

'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case      =    logging.get_logger(__name__)

_snake_case      =    {
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def    _A						(      snake_case						,				snake_case						,				snake_case						,				snake_case						,				snake_case	)     ->      Dict:







   for attribute in key.split("."	):
      _lowercase     : Optional[int]     	=  getattr(__UpperCAmelCase						,				__UpperCAmelCase	)

   if weight_type is not None:
      _lowercase     : Any     	=  getattr(__UpperCAmelCase						,				__UpperCAmelCase	).shape
   else:
      _lowercase     : Optional[Any]     	=  hf_pointer.shape

   assert hf_shape == value.shape, (
       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":
      _lowercase     : Union[str, Any]     	=  value
   elif weight_type == "weight_g":
      _lowercase     : Tuple     	=  value
   elif weight_type == "weight_v":
      _lowercase     : Union[str, Any]     	=  value
   elif weight_type == "bias":
      _lowercase     : Optional[Any]     	=  value
   else:
      _lowercase     : List[Any]     	=  value

   logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''	)







def    _A						(      snake_case						,				snake_case						,				snake_case	)     ->      Union[str, Any]:
   _lowercase     : List[Any]     	=  []
   _lowercase     : Any     	=  fairseq_model.state_dict()

   _lowercase     : Optional[Any]     	=  hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowercase     : Optional[Any]     	=  False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase						,				hf_model.config.feat_extract_norm == "group"						,				)
         _lowercase     : Union[str, Any]     	=  True
      else:
         for key, mapped_key in MAPPING.items():
            _lowercase     : int     	=  "hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

            if key in name or (key.split("w2v_model."	)[-1] == name.split("."	)[0] and not is_finetuned):
               _lowercase     : Any     	=  True
               if "*" in mapped_key:
                  _lowercase     : Union[str, Any]     	=  name.split(__UpperCAmelCase	)[0].split("."	)[-2]
                  _lowercase     : Optional[Any]     	=  mapped_key.replace("*"						,				__UpperCAmelCase	)
               if "weight_g" in name:
                  _lowercase     : Optional[Any]     	=  "weight_g"
               elif "weight_v" in name:
                  _lowercase     : Optional[int]     	=  "weight_v"
               elif "weight" in name:
                  _lowercase     : Any     	=  "weight"
               elif "bias" in name:
                  _lowercase     : List[str]     	=  "bias"
               else:
                  _lowercase     : List[str]     	=  None
               set_recursively(__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase	)
            continue
      if not is_used:
         unused_weights.append(__UpperCAmelCase	)

   logger.warning(F'''Unused weights: {unused_weights}'''	)







def    _A						(      snake_case						,				snake_case						,				snake_case						,				snake_case						,				snake_case	)     ->      Union[str, Any]:
   _lowercase     : List[str]     	=  full_name.split("conv_layers."	)[-1]
   _lowercase     : Optional[int]     	=  name.split("."	)
   _lowercase     : List[str]     	=  int(items[0]	)
   _lowercase     : Optional[Any]     	=  int(items[1]	)

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         _lowercase     : Optional[Any]     	=  value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         _lowercase     : Union[str, Any]     	=  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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         _lowercase     : Dict     	=  value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         _lowercase     : List[Any]     	=  value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
   else:
      unused_weights.append(__UpperCAmelCase	)







@torch.no_grad()
def    _A						(      snake_case						,				snake_case						,				snake_case=None						,				snake_case=None						,				snake_case=True	)     ->      Tuple:







   if config_path is not None:
      _lowercase     : str     	=  HubertConfig.from_pretrained(__UpperCAmelCase	)
   else:
      _lowercase     : str     	=  HubertConfig()

