


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		:				Dict									=       logging.get_logger(__name__)

_A		:				List[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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str						,      snake_case	:    Union[str, Any]						,      snake_case	:    str )       ->							Tuple:
   for attribute in key.split(""".""" ):
      __lowerCamelCase:				Any     =	getattr(snake_case						,      snake_case )

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

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



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    Any						,      snake_case	:    List[Any] )       ->							Tuple:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Dict     =	fairseq_model.state_dict()

   __lowerCamelCase:				List[str]     =	hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



def     __lowerCAmelCase						(							snake_case	:    Tuple						,      snake_case	:    Union[str, Any]						,      snake_case	:    Optional[Any]						,      snake_case	:    str						,      snake_case	:    List[Any] )       ->							Union[str, Any]:
   __lowerCamelCase:				List[str]     =	full_name.split("""conv_layers.""" )[-1]
   __lowerCamelCase:				List[Any]     =	name.split(""".""" )
   __lowerCamelCase:				int     =	int(items[0] )
   __lowerCamelCase:				str     =	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.'
         )
         __lowerCamelCase:				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.'
         )
         __lowerCamelCase:				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."
         )
         __lowerCamelCase:				List[str]     =	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.'
         )
         __lowerCamelCase:				Dict     =	value
         logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
   else:
      unused_weights.append(snake_case )



@torch.no_grad()
def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Optional[Any]						,      snake_case	:    Union[str, Any]=None						,      snake_case	:    str=None						,      snake_case	:    List[Any]=True )       ->							List[str]:
   if config_path is not None:
      __lowerCamelCase:				Tuple     =	HubertConfig.from_pretrained(snake_case )
   else:
      __lowerCamelCase:				List[Any]     =	HubertConfig()

   if is_finetuned:
      if dict_path:
         __lowerCamelCase:				Dict     =	Dictionary.load(snake_case )

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

      __lowerCamelCase:				Optional[Any]     =	HubertForCTC(snake_case )
   else:
      __lowerCamelCase:				str     =	HubertModel(snake_case )

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

   __lowerCamelCase:				List[Any]     =	model[0].eval()

   recursively_load_weights(snake_case						,      snake_case						,      snake_case )

   hf_wavavec.save_pretrained(snake_case )


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		:				Dict									=       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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


