
"""simple docstring"""
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


_a   :      Optional[Any]       =				"""1.5.0"""

_a   :      Optional[Any]       =				["""vision-encoder-decoder"""]
def 					a__ ( a :					Any		):

  """simple docstring"""







  if is_optimum_available():
    from optimum.version import __version__ as optimum_version

    _snake_case  :    Dict            = version.parse(a		)
    if parsed_optimum_version < version.parse(a		):
      raise RuntimeError(
          f'transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You '
          "can upgrade optimum by running: pip install -U optimum[exporters]"		)
  else:
    raise RuntimeError(
        "transformers.onnx requires optimum to run, you can install the library by running: pip install "
        "optimum[exporters]"		)
  _snake_case  :    str            = [
      sys.executable,
      "-m",
      "optimum.exporters.onnx",
      f'--model {args.model}',
      f'--task {args.feature}',
      f'--framework {args.framework}' if args.framework is not None else "",
      f'{args.output}',
  ]
  _snake_case  :    str            = subprocess.Popen(" ".join(a		)							,  stdout=subprocess.PIPE							,  shell=a		)
  proc.wait()

  logger.info(
      "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
      "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
      "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."		)
def 					a__ ( a :					Dict		):

  """simple docstring"""







  _snake_case  :    Tuple            = args.output if args.output.is_file() else args.output.joinpath("model.onnx"		)
  if not args.output.parent.exists():
    args.output.parent.mkdir(parents=a		)

  # Allocate the model
  _snake_case  :    Optional[Any]            = FeaturesManager.get_model_from_feature(
      args.feature							,  args.model							,  framework=args.framework							,  cache_dir=args.cache_dir		)

  _snake_case      ,       _snake_case  :    Optional[Any]            = FeaturesManager.check_supported_model_or_raise(a							,  feature=args.feature		)
  _snake_case  :    Union[str, Any]            = model_onnx_config(model.config		)

  if model_kind in ENCODER_DECODER_MODELS:
    _snake_case  :    Optional[Any]            = model.get_encoder()
    _snake_case  :    Dict            = model.get_decoder()

    _snake_case  :    Any            = onnx_config.get_encoder_config(encoder_model.config		)
    _snake_case  :    str            = onnx_config.get_decoder_config(
        encoder_model.config							,  decoder_model.config							,  feature=args.feature		)

    if args.opset is None:
      _snake_case  :    str            = max(encoder_onnx_config.default_onnx_opset							,  decoder_onnx_config.default_onnx_opset		)

    if args.opset < min(encoder_onnx_config.default_onnx_opset							,  decoder_onnx_config.default_onnx_opset		):
      raise ValueError(
          f'Opset {args.opset} is not sufficient to export {model_kind}. At least '
          f' {min(encoder_onnx_config.default_onnx_opset							,  decoder_onnx_config.default_onnx_opset		)} is required.'		)

    _snake_case  :    Any            = AutoFeatureExtractor.from_pretrained(args.model		)

    _snake_case      ,       _snake_case  :    str            = export(
        a							,  a							,  a							,  args.opset							,  args.output.parent.joinpath("encoder_model.onnx"		)							,  )

    validate_model_outputs(
        a							,  a							,  a							,  args.output.parent.joinpath("encoder_model.onnx"		)							,  a							,  args.atol if args.atol else encoder_onnx_config.atol_for_validation							,  )

    _snake_case  :    str            = AutoTokenizer.from_pretrained(args.model		)