   if is_finetuned:
      if dict_path:
         _lowercase     : Any     	=  Dictionary.load(__UpperCAmelCase	)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         _lowercase     : Tuple     	=  target_dict.pad_index
         _lowercase     : Optional[Any]     	=  target_dict.bos_index
         _lowercase     : Tuple     	=  target_dict.eos_index
         _lowercase     : int     	=  len(target_dict.symbols	)
         _lowercase     : str     	=  os.path.join(__UpperCAmelCase						,				"vocab.json"	)
         if not os.path.isdir(__UpperCAmelCase	):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__UpperCAmelCase	)	)
            return
         os.makedirs(__UpperCAmelCase						,				exist_ok=__UpperCAmelCase	)
         with open(__UpperCAmelCase						,				"w"						,				encoding="utf-8"	) as vocab_handle:
            json.dump(target_dict.indices						,				__UpperCAmelCase	)
         _lowercase     : Tuple     	=  WavaVecaCTCTokenizer(
             __UpperCAmelCase						,				unk_token=target_dict.unk_word						,				pad_token=target_dict.pad_word						,				bos_token=target_dict.bos_word						,				eos_token=target_dict.eos_word						,				word_delimiter_token="|"						,				do_lower_case=__UpperCAmelCase						,				)
         _lowercase     : int     	=  True if config.feat_extract_norm == "layer" else False
         _lowercase     : Optional[int]     	=  WavaVecaFeatureExtractor(
             feature_size=1						,				sampling_rate=1_60_00						,				padding_value=0						,				do_normalize=__UpperCAmelCase						,				return_attention_mask=__UpperCAmelCase						,				)
         _lowercase     : Union[str, Any]     	=  WavaVecaProcessor(feature_extractor=__UpperCAmelCase						,				tokenizer=__UpperCAmelCase	)
         processor.save_pretrained(__UpperCAmelCase	)

      _lowercase     : Optional[Any]     	=  HubertForCTC(__UpperCAmelCase	)
   else:
      _lowercase     : List[str]     	=  HubertModel(__UpperCAmelCase	)

   if is_finetuned:
      _lowercase	, _lowercase	, _lowercase     : List[Any]     	=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,				arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
   else:
      _lowercase	, _lowercase	, _lowercase     : Tuple     	=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

   _lowercase     : Any     	=  model[0].eval()

   recursively_load_weights(__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase	)

   hf_wavavec.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
    _snake_case      =    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'
    )
    _snake_case      =    parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )













'''simple docstring'''

from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						''
   __UpperCamelCase :			int						   =						'hf-legacy'  # "hf://"" is reserved for hffs





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[DatasetInfo] = None					,lowerCamelCase					:      Optional[str] = None					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(self					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							repo_info
      __SCREAMING_SNAKE_CASE   				=							token
      __SCREAMING_SNAKE_CASE   				=							None





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      if self.dir_cache is None:
         __SCREAMING_SNAKE_CASE   				=							{}
         for hf_file in self.repo_info.siblings:
            # TODO(QL): add sizes
            __SCREAMING_SNAKE_CASE   				=							{
                """name""": hf_file.rfilename,
                """size""": None,
                """type""": """file""",
            }
            self.dir_cache.update(
                {
                    str(lowerCamelCase     ): {"""name""": str(lowerCamelCase     ), """size""": None, """type""": """directory"""}
                    for d in list(PurePosixPath(hf_file.rfilename     ).parents     )[:-1]
                }     )





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      str					,lowerCamelCase					:      str = "rb"					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      if not isinstance(self.repo_info					,lowerCamelCase     ):
         raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""     )
      __SCREAMING_SNAKE_CASE   				=							hf_hub_url(self.repo_info.id					,lowerCamelCase					,revision=self.repo_info.sha     )
      return fsspec.open(
          lowerCamelCase					,mode=lowerCamelCase					,headers=get_authentication_headers_for_url(lowerCamelCase					,use_auth_token=self.token     )					,client_kwargs={"""trust_env""": True}					,).open()





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Any					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							self._strip_protocol(lowerCamelCase     )
      if path in self.dir_cache:
         return self.dir_cache[path]
      else:
         raise FileNotFoundError(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      Any					,lowerCamelCase					:      str=False					,**lowerCamelCase					:      Any     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							PurePosixPath(path.strip("""/"""     )     )
      __SCREAMING_SNAKE_CASE   				=							{}
      for p, f in self.dir_cache.items():
         __SCREAMING_SNAKE_CASE   				=							PurePosixPath(p.strip("""/"""     )     )
         __SCREAMING_SNAKE_CASE   				=							p.parent
         if root == path:
            __SCREAMING_SNAKE_CASE   				=							f
      __SCREAMING_SNAKE_CASE   				=							list(paths.values()     )
      if detail:
         return out
      else:
         return sorted(f["""name"""] for f in out     )





