


'''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				:							Optional[int]		       =		logging.get_logger(__name__)


__SCREAMING_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""",
}

__SCREAMING_SNAKE_CASE				:							str		       =		"""
    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 				__lowerCAmelCase      (      _a	):

      """simple docstring"""






      _UpperCAmelCase		:					Union[str, Any]      	="bark_module"
      _UpperCAmelCase		:					Optional[Any]      	=["past_key_values"]

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






      def __init__(     self       :			Tuple   ,     lowerCAmelCase       :			str=10_24   ,     lowerCAmelCase       :			Dict=1_00_48   ,     lowerCAmelCase       :			Tuple=1_00_48   ,     lowerCAmelCase       :			Optional[int]=12   ,     lowerCAmelCase       :			Union[str, Any]=12   ,     lowerCAmelCase       :			List[Any]=7_68   ,     lowerCAmelCase       :			Optional[int]=0.0   ,     lowerCAmelCase       :			Optional[int]=True   ,     lowerCAmelCase       :			Union[str, Any]=0.0_2   ,     lowerCAmelCase       :			List[str]=True   ,     **lowerCAmelCase       :			Tuple   ,     ):
             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 							_UpperCAmelCase					(     cls       :			Dict   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			Any = None   ,     lowerCAmelCase       :			Tuple = False   ,     lowerCAmelCase       :			Dict = False   ,     lowerCAmelCase       :			List[str] = None   ,     lowerCAmelCase       :			str = "main"   ,     **lowerCAmelCase       :			List[str]   ,     ):
             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 				__lowerCAmelCase      (      _a	):

      """simple docstring"""






      _UpperCAmelCase		:					Dict      	="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 				__lowerCAmelCase      (      _a	):

      """simple docstring"""






      _UpperCAmelCase		:					str      	="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 				__lowerCAmelCase      (      _a	):

      """simple docstring"""






      _UpperCAmelCase		:					Optional[int]      	="fine_acoustics"






      def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Tuple=True   ,     lowerCAmelCase       :			str=8   ,     lowerCAmelCase       :			Optional[Any]=1   ,     **lowerCAmelCase       :			Tuple  ):
             A_ 						=		n_codes_total
             A_ 						=		n_codes_given

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






class 				__lowerCAmelCase      (      _a	):

      """simple docstring"""






      _UpperCAmelCase		:					List[Any]      	="bark"
      _UpperCAmelCase		:					Union[str, Any]      	=True






      def __init__(     self       :			Tuple   ,     lowerCAmelCase       :			Any = None   ,     lowerCAmelCase       :			str = None   ,     lowerCAmelCase       :			int = None   ,     lowerCAmelCase       :			Dict = None   ,     lowerCAmelCase       :			int=0.0_2   ,     **lowerCAmelCase       :			Optional[Any]   ,     ):
             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 							_UpperCAmelCase					(     cls       :			Optional[int]   ,     lowerCAmelCase       :			int   ,     lowerCAmelCase       :			Union[str, Any]   ,     lowerCAmelCase       :			List[str]   ,     lowerCAmelCase       :			List[Any]   ,     **lowerCAmelCase       :			Optional[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       :			Dict  ):
             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 argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE				:							str		       =		logging.getLogger()







def 			a_							(  ):
							A_ 						=		argparse.ArgumentParser()
							parser.add_argument("-f"   )
							A_ 						=		parser.parse_args()
							return args.f







class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		logging.StreamHandler(sys.stdout  )
													logger.addHandler(lowerCAmelCase  )

													A_ 						=		self.get_auto_remove_tmp_dir()
													A_ 						=		F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

													with patch.object(lowerCAmelCase   ,     "argv"   ,     lowerCAmelCase  ):
																				A_ 						=		run_glue_with_pabee.main()
																				for value in result.values():
																											self.assertGreaterEqual(lowerCAmelCase   ,     0.7_5  )