    _snake_case      ,       _snake_case  :    List[Any]            = export(
        a							,  a							,  a							,  args.opset							,  args.output.parent.joinpath("decoder_model.onnx"		)							,  )

    validate_model_outputs(
        a							,  a							,  a							,  args.output.parent.joinpath("decoder_model.onnx"		)							,  a							,  args.atol if args.atol else decoder_onnx_config.atol_for_validation							,  )
    logger.info(
        f'All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"		).as_posix()},'
        f' {args.output.parent.joinpath("decoder_model.onnx"		).as_posix()}'		)

  else:
    # Instantiate the appropriate preprocessor
    if args.preprocessor == "auto":
      _snake_case  :    int            = get_preprocessor(args.model		)
    elif args.preprocessor == "tokenizer":
      _snake_case  :    str            = AutoTokenizer.from_pretrained(args.model		)
    elif args.preprocessor == "image_processor":
      _snake_case  :    Dict            = AutoImageProcessor.from_pretrained(args.model		)
    elif args.preprocessor == "feature_extractor":
      _snake_case  :    Optional[Any]            = AutoFeatureExtractor.from_pretrained(args.model		)
    elif args.preprocessor == "processor":
      _snake_case  :    int            = AutoProcessor.from_pretrained(args.model		)
    else:
      raise ValueError(f'Unknown preprocessor type \'{args.preprocessor}\''		)

    # Ensure the requested opset is sufficient
    if args.opset is None:
      _snake_case  :    List[str]            = onnx_config.default_onnx_opset

    if args.opset < onnx_config.default_onnx_opset:
      raise ValueError(
          f'Opset {args.opset} is not sufficient to export {model_kind}. '
          f'At least  {onnx_config.default_onnx_opset} is required.'		)

    _snake_case      ,       _snake_case  :    Tuple            = export(
        a							,  a							,  a							,  args.opset							,  args.output							,  )

    if args.atol is None:
      _snake_case  :    Optional[Any]            = onnx_config.atol_for_validation

    validate_model_outputs(a							,  a							,  a							,  args.output							,  a							,  args.atol		)
    logger.info(f'All good, model saved at: {args.output.as_posix()}'		)
    warnings.warn(
        "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
        " using optimum.exporters.onnx in future. You can find more information here:"
        " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."							,  a							,  )
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    List[Any]            = ArgumentParser("Hugging Face Transformers ONNX exporter"		)
  parser.add_argument(
      "-m"							,  "--model"							,  type=a							,  required=a							,  help="Model ID on huggingface.co or path on disk to load model from."		)
  parser.add_argument(
      "--feature"							,  default="default"							,  help="The type of features to export the model with."							,  )
  parser.add_argument("--opset"							,  type=a							,  default=a							,  help="ONNX opset version to export the model with."		)
  parser.add_argument(
      "--atol"							,  type=a							,  default=a							,  help="Absolute difference tolerance when validating the model."		)
  parser.add_argument(
      "--framework"							,  type=a							,  choices=["pt", "tf"]							,  default=a							,  help=(
          "The framework to use for the ONNX export."
          " If not provided, will attempt to use the local checkpoint's original framework"
          " or what is available in the environment."
      )							,  )
  parser.add_argument("output"							,  type=a							,  help="Path indicating where to store generated ONNX model."		)
  parser.add_argument("--cache_dir"							,  type=a							,  default=a							,  help="Path indicating where to store cache."		)
  parser.add_argument(
      "--preprocessor"							,  type=a							,  choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]							,  default="auto"							,  help="Which type of preprocessor to use. 'auto' tries to automatically detect it."							,  )
  parser.add_argument(
      "--export_with_transformers"							,  action="store_true"							,  help=(
          "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
          "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
          "recommended."
      )							,  )

  _snake_case  :    str            = parser.parse_args()
  if args.export_with_transformers or not is_optimum_available():
    export_with_transformers(a		)
  else:
    export_with_optimum(a		)


if __name__ == "__main__":
    _a   :      int       =				logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
    logger.setLevel(logging.INFO)
    main()









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_					:		Dict           = logging.get_logger(__name__)


UpperCAmelCase_					:		Dict           = {
    '''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_					:		Any           = '''
    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        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	str	      =    """bark_module"""
	snake_case__				:	int	      =    ["""past_key_values"""]

	snake_case__				:	Optional[int]	      =    {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	    """vocab_size""": """input_vocab_size""",
	    """window_size""": """block_size""",
	}





