






"""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


lowerCamelCase__			=   logging.get_logger(__name__)


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

lowerCamelCase__			=   """
    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 					_UpperCamelCase		( _a):
    __lowerCamelCase         =  "bark_module"
    __lowerCamelCase         =  ["past_key_values"]

    __lowerCamelCase         =  {
        "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__=0.0_2						,      lowerCamelCase__=True						,      **lowerCamelCase__						,      ):


      """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  A    (cls						,      lowerCamelCase__						,      lowerCamelCase__ = None						,      lowerCamelCase__ = False						,      lowerCamelCase__ = False						,      lowerCamelCase__ = None						,      lowerCamelCase__ = "main"						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							cache_dir
      A__											=							force_download
      A__											=							local_files_only
      A__											=							revision

      cls._set_token_in_kwargs(lowerCamelCase__						,      lowerCamelCase__   )

      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 					_UpperCamelCase		( _a):
    __lowerCamelCase         =  "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 					_UpperCamelCase		( _a):
    __lowerCamelCase         =  "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 					_UpperCamelCase		( _a):
    __lowerCamelCase         =  "fine_acoustics"






    def __init__(self						,      lowerCamelCase__=True						,      lowerCamelCase__=8						,      lowerCamelCase__=1						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							n_codes_total
      A__											=							n_codes_given

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







class 					_UpperCamelCase		( _a):
    __lowerCamelCase         =  "bark"
    __lowerCamelCase         =  True






    def __init__(self						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__ = None						,      lowerCamelCase__=0.0_2						,      **lowerCamelCase__						,      ):


      """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  A    (cls						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      **lowerCamelCase__						,      ):


      """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  A    (self   ):


      """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












"""simple docstring"""




import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
      from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
      lowerCamelCase__			=   None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      lowerCamelCase__			=   None
      lowerCamelCase__			=   None
      lowerCamelCase__			=   None






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any    ):
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    A__											=							unittest.skip("""test requires Datasets, Faiss, psutil"""    )(UpperCamelCase    )
  return test_case







@require_distributed_retrieval
class 					_UpperCamelCase		( __snake_case):






    def  A    (self   ):


      """simple docstring"""
      A__											=							tempfile.mkdtemp()
      A__											=							8

      # DPR tok
      A__											=							[
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      A__											=							os.path.join(self.tmpdirname						,      """dpr_tokenizer"""   )
      os.makedirs(lowerCamelCase__						,      exist_ok=lowerCamelCase__   )
      A__											=							os.path.join(lowerCamelCase__						,      DPR_VOCAB_FILES_NAMES["""vocab_file"""]   )
      with open(self.vocab_file						,      """w"""						,      encoding="""utf-8"""   ) as vocab_writer:
        vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]   )   )

      # BART tok
      A__											=							[
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      A__											=							dict(zip(lowerCamelCase__						,      range(len(lowerCamelCase__   )   )   )   )
      A__											=							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      A__											=							{"""unk_token""": """<unk>"""}

      A__											=							os.path.join(self.tmpdirname						,      """bart_tokenizer"""   )
      os.makedirs(lowerCamelCase__						,      exist_ok=lowerCamelCase__   )
      A__											=							os.path.join(lowerCamelCase__						,      BART_VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							os.path.join(lowerCamelCase__						,      BART_VOCAB_FILES_NAMES["""merges_file"""]   )
      with open(self.vocab_file						,      """w"""						,      encoding="""utf-8"""   ) as fp:
        fp.write(json.dumps(lowerCamelCase__   ) + """\n"""   )
      with open(self.merges_file						,      """w"""						,      encoding="""utf-8"""   ) as fp:
        fp.write("""\n""".join(lowerCamelCase__   )   )






    def  A    (self   ):


      """simple docstring"""
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,      """dpr_tokenizer"""   )   )






    def  A    (self   ):


      """simple docstring"""
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,      """bart_tokenizer"""   )   )






    def  A    (self   ):


      """simple docstring"""
      shutil.rmtree(self.tmpdirname   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							Dataset.from_dict(
          {
              """id""": ["""0""", """1"""],
              """text""": ["""foo""", """bar"""],
              """title""": ["""Foo""", """Bar"""],
              """embeddings""": [np.ones(self.retrieval_vector_size   ), 2 * np.ones(self.retrieval_vector_size   )],
          }   )
      dataset.add_faiss_index("""embeddings"""						,      string_factory="""Flat"""						,      metric_type=faiss.METRIC_INNER_PRODUCT   )
      return dataset






