





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, 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',
    'mask_emb': 'masked_spec_embed',
}




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )   ->						List[str]:
       for attribute in key.split("."     ):
              UpperCamelCase_      				=     getattr(UpperCamelCase_    ,						UpperCamelCase_     )

       if weight_type is not None:
              UpperCamelCase_      				=     getattr(UpperCamelCase_    ,						UpperCamelCase_     ).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        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						Tuple:
       UpperCamelCase_      				=     []
       UpperCamelCase_      				=     fairseq_model.state_dict()

       UpperCamelCase_      				=     hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCamelCase_      				=     False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						hf_model.config.feat_extract_norm == "group"    ,						)
                     UpperCamelCase_      				=     True
              else:
                     for key, mapped_key in MAPPING.items():
                            UpperCamelCase_      				=     mapped_key

                            if key in name:
                                   UpperCamelCase_      				=     True
                                   if "*" in mapped_key:
                                          UpperCamelCase_      				=     name.split(UpperCamelCase_     )[0].split("."     )[-2]
                                          UpperCamelCase_      				=     mapped_key.replace("*"    ,						UpperCamelCase_     )
                                   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(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )
                            continue
              if not is_used:
                     unused_weights.append(UpperCamelCase_     )

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




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )   ->						Union[str, Any]:
       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(UpperCamelCase_     )




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Tuple:
       UpperCamelCase_      				=     HubertConfig()
       UpperCamelCase_      				=     model.config

       UpperCamelCase_      				=     fs_config.activation_dropout
       UpperCamelCase_      				=     False
       UpperCamelCase_      				=     fs_config.attention_dropout
       UpperCamelCase_      				=     False
       UpperCamelCase_      				=     eval(fs_config.extractor_conv_feature_layers     )
       UpperCamelCase_      				=     [x[0] for x in conv_layers]
       UpperCamelCase_      				=     [x[1] for x in conv_layers]
       UpperCamelCase_      				=     [x[2] for x in conv_layers]
       UpperCamelCase_      				=     "gelu"
       UpperCamelCase_      				=     "layer" if fs_config.extractor_mode == "layer_norm" else "group"
       UpperCamelCase_      				=     False
       UpperCamelCase_      				=     0.0
       UpperCamelCase_      				=     0.0
       UpperCamelCase_      				=     fs_config.activation_fn
       UpperCamelCase_      				=     fs_config.dropout
       UpperCamelCase_      				=     fs_config.encoder_embed_dim
       UpperCamelCase_      				=     0.02
       UpperCamelCase_      				=     fs_config.encoder_ffn_embed_dim
       UpperCamelCase_      				=     1e-5
       UpperCamelCase_      				=     0.0
       UpperCamelCase_      				=     fs_config.encoder_attention_heads
       UpperCamelCase_      				=     fs_config.conv_pos_groups
       UpperCamelCase_      				=     fs_config.conv_pos
       UpperCamelCase_      				=     len(UpperCamelCase_     )
       UpperCamelCase_      				=     fs_config.encoder_layers

       return config




@torch.no_grad()
def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_=None     )   ->						str:
       UpperCamelCase_      				=     distilhubert().model.model

       if config_path is not None:
              UpperCamelCase_      				=     HubertConfig.from_pretrained(UpperCamelCase_     )
       else:
              UpperCamelCase_      				=     convert_config(UpperCamelCase_     )
       UpperCamelCase_      				=     model.eval()

       UpperCamelCase_      				=     WavaVecaFeatureExtractor(
           feature_size=1    ,						sampling_rate=16000    ,						padding_value=0    ,						do_normalize=UpperCamelCase_    ,						return_attention_mask=UpperCamelCase_    ,						)
       UpperCamelCase_      				=     HubertModel(UpperCamelCase_     )

       recursively_load_weights(UpperCamelCase_    ,						UpperCamelCase_     )

       feature_extractor.save_pretrained(UpperCamelCase_     )
       hf_model.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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     _UpperCAmelCase				       = parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








from typing import TYPE_CHECKING

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


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = ['MPNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase				       = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