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   List[Any]=1_024					,    __lowerCamelCase							:   int=10_048					,    __lowerCamelCase							:   List[Any]=10_048					,    __lowerCamelCase							:   str=12					,    __lowerCamelCase							:   Dict=12					,    __lowerCamelCase							:   Tuple=768					,    __lowerCamelCase							:   Dict=0.0					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Tuple=0.02					,    __lowerCamelCase							:   List[str]=True					,    **__lowerCamelCase							:   str					,    ):
		UpperCamelCase	:Optional[int]		=      block_size
		UpperCamelCase	:List[Any]		=      input_vocab_size
		UpperCamelCase	:int		=      output_vocab_size
		UpperCamelCase	:int		=      num_layers
		UpperCamelCase	:Union[str, Any]		=      num_heads
		UpperCamelCase	:Dict		=      hidden_size
		UpperCamelCase	:Any		=      dropout
		UpperCamelCase	:List[str]		=      bias
		UpperCamelCase	:Tuple		=      use_cache
		UpperCamelCase	:Any		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   Union[str, os.PathLike]					,    __lowerCamelCase							:   Optional[Union[str, os.PathLike]] = None					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   Optional[Union[str, bool]] = None					,    __lowerCamelCase							:   str = "main"					,    **__lowerCamelCase							:   List[Any]					,    ):
		UpperCamelCase	:Dict		=      cache_dir
		UpperCamelCase	:List[Any]		=      force_download
		UpperCamelCase	:List[Any]		=      local_files_only
		UpperCamelCase	:int		=      revision

		cls._set_token_in_kwargs(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	, UpperCamelCase	:Dict		=      cls.get_config_dict(__lowerCamelCase					,    **__lowerCamelCase      )

		# get the config dict if we are loading from Bark
		if config_dict.get("""model_type"""      ) == "bark":
			UpperCamelCase	:List[str]		=      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        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Optional[int]	      =    """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        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	int	      =    """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        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	List[Any]	      =    """fine_acoustics"""





	def __init__(						self							:   Any					,    __lowerCamelCase							:   str=True					,    __lowerCamelCase							:   Optional[int]=8					,    __lowerCamelCase							:   Dict=1					,    **__lowerCamelCase							:   str      ):
		UpperCamelCase	:List[str]		=      n_codes_total
		UpperCamelCase	:str		=      n_codes_given

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

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Union[str, Any]	      =    """bark"""
	snake_case__				:	Union[str, Any]	      =    True





	def __init__(						self							:   int					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   str=0.02					,    **__lowerCamelCase							:   List[str]					,    ):
		if semantic_config is None:
			UpperCamelCase	:Union[str, Any]		=      {}
			logger.info("""semantic_config is None. initializing the semantic model with default values."""      )

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

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

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

		UpperCamelCase	:Tuple		=      BarkSemanticConfig(**__lowerCamelCase      )
		UpperCamelCase	:str		=      BarkCoarseConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      BarkFineConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      AutoConfig.for_model(**__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   BarkSemanticConfig					,    __lowerCamelCase							:   BarkCoarseConfig					,    __lowerCamelCase							:   BarkFineConfig					,    __lowerCamelCase							:   AutoConfig					,    **__lowerCamelCase							:   int					,    ):
		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							:   Dict      ):
		UpperCamelCase	:List[Any]		=      copy.deepcopy(self.__dict__      )

		UpperCamelCase	:Any		=      self.semantic_config.to_dict()
		UpperCamelCase	:Optional[int]		=      self.coarse_acoustics_config.to_dict()
		UpperCamelCase	:Tuple		=      self.fine_acoustics_config.to_dict()
		UpperCamelCase	:List[str]		=      self.codec_config.to_dict()

		UpperCamelCase	:List[str]		=      self.__class__.model_type
		return output
