




"""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


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE_      =					'\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_										=					'''bark_module'''
   A_										=					['''past_key_values''']

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







   def __init__(		self					,				lowerCamelCase_=1_0_2_4					,				lowerCamelCase_=1_0_0_4_8					,				lowerCamelCase_=1_0_0_4_8					,				lowerCamelCase_=1_2					,				lowerCamelCase_=1_2					,				lowerCamelCase_=7_6_8					,				lowerCamelCase_=0.0					,				lowerCamelCase_=True					,				lowerCamelCase_=0.02					,				lowerCamelCase_=True					,				**lowerCamelCase_					,				)							->      List[Any]:
       UpperCamelCase										=  block_size
       UpperCamelCase										=  input_vocab_size
       UpperCamelCase										=  output_vocab_size
       UpperCamelCase										=  num_layers
       UpperCamelCase										=  num_heads
       UpperCamelCase										=  hidden_size
       UpperCamelCase										=  dropout
       UpperCamelCase										=  bias
       UpperCamelCase										=  use_cache
       UpperCamelCase										=  initializer_range

       super().__init__(**lowerCamelCase_)







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

       cls._set_token_in_kwargs(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase					,  UpperCamelCase										=  cls.get_config_dict(lowerCamelCase_					,				**lowerCamelCase_)

       # get the config dict if we are loading from Bark
       if config_dict.get('''model_type''') == "bark":
           UpperCamelCase										=  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''' )  ,   '''
    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 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''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 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''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 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''fine_acoustics'''







   def __init__(		self					,				lowerCamelCase_=True					,				lowerCamelCase_=8					,				lowerCamelCase_=1					,				**lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  n_codes_total
       UpperCamelCase										=  n_codes_given

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

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					'''bark'''
   A_										=					True







   def __init__(		self					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				lowerCamelCase_=0.02					,				**lowerCamelCase_					,				)							->      int:
       if semantic_config is None:
           UpperCamelCase										=  {}
           logger.info('''semantic_config is None. initializing the semantic model with default values.''')

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

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

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

       UpperCamelCase										=  BarkSemanticConfig(**lowerCamelCase_)
       UpperCamelCase										=  BarkCoarseConfig(**lowerCamelCase_)
       UpperCamelCase										=  BarkFineConfig(**lowerCamelCase_)
       UpperCamelCase										=  AutoConfig.for_model(**lowerCamelCase_)

       UpperCamelCase										=  initializer_range

       super().__init__(**lowerCamelCase_)







   @classmethod
   def      UpperCAmelCase__ (		cls					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				**lowerCamelCase_					,				)							->      List[Any]:
       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      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  copy.deepcopy(self.__dict__)

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

       UpperCamelCase										=  self.__class__.model_type
       return output





"""simple docstring"""







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  Dataset.from_dict({'''i''': range(17						)}						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  3
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(_lowercase						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





@pytest.mark.parametrize('''shards_per_node''' ,[1, 2, 3]						)
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    def gen(_lowercase						):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  shards_per_node * world_size
    UpperCamelCase										=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowercase						)]}
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase ,gen_kwargs=_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  2
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)

    UpperCamelCase										=  split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle(seed=42						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle()

    UpperCamelCase										=  split_dataset_by_node(full_ds.shuffle(seed=42						) ,rank=0 ,world_size=_lowercase						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(full_ds.shuffle() ,rank=0 ,world_size=_lowercase						)





@pytest.mark.parametrize('''streaming''' ,[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  2
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)





@pytest.mark.parametrize(
    '''nproc_per_node, num_workers''' ,[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ] ,)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  True
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)