



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()
a_   :int        =      logging.get_logger(__name__)

a_   :Optional[int]        =      {
    "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        lowercase_							(A : Optional[Any]		,					A : Tuple		,					A : Union[str, Any]		,					A : Tuple		,					A : Optional[Any]      ):
       for attribute in key.split('.'      ):
              snake_case__							:	Any   	=	getattr(A		,					A      )

       if weight_type is not None:
              snake_case__							:	Union[str, Any]   	=	getattr(A		,					A      ).shape
       else:
              snake_case__							:	Tuple   	=	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":
              snake_case__							:	Tuple   	=	value
       elif weight_type == "weight_g":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "weight_v":
              snake_case__							:	Tuple   	=	value
       elif weight_type == "bias":
              snake_case__							:	str   	=	value
       else:
              snake_case__							:	Optional[Any]   	=	value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : Optional[Any]		,					A : List[str]		,					A : Any      ):
       snake_case__							:	Union[str, Any]   	=	[]
       snake_case__							:	str   	=	fairseq_model.state_dict()

       snake_case__							:	List[Any]   	=	hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__							:	Any   	=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == 'group'		,					)
                     snake_case__							:	Any   	=	True
              else:
                     for key, mapped_key in MAPPING.items():
                            snake_case__							:	Any   	=	'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):
                                   snake_case__							:	List[str]   	=	True
                                   if "*" in mapped_key:
                                          snake_case__							:	Optional[Any]   	=	name.split(A      )[0].split('.'      )[-2]
                                          snake_case__							:	List[str]   	=	mapped_key.replace('*'		,					A      )
                                   if "weight_g" in name:
                                          snake_case__							:	Union[str, Any]   	=	'weight_g'
                                   elif "weight_v" in name:
                                          snake_case__							:	int   	=	'weight_v'
                                   elif "weight" in name:
                                          snake_case__							:	Tuple   	=	'weight'
                                   elif "bias" in name:
                                          snake_case__							:	int   	=	'bias'
                                   else:
                                          snake_case__							:	List[Any]   	=	None
                                   set_recursively(A		,					A		,					A		,					A		,					A      )
                            continue
              if not is_used:
                     unused_weights.append(A      )

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Union[str, Any]		,					A : str		,					A : Any		,					A : int		,					A : List[Any]      ):
       snake_case__							:	Optional[int]   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	int   	=	name.split('.'      )
       snake_case__							:	str   	=	int(items[0]      )
       snake_case__							:	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.'''
                     )
                     snake_case__							:	List[str]   	=	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.'''
                     )
                     snake_case__							:	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."
                     )
                     snake_case__							:	List[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.'''
                     )
                     snake_case__							:	Any   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
@torch.no_grad()
def        lowercase_							(A : List[str]		,					A : Optional[int]		,					A : List[Any]=None		,					A : Union[str, Any]=None		,					A : List[str]=True      ):
       if config_path is not None:
              snake_case__							:	Tuple   	=	HubertConfig.from_pretrained(A      )
       else:
              snake_case__							:	Tuple   	=	HubertConfig()

       if is_finetuned:
              if dict_path:
                     snake_case__							:	int   	=	Dictionary.load(A      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     snake_case__							:	Dict   	=	target_dict.pad_index
                     snake_case__							:	Union[str, Any]   	=	target_dict.bos_index
                     snake_case__							:	Optional[Any]   	=	target_dict.eos_index
                     snake_case__							:	Union[str, Any]   	=	len(target_dict.symbols      )
                     snake_case__							:	List[Any]   	=	os.path.join(A		,					'vocab.json'      )
                     if not os.path.isdir(A      ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A      )      )
                            return
                     os.makedirs(A		,					exist_ok=A      )
                     with open(A		,					'w'		,					encoding='utf-8'      ) as vocab_handle:
                            json.dump(target_dict.indices		,					A      )
                     snake_case__							:	Tuple   	=	WavaVecaCTCTokenizer(
                         A		,					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=A		,					)
                     snake_case__							:	List[Any]   	=	True if config.feat_extract_norm == 'layer' else False
                     snake_case__							:	List[str]   	=	WavaVecaFeatureExtractor(
                         feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
                     snake_case__							:	List[Any]   	=	WavaVecaProcessor(feature_extractor=A		,					tokenizer=A      )
                     processor.save_pretrained(A      )

              snake_case__							:	Optional[Any]   	=	HubertForCTC(A      )
       else:
              snake_case__							:	int   	=	HubertModel(A      )

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

       snake_case__							:	str   	=	model[0].eval()

       recursively_load_weights(A		,					A		,					A      )

       hf_wavavec.save_pretrained(A      )


if __name__ == "__main__":
 a_   :Any        =      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"
 )
 a_   :List[Any]        =      parser.parse_args()
 convert_hubert_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )











from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)