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=1_2_3_4_5   ):


      """simple docstring"""
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      )
      with patch("""transformers.models.rag.retrieval_rag.load_dataset"""   ) as mock_load_dataset:
        A__											=							dataset
        A__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
        if init_retrieval:
          retriever.init_retrieval(lowerCamelCase__   )
      return retriever






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      # Have to run in local mode because sys.path modifications at top of
      # file are not propogated to remote workers.
      # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
      ray.init(local_mode=lowerCamelCase__   )
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      )
      A__											=							ray.remote(lowerCamelCase__   )
      A__											=							[remote_cls.remote() for _ in range(1   )]
      with patch("""transformers.models.rag.retrieval_rag.load_dataset"""   ) as mock_load_dataset:
        A__											=							self.get_dummy_dataset()
        A__											=							RagRayDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowerCamelCase__						,      )
        if init_retrieval:
          retriever.init_retrieval()
      return retriever






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=1_2_3_4_5   ):


      """simple docstring"""
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name="""custom"""						,      )
      if from_disk:
        A__											=							os.path.join(self.tmpdirname						,      """dataset"""   )
        A__											=							os.path.join(self.tmpdirname						,      """index.faiss"""   )
        dataset.get_index("""embeddings"""   ).save(os.path.join(self.tmpdirname						,      """index.faiss"""   )   )
        dataset.drop_index("""embeddings"""   )
        dataset.save_to_disk(os.path.join(self.tmpdirname						,      """dataset"""   )   )
        del dataset
        A__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
      else:
        A__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      index=CustomHFIndex(config.retrieval_vector_size						,      lowerCamelCase__   )						,      )
      if init_retrieval:
        retriever.init_retrieval(lowerCamelCase__   )
      return retriever






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      # Have to run in local mode because sys.path modifications at top of
      # file are not propogated to remote workers.
      # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
      ray.init(local_mode=lowerCamelCase__   )
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name="""custom"""						,      )
      A__											=							ray.remote(lowerCamelCase__   )
      A__											=							[remote_cls.remote() for _ in range(1   )]
      if from_disk:
        A__											=							os.path.join(self.tmpdirname						,      """dataset"""   )
        A__											=							os.path.join(self.tmpdirname						,      """index.faiss"""   )
        dataset.get_index("""embeddings"""   ).save(os.path.join(self.tmpdirname						,      """index.faiss"""   )   )
        dataset.drop_index("""embeddings"""   )
        dataset.save_to_disk(os.path.join(self.tmpdirname						,      """dataset"""   )   )
        del dataset
        A__											=							RagRayDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowerCamelCase__						,      index=CustomHFIndex.load_from_disk(
                vector_size=config.retrieval_vector_size						,      dataset_path=config.passages_path						,      index_path=config.index_path						,      )						,      )
      else:
        A__											=							RagRayDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowerCamelCase__						,      index=CustomHFIndex(config.retrieval_vector_size						,      lowerCamelCase__   )						,      )
      if init_retrieval:
        retriever.init_retrieval()
      return retriever






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__						,A__						,A__											=							retriever.retrieve(lowerCamelCase__						,      n_docs=lowerCamelCase__   )
      self.assertEqual(retrieved_doc_embeds.shape						,      (2, n_docs, self.retrieval_vector_size)   )
      self.assertEqual(len(lowerCamelCase__   )						,      2   )
      self.assertEqual(sorted(doc_dicts[0]   )						,      ["""embeddings""", """id""", """text""", """title"""]   )
      self.assertEqual(len(doc_dicts[0]["""id"""]   )						,      lowerCamelCase__   )
      self.assertEqual(doc_dicts[0]["""id"""][0]						,      """1"""   )  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["""id"""][0]						,      """0"""   )  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()						,      [[1], [0]]   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__						,      )






    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__						,      )






    @require_ray
    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__   )
      ray.shutdown()






    @require_ray
    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )
      with self.assertRaises(lowerCamelCase__   ):
        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__						,      )
      ray.shutdown()






    @require_ray
    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__   )
      ray.shutdown()




