'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowerCamelCase :				int														= {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""hubert"""



   def __init__(    self					,      lowerCAmelCase__=32					,      lowerCAmelCase__=768					,      lowerCAmelCase__=12					,      lowerCAmelCase__=12					,      lowerCAmelCase__=3_072					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=True					,      lowerCAmelCase__=0.0					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.02					,      lowerCAmelCase__=1e-5					,      lowerCAmelCase__="group"					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512)					,      lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)					,      lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)					,      lowerCAmelCase__=False					,      lowerCAmelCase__=128					,      lowerCAmelCase__=16					,      lowerCAmelCase__=False					,      lowerCAmelCase__=True					,      lowerCAmelCase__=0.05					,      lowerCAmelCase__=10					,      lowerCAmelCase__=2					,      lowerCAmelCase__=0.0					,      lowerCAmelCase__=10					,      lowerCAmelCase__=0					,      lowerCAmelCase__="sum"					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__=256					,      lowerCAmelCase__=0					,      lowerCAmelCase__=1					,      lowerCAmelCase__=2					,      **lowerCAmelCase__					,      )				->     Dict:
        """simple docstring"""





        super().__init__(**lowerCAmelCase__					,      pad_token_id=lowerCAmelCase__					,      bos_token_id=lowerCAmelCase__					,      eos_token_id=lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =hidden_size
        _UpperCamelCase  :Union[str, Any]       =feat_extract_norm
        _UpperCamelCase  :Union[str, Any]       =feat_extract_activation
        _UpperCamelCase  :int       =list(lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =list(lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =list(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[int]       =conv_bias
        _UpperCamelCase  :Union[str, Any]       =num_conv_pos_embeddings
        _UpperCamelCase  :int       =num_conv_pos_embedding_groups
        _UpperCamelCase  :Dict       =len(self.conv_dim     )
        _UpperCamelCase  :Dict       =num_hidden_layers
        _UpperCamelCase  :Tuple       =intermediate_size
        _UpperCamelCase  :Optional[Any]       =hidden_act
        _UpperCamelCase  :Dict       =num_attention_heads
        _UpperCamelCase  :Optional[int]       =hidden_dropout
        _UpperCamelCase  :int       =attention_dropout
        _UpperCamelCase  :Optional[Any]       =activation_dropout
        _UpperCamelCase  :List[Any]       =feat_proj_layer_norm
        _UpperCamelCase  :Union[str, Any]       =feat_proj_dropout
        _UpperCamelCase  :Optional[int]       =final_dropout
        _UpperCamelCase  :Optional[int]       =layerdrop
        _UpperCamelCase  :Union[str, Any]       =layer_norm_eps
        _UpperCamelCase  :str       =initializer_range
        _UpperCamelCase  :List[str]       =vocab_size
        _UpperCamelCase  :int       =do_stable_layer_norm
        _UpperCamelCase  :int       =use_weighted_layer_sum
        _UpperCamelCase  :Optional[int]       =classifier_proj_size

        if (
            (len(self.conv_stride     ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
            or (len(self.conv_dim     ) != self.num_feat_extract_layers)
        ):
             raise ValueError(
                 """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                 """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                 f''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
                 f''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _UpperCamelCase  :Union[str, Any]       =apply_spec_augment
        _UpperCamelCase  :Tuple       =mask_time_prob
        _UpperCamelCase  :Union[str, Any]       =mask_time_length
        _UpperCamelCase  :Optional[int]       =mask_time_min_masks
        _UpperCamelCase  :Any       =mask_feature_prob
        _UpperCamelCase  :Any       =mask_feature_length
        _UpperCamelCase  :List[Any]       =mask_feature_min_masks

        # ctc loss
        _UpperCamelCase  :Optional[int]       =ctc_loss_reduction
        _UpperCamelCase  :List[str]       =ctc_zero_infinity




   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        return functools.reduce(operator.mul					,      self.conv_stride					,      1     )
'''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


_lowerCamelCase :				List[Any]														= """1.5.0"""

_lowerCamelCase :				List[Any]														= ["""vision-encoder-decoder"""]



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







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

          _UpperCamelCase  :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]"""      )
     _UpperCamelCase  :Tuple       =[
         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}''',
     ]
     _UpperCamelCase  :Union[str, Any]       =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  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =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
     _UpperCamelCase  :Dict       =FeaturesManager.get_model_from_feature(
         args.feature			,      args.model			,      framework=args.framework			,      cache_dir=args.cache_dir      )

     _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =FeaturesManager.check_supported_model_or_raise(__a			,      feature=args.feature      )
     _UpperCamelCase  :int       =model_onnx_config(model.config      )

     if model_kind in ENCODER_DECODER_MODELS:
          _UpperCamelCase  :Tuple       =model.get_encoder()
          _UpperCamelCase  :Tuple       =model.get_decoder()

          _UpperCamelCase  :Dict       =onnx_config.get_encoder_config(encoder_model.config      )
          _UpperCamelCase  :str       =onnx_config.get_decoder_config(
              encoder_model.config			,      decoder_model.config			,      feature=args.feature      )

          if args.opset is None:
               _UpperCamelCase  :int       =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.'''      )

          _UpperCamelCase  :Optional[Any]       =AutoFeatureExtractor.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =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			,      )

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

          _UpperCamelCase       ,      _UpperCamelCase  :str       =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":
               _UpperCamelCase  :Union[str, Any]       =get_preprocessor(args.model      )
          elif args.preprocessor == "tokenizer":
               _UpperCamelCase  :Dict       =AutoTokenizer.from_pretrained(args.model      )
          elif args.preprocessor == "image_processor":
               _UpperCamelCase  :Union[str, Any]       =AutoImageProcessor.from_pretrained(args.model      )
          elif args.preprocessor == "feature_extractor":
               _UpperCamelCase  :int       =AutoFeatureExtractor.from_pretrained(args.model      )
          elif args.preprocessor == "processor":
               _UpperCamelCase  :Dict       =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:
               _UpperCamelCase  :Optional[int]       =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.'''      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output			,      )

          if args.atol is None:
               _UpperCamelCase  :List[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  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, 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."""
         )			,      )

     _UpperCamelCase  :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__":
      _lowerCamelCase :				Optional[int]														= logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
      logger.setLevel(logging.INFO)
      main()