





'''simple docstring'''




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :str       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
__SCREAMING_SNAKE_CASE     :int       =				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      Any			,     __lowercase							:      int			,     __lowercase							:      Optional[int]			,     __lowercase							:      Union[str, Any]       )		->       int:


     '''simple docstring'''



     for attribute in key.split("."       ):
          _UpperCAmelCase     							=   getattr(__lowercase			,     __lowercase       )

     if weight_type is not None:
          _UpperCAmelCase     							=   getattr(__lowercase			,     __lowercase       ).shape
     else:
          _UpperCAmelCase     							=   hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
              f' {value.shape} for {full_name}'       )

     if weight_type == "weight":
          _UpperCAmelCase     							=   value
     elif weight_type == "weight_g":
          _UpperCAmelCase     							=   value
     elif weight_type == "weight_v":
          _UpperCAmelCase     							=   value
     elif weight_type == "bias":
          _UpperCAmelCase     							=   value
     else:
          _UpperCAmelCase     							=   value

     logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Optional[Any]       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   []
     _UpperCAmelCase     							=   fairseq_model.state_dict()

     _UpperCAmelCase     							=   hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          _UpperCAmelCase     							=   False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     hf_model.config.feat_extract_norm == "group"			,     )
               _UpperCAmelCase     							=   True
          else:
               for key, mapped_key in MAPPING.items():
                    _UpperCAmelCase     							=   "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split("."       )[:-1]       ) != key):
                              # special case since naming is very similar
                              continue
                         _UpperCAmelCase     							=   True
                         if "*" in mapped_key:
                              _UpperCAmelCase     							=   name.split(__lowercase       )[0].split("."       )[-2]
                              _UpperCAmelCase     							=   mapped_key.replace("*"			,     __lowercase       )
                         if "weight_g" in name:
                              _UpperCAmelCase     							=   "weight_g"
                         elif "weight_v" in name:
                              _UpperCAmelCase     							=   "weight_v"
                         elif "bias" in name:
                              _UpperCAmelCase     							=   "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _UpperCAmelCase     							=   "weight"
                         else:
                              _UpperCAmelCase     							=   None
                         set_recursively(__lowercase			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase       )
                    continue
          if not is_used:
               unused_weights.append(__lowercase       )

     logger.warning(f'Unused weights: {unused_weights}'       )
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      Any			,     __lowercase							:      Any			,     __lowercase							:      List[Any]			,     __lowercase							:      Optional[Any]       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   full_name.split("conv_layers."       )[-1]
     _UpperCAmelCase     							=   name.split("."       )
     _UpperCAmelCase     							=   int(items[0]       )
     _UpperCAmelCase     							=   int(items[1]       )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'       )
               _UpperCAmelCase     							=   value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'       )
               _UpperCAmelCase     							=   value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'       )
               _UpperCAmelCase     							=   value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'       )
               _UpperCAmelCase     							=   value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
     else:
          unused_weights.append(__lowercase       )
@torch.no_grad()
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      Optional[int]			,     __lowercase							:      Dict=None			,     __lowercase							:      List[str]=None			,     __lowercase							:      Union[str, Any]=True       )		->       int:


     '''simple docstring'''



     if config_path is not None:
          _UpperCAmelCase     							=   UniSpeechSatConfig.from_pretrained(__lowercase       )
     else:
          _UpperCAmelCase     							=   UniSpeechSatConfig()

     _UpperCAmelCase     							=   ""

     if is_finetuned:
          _UpperCAmelCase     							=   UniSpeechSatForCTC(__lowercase       )
     else:
          _UpperCAmelCase     							=   UniSpeechSatForPreTraining(__lowercase       )

     _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]			,     arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
     _UpperCAmelCase     							=   model[0].eval()

     recursively_load_weights(__lowercase			,     __lowercase       )

     hf_wavavec.save_pretrained(__lowercase       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :int       =				argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
  parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  parser.add_argument(
      '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
  )
  __SCREAMING_SNAKE_CASE     :Tuple       =				parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )











'''simple docstring'''




from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      if not self.is_available():
           raise RuntimeError(
               f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'    )




 @classmethod
 def      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} hyperparameter search backends available. Using {name} as the default.'       )
          return name
     raise RuntimeError(
         "No hyperparameter search backend available.\n"
         + "\n".join(
             f' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )




