






'''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()
UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for attribute in key.split("."							):
      lowercase_	:    Dict           =     getattr(_UpperCamelCase						,		_UpperCamelCase							)

   if weight_type is not None:
      lowercase_	:    Union[str, Any]           =     getattr(_UpperCamelCase						,		_UpperCamelCase							).shape
   else:
      lowercase_	:    List[str]           =     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_	:    Optional[int]           =     value
   elif weight_type == "weight_g":
      lowercase_	:    Dict           =     value
   elif weight_type == "weight_v":
      lowercase_	:    Dict           =     value
   elif weight_type == "bias":
      lowercase_	:    List[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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     []
   lowercase_	:    Tuple           =     fairseq_model.state_dict()

   lowercase_	:    str           =     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase_	:    Dict           =     False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		hf_model.config.feat_extract_norm == "group"						,		)
         lowercase_	:    Any           =     True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase_	:    Dict           =     "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_	:    int           =     name.split(_UpperCamelCase							)[0].split("."							)[-2]
                  lowercase_	:    int           =     mapped_key.replace("*"						,		_UpperCamelCase							)
               if "weight_g" in name:
                  lowercase_	:    Union[str, Any]           =     "weight_g"
               elif "weight_v" in name:
                  lowercase_	:    str           =     "weight_v"
               elif "weight" in name:
                  lowercase_	:    str           =     "weight"
               elif "bias" in name:
                  lowercase_	:    Union[str, Any]           =     "bias"
               else:
                  lowercase_	:    int           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     full_name.split("conv_layers."							)[-1]
   lowercase_	:    Tuple           =     name.split("."							)
   lowercase_	:    List[Any]           =     int(items[0]							)
   lowercase_	:    List[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_	:    Union[str, 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_	:    List[str]           =     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_	:    Union[str, Any]           =     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_	:    Tuple           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=True							):


   """simple docstring"""




   if config_path is not None:
      lowercase_	:    Tuple           =     HubertConfig.from_pretrained(_UpperCamelCase							)
   else:
      lowercase_	:    Any           =     HubertConfig()

   if is_finetuned:
      if dict_path:
         lowercase_	:    List[str]           =     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_	:    Tuple           =     target_dict.bos_index
         lowercase_	:    Union[str, Any]           =     target_dict.eos_index
         lowercase_	:    List[Any]           =     len(target_dict.symbols							)
         lowercase_	:    Optional[int]           =     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_	:    Dict           =     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_	:    str           =     True if config.feat_extract_norm == "layer" else False
         lowercase_	:    str           =     WavaVecaFeatureExtractor(
             feature_size=1						,		sampling_rate=1_6000						,		padding_value=0						,		do_normalize=_UpperCamelCase						,		return_attention_mask=_UpperCamelCase						,		)
         lowercase_	:    List[Any]           =     WavaVecaProcessor(feature_extractor=_UpperCamelCase						,		tokenizer=_UpperCamelCase							)
         processor.save_pretrained(_UpperCamelCase							)

      lowercase_	:    Union[str, Any]           =     HubertForCTC(_UpperCamelCase							)
   else:
      lowercase_	:    str           =     HubertModel(_UpperCamelCase							)

   if is_finetuned:
      lowercase_    ,		lowercase_    ,		lowercase_	:    int           =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,		arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
   else:
      lowercase_    ,		lowercase_    ,		lowercase_	:    str           =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

   lowercase_	:    Union[str, Any]           =     model[0].eval()

   recursively_load_weights(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)

   hf_wavavec.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
 UpperCamelCase__ =			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'
 )
 UpperCamelCase__ =			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'''
import heapq as hq
import math
from collections.abc import Iterator


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




