic_config is None:
	semantic_config = {}
	logger.info("semantic_config is None. initializing the semantic model with default values.")

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

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

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

	self.semantic_config = BarkSemanticConfig(**semantic_config)
	self.coarse_acoustics_config = BarkCoarseConfig(**coarse_acoustics_config)
	self.fine_acoustics_config = BarkFineConfig(**fine_acoustics_config)
	codec_model_type = codec_config["model_type"] if "model_type" in codec_config else "encodec"
	self.codec_config = CONFIG_MAPPING[codec_model_type](**codec_config)

	self.initializer_range = initializer_range

	super().__init__(**kwargs)

	@classmethod
	def from_sub_model_configs(
	cls,
	semantic_config: BarkSemanticConfig,
	coarse_acoustics_config: BarkCoarseConfig,
	fine_acoustics_config: BarkFineConfig,
	codec_config: PretrainedConfig,
	**kwargs,
	):
	r"""
	Instantiate a [`BarkConfig`] (or a derived class) from bark sub-models configuration.

	Returns:
	[`BarkConfig`]: An instance of a configuration object
	"""
	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(),
	**kwargs,
	)