


'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

A__:    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''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
A__:    Tuple					    =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	List[str]    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Tuple  )  ->			List[str]:
       for attribute in key.split("""."""  ):
              if is_finetuned:
                     if attribute in ["quantizer", "project_q", "project_hid"]:
                            # those layers are only relevant for pretraining and should be dropped
                            return

                     if attribute == "ctc_proj":
                            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                            _a    : Tuple     					="""lm_head"""

              _a    : Optional[Any]     					=getattr(_UpperCAmelCase    ,_UpperCAmelCase  )

       if weight_type is not None:
              _a    : Dict     					=getattr(_UpperCAmelCase    ,_UpperCAmelCase  ).shape
       else:
              _a    : 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":
              _a    : Dict     					=value
       elif weight_type == "weight_g":
              _a    : Union[str, Any]     					=value
       elif weight_type == "weight_v":
              _a    : Tuple     					=value
       elif weight_type == "bias":
              _a    : int     					=value
       else:
              _a    : 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	:	Any    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	List[str]  )  ->			Dict:
       _a    : int     					=[]
       _a    : Tuple     					=fairseq_model.state_dict()

       _a    : str     					=hf_model.unispeech.feature_extractor

       for name, value in fairseq_dict.items():
              _a    : Dict     					=False
              if "conv_layers" in name:
                     load_conv_layer(
                         _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,hf_model.config.feat_extract_norm == """group"""    ,)
                     _a    : List[str]     					=True
              else:
                     for key, mapped_key in MAPPING.items():
                            _a    : List[Any]     					="""unispeech.""" + 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]:
                                   _a    : Dict     					=True
                                   if "*" in mapped_key:
                                          _a    : Optional[int]     					=name.split(_UpperCAmelCase  )[0].split("""."""  )[-2]
                                          _a    : Any     					=mapped_key.replace("""*"""    ,_UpperCAmelCase  )
                                   if "weight_g" in name:
                                          _a    : Tuple     					="""weight_g"""
                                   elif "weight_v" in name:
                                          _a    : int     					="""weight_v"""
                                   elif "bias" in name:
                                          _a    : Any     					="""bias"""
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          _a    : Union[str, Any]     					="""weight"""
                                   else:
                                          _a    : Optional[int]     					=None
                                   set_recursively(_UpperCAmelCase    ,_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	:	Optional[Any]    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	Tuple  )  ->			Optional[Any]:
       _a    : Optional[int]     					=full_name.split("""conv_layers."""  )[-1]
       _a    : int     					=name.split("""."""  )
       _a    : Optional[Any]     					=int(items[0]  )
       _a    : 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."
                     )
                     _a    : Dict     					=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."
                     )
                     _a    : 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."
                     )
                     _a    : 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."
                     )
                     _a    : Optional[int]     					=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	:	Any    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	List[str]=None    ,_UpperCAmelCase	:	Tuple=None    ,_UpperCAmelCase	:	Optional[Any]=True  )  ->			str:
       if config_path is not None:
              _a    : List[Any]     					=UniSpeechConfig.from_pretrained(_UpperCAmelCase  )
       else:
              _a    : str     					=UniSpeechConfig()

       if is_finetuned:
              if dict_path:
                     _a    : Union[str, Any]     					=Dictionary.load_from_json(_UpperCAmelCase  )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _a    : int     					=target_dict.pad_index
                     _a    : int     					=target_dict.bos_index
                     _a    : int     					=target_dict.eos_index
                     _a    : List[str]     					=len(target_dict.symbols  )
                     _a    : List[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  )
                     _a    : str     					=target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     _a    : Optional[Any]     					=42
                     _a    : Dict     					=43
                     with open(_UpperCAmelCase    ,"""w"""    ,encoding="""utf-8"""  ) as vocab_handle:
                            json.dump(_UpperCAmelCase    ,_UpperCAmelCase  )
                     _a    : List[Any]     					=WavaVecaPhonemeCTCTokenizer(
                         _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    ,)
                     _a    : Tuple     					=True if config.feat_extract_norm == """layer""" else False
                     _a    : Dict     					=WavaVecaFeatureExtractor(
                         feature_size=1    ,sampling_rate=16000    ,padding_value=0    ,do_normalize=_UpperCAmelCase    ,return_attention_mask=_UpperCAmelCase    ,)
                     _a    : List[Any]     					=WavaVecaProcessor(feature_extractor=_UpperCAmelCase    ,tokenizer=_UpperCAmelCase  )
                     processor.save_pretrained(_UpperCAmelCase  )

              _a    : List[Any]     					=UniSpeechForCTC(_UpperCAmelCase  )
       else:
              _a    : List[str]     					=UniSpeechForPreTraining(_UpperCAmelCase  )

       if is_finetuned:
              _a					,    _a					,    _a    : List[str]     					=fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]    ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  ), """w2v_path""": checkpoint_path}  )
       else:
              _a					,    _a					,    _a    : int     					=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

       _a    : Optional[int]     					=model[0].eval()

       recursively_load_weights(_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  )

       hf_unispeech.save_pretrained(_UpperCAmelCase  )


if __name__ == "__main__":
   A__:    Dict					    =       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__:    Optional[int]					    =       parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__:    str					    =       {}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple					    =       ['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpt_swa import GPTSwaTokenizer

else:
   import sys

   A__:    str					    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


