






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					=	logging.get_logger(__name__)

__A					=	{
    "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[int]  ,      A_:						Optional[int]  ,      A_:						Union[str, Any]  ,      A_:						Optional[int]  ,      A_:						Dict		)	->				List[Any]:







       """simple docstring"""






       for attribute in key.split("""."""		):
              __UpperCAmelCase     				=getattr(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE		)

       if weight_type is not None:
              __UpperCAmelCase     				=getattr(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE		).shape
       else:
              __UpperCAmelCase     				=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":
              __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       lowercase__	(					A_:						Dict  ,      A_:						List[str]  ,      A_:						int		)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=[]
       __UpperCAmelCase     				=fairseq_model.state_dict()

       __UpperCAmelCase     				=hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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







def       lowercase__	(					A_:						Tuple  ,      A_:						Tuple  ,      A_:						Any  ,      A_:						List[Any]  ,      A_:						List[str]		)	->				List[Any]:







       """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:
                     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.'''
                     )
                     __UpperCAmelCase     				=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.'''
                     )
                     __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:
                     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."
                     )
                     __UpperCAmelCase     				=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.'''
                     )
                     __UpperCAmelCase     				=value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
       else:
              unused_weights.append(_SCREAMING_SNAKE_CASE		)







@torch.no_grad()
def       lowercase__	(					A_:						int  ,      A_:						Union[str, Any]  ,      A_:						str=None  ,      A_:						int=None  ,      A_:						Union[str, Any]=True		)	->				Optional[int]:







       """simple docstring"""






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

       if is_finetuned:
              if dict_path:
                     __UpperCAmelCase     				=Dictionary.load(_SCREAMING_SNAKE_CASE		)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     __UpperCAmelCase     				=target_dict.pad_index
                     __UpperCAmelCase     				=target_dict.bos_index
                     __UpperCAmelCase     				=target_dict.eos_index
                     __UpperCAmelCase     				=len(target_dict.symbols		)
                     __UpperCAmelCase     				=os.path.join(_SCREAMING_SNAKE_CASE  ,      """vocab.json"""		)
                     if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
                            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE		)		)
                            return
                     os.makedirs(_SCREAMING_SNAKE_CASE  ,      exist_ok=_SCREAMING_SNAKE_CASE		)
                     with open(_SCREAMING_SNAKE_CASE  ,      """w"""  ,      encoding="""utf-8"""		) as vocab_handle:
                            json.dump(target_dict.indices  ,      _SCREAMING_SNAKE_CASE		)
                     __UpperCAmelCase     				=WavaVecaCTCTokenizer(
                         _SCREAMING_SNAKE_CASE  ,      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=_SCREAMING_SNAKE_CASE  ,      )
                     __UpperCAmelCase     				=True if config.feat_extract_norm == 'layer' else False
                     __UpperCAmelCase     				=WavaVecaFeatureExtractor(
                         feature_size=1  ,      sampling_rate=16000  ,      padding_value=0  ,      do_normalize=_SCREAMING_SNAKE_CASE  ,      return_attention_mask=_SCREAMING_SNAKE_CASE  ,      )
                     __UpperCAmelCase     				=WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE  ,      tokenizer=_SCREAMING_SNAKE_CASE		)
                     processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

              __UpperCAmelCase     				=HubertForCTC(_SCREAMING_SNAKE_CASE		)
       else:
              __UpperCAmelCase     				=HubertModel(_SCREAMING_SNAKE_CASE		)

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

       __UpperCAmelCase     				=model[0].eval()

       recursively_load_weights(_SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE  ,      _SCREAMING_SNAKE_CASE		)

       hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
   __A					=	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					=	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 tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__A											=						"""sshleifer/bart-tiny-random"""
__A											=						"""patrickvonplaten/t5-tiny-random"""






@require_torch
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return AutoConfig.from_pretrained(__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[str]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.num_hidden_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :List[Any]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=__UpperCAmelCase )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					self.teacher_config.encoder_layers )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							*lowerCAmelCase__     :Optional[int]											=				create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=1       ,					d=1 )
         self.assertEqual(student.config.encoder_layers       ,					1 )
         self.assertEqual(student.config.decoder_layers       ,					1 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         with self.assertRaises(__UpperCAmelCase ):
           create_student_by_copying_alternating_layers(__UpperCAmelCase       ,					tempfile.mkdtemp()       ,					e=__UpperCAmelCase       ,					d=__UpperCAmelCase )
