'''simple docstring'''




import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__snake_case  : int		       =    logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__snake_case  : Tuple		       =    direct_transformers_import(Path(__file__).parent)


__snake_case  : List[Any]		       =    {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   Union[str, Any]  					=							["feature_extractor", "tokenizer"]
       # Names need to be attr_class for attr in attributes
       __lowercase   :   Any  					=							None
       __lowercase   :   int  					=							None
       __lowercase   :   List[str]  					=							None

       def __init__( self       ,	*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:

           for key in kwargs:
               if key not in self.attributes:
                   raise TypeError(F'''Unexpected keyword argument {key}.'''					)
           for arg, attribute_name in zip(_SCREAMING_SNAKE_CASE       ,	self.attributes					):
               if attribute_name in kwargs:
                   raise TypeError(F'''Got multiple values for argument {attribute_name}.'''					)
               else:
                   A_      =  arg

           if len(_SCREAMING_SNAKE_CASE					) != len(self.attributes					):
               raise ValueError(
                   F'''This processor requires {len(self.attributes					)} arguments: {', '.join(self.attributes					)}. Got '''
                   F'''{len(_SCREAMING_SNAKE_CASE					)} arguments instead.'''					)

           # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
           for attribute_name, arg in kwargs.items():
               A_      =  getattr(self       ,	F'''{attribute_name}_class'''					)
               # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
               A_      =  AUTO_TO_BASE_CLASS_MAPPING.get(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               if isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                   A_      =  tuple(getattr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					) for n in class_name if n is not None					)
               else:
                   A_      =  getattr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               if not isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                   raise ValueError(
                       F'''Received a {type(_SCREAMING_SNAKE_CASE					).__name__} for argument {attribute_name}, but a {class_name} was expected.'''					)

               setattr(self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

       def __repr__( self					)						->	List[Any]:
           A_      =  [F'''- {name}: {repr(getattr(self       ,	_SCREAMING_SNAKE_CASE					)					)}''' for name in self.attributes]
           A_      =  '''\n'''.join(_SCREAMING_SNAKE_CASE					)
           return F'''{self.__class__.__name__}:\n{attributes_repr}'''

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	**_SCREAMING_SNAKE_CASE					)						->	Tuple:
           os.makedirs(_SCREAMING_SNAKE_CASE       ,	exist_ok=_SCREAMING_SNAKE_CASE					)

           if push_to_hub:
               A_      =  kwargs.pop('''commit_message'''       ,	_SCREAMING_SNAKE_CASE					)
               A_      =  kwargs.pop('''repo_id'''       ,	save_directory.split(os.path.sep					)[-1]					)
               A_      =  self._create_repo(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_      =  self._get_files_timestamps(_SCREAMING_SNAKE_CASE					)
           # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
           # loaded from the Hub.
           if self._auto_class is not None:
               A_      =  [getattr(self       ,	_SCREAMING_SNAKE_CASE					) for attribute_name in self.attributes]
               A_      =  [(a.init_kwargs if isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					) else a) for a in attrs]
               custom_object_save(self       ,	_SCREAMING_SNAKE_CASE       ,	config=_SCREAMING_SNAKE_CASE					)

           for attribute_name in self.attributes:
               A_      =  getattr(self       ,	_SCREAMING_SNAKE_CASE					)
               # Include the processor class in the attribute config so this processor can then be reloaded with the
               # `AutoProcessor` API.
               if hasattr(_SCREAMING_SNAKE_CASE       ,	'''_set_processor_class'''					):
                   attribute._set_processor_class(self.__class__.__name__					)
               attribute.save_pretrained(_SCREAMING_SNAKE_CASE					)

           if self._auto_class is not None:
               # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
               for attribute_name in self.attributes:
                   A_      =  getattr(self       ,	_SCREAMING_SNAKE_CASE					)
                   if isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                       del attribute.init_kwargs["auto_map"]

           if push_to_hub:
               self._upload_modified_files(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	commit_message=_SCREAMING_SNAKE_CASE       ,	token=kwargs.get('''use_auth_token'''					)       ,	)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = "main"       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	Union[str, Any]:
           A_      =  cache_dir
           A_      =  force_download
           A_      =  local_files_only
           A_      =  revision

           A_      =  kwargs.pop('''use_auth_token'''       ,	_SCREAMING_SNAKE_CASE					)
           if use_auth_token is not None:
               warnings.warn(
                   '''The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'''       ,	_SCREAMING_SNAKE_CASE					)
               if token is not None:
                   raise ValueError(
                       '''`token` and `use_auth_token` are both specified. Please set only the argument `token`.'''					)
               A_      =  use_auth_token

           if token is not None:
               # change to `token` in a follow-up PR
               A_      =  token

           A_      =  cls._get_arguments_from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           return cls(*_SCREAMING_SNAKE_CASE					)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE="AutoProcessor"					)						->	List[Any]:

           if not isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
               A_      =  auto_class.__name__

           import transformers.models.auto as auto_module

           if not hasattr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
               raise ValueError(F'''{auto_class} is not a valid auto class.'''					)

           A_      =  auto_class

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           A_      =  []
           for attribute_name in cls.attributes:
               A_      =  getattr(cls       ,	F'''{attribute_name}_class'''					)
               if isinstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					):
                   A_      =  tuple(getattr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					) if n is not None else None for n in class_name					)
                   A_      =  kwargs.get('''use_fast'''       ,	_SCREAMING_SNAKE_CASE					)
                   if use_fast and classes[1] is not None:
                       A_      =  classes[1]
                   else:
                       A_      =  classes[0]
               else:
                   A_      =  getattr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               args.append(attribute_class.from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)					)
           return args

       @property
       def    __A			( self					)						->	Union[str, Any]:
           A_      =  getattr(self       ,	self.attributes[0]					)
           return getattr(_SCREAMING_SNAKE_CASE       ,	'''model_input_names'''       ,	_SCREAMING_SNAKE_CASE					)
__snake_case  : Optional[int]		       =    copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
  __snake_case  : str		       =    ProcessorMixin.push_to_hub.__doc__.format(
      object='processor', object_class='AutoProcessor', object_files='processor files'
  )







"""simple docstring"""







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Any      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					WavLMForSequenceClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['projector.weight']
   __lowerCamelCase    		=					downstream_dict['projector.bias']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.bias']
   return model



def       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __lowerCamelCase    		=					WavLMForXVector.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['connector.weight']
   __lowerCamelCase    		=					downstream_dict['connector.bias']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      __lowerCamelCase    		=					downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      __lowerCamelCase    		=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
   __lowerCamelCase    		=					downstream_dict['objective.W']
   return model



@torch.no_grad()
def       A__    (	lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]      , lowercase_     :	Union[str, Any]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					torch.load(lowercase_      , map_location='cpu'						)

   __lowerCamelCase    		=					checkpoint['Downstream']

   __lowerCamelCase    		=					WavLMConfig.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
       lowercase_      , return_attention_mask=lowercase_      , do_normalize=lowercase_						)

   __lowerCamelCase    		=					hf_config.architectures[0]
   if arch.endswith('ForSequenceClassification'						):
      __lowerCamelCase    		=					convert_classification(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForAudioFrameClassification'						):
      __lowerCamelCase    		=					convert_diarization(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForXVector'						):
      __lowerCamelCase    		=					convert_xvector(lowercase_      , lowercase_      , lowercase_						)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''						)

   if hf_config.use_weighted_layer_sum:
      __lowerCamelCase    		=					checkpoint['Featurizer']['weights']

   hf_feature_extractor.save_pretrained(lowercase_						)
   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






