

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase							=logging.get_logger(__name__)

_lowerCamelCase							={
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      __UpperCAmelCase     						= 'plbart'
      __UpperCAmelCase     						= ['past_key_values']
      __UpperCAmelCase     						= {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__( self	:				Tuple     ,snake_case	:				Any=50005     ,snake_case	:				Optional[Any]=1024     ,snake_case	:				List[Any]=6     ,snake_case	:				Tuple=3072     ,snake_case	:				List[str]=12     ,snake_case	:				Any=6     ,snake_case	:				Union[str, Any]=3072     ,snake_case	:				List[Any]=12     ,snake_case	:				int=0.0     ,snake_case	:				Union[str, Any]=0.0     ,snake_case	:				List[Any]=True     ,snake_case	:				Optional[Any]=True     ,snake_case	:				int="gelu"     ,snake_case	:				Tuple=768     ,snake_case	:				List[str]=0.1     ,snake_case	:				List[str]=0.1     ,snake_case	:				Any=0.0     ,snake_case	:				Optional[Any]=0.02     ,snake_case	:				List[str]=0.0     ,snake_case	:				int=True     ,snake_case	:				Union[str, Any]=1     ,snake_case	:				int=0     ,snake_case	:				Any=2     ,snake_case	:				Any=2     ,**snake_case	:				Dict     ,):
           SCREAMING_SNAKE_CASE 		=vocab_size
           SCREAMING_SNAKE_CASE 		=max_position_embeddings
           SCREAMING_SNAKE_CASE 		=d_model
           SCREAMING_SNAKE_CASE 		=encoder_ffn_dim
           SCREAMING_SNAKE_CASE 		=encoder_layers
           SCREAMING_SNAKE_CASE 		=encoder_attention_heads
           SCREAMING_SNAKE_CASE 		=decoder_ffn_dim
           SCREAMING_SNAKE_CASE 		=decoder_layers
           SCREAMING_SNAKE_CASE 		=decoder_attention_heads
           SCREAMING_SNAKE_CASE 		=dropout
           SCREAMING_SNAKE_CASE 		=attention_dropout
           SCREAMING_SNAKE_CASE 		=activation_dropout
           SCREAMING_SNAKE_CASE 		=activation_function
           SCREAMING_SNAKE_CASE 		=init_std
           SCREAMING_SNAKE_CASE 		=encoder_layerdrop
           SCREAMING_SNAKE_CASE 		=decoder_layerdrop
           SCREAMING_SNAKE_CASE 		=classifier_dropout
           SCREAMING_SNAKE_CASE 		=use_cache
           SCREAMING_SNAKE_CASE 		=encoder_layers
           SCREAMING_SNAKE_CASE 		=scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=snake_case     ,bos_token_id=snake_case     ,eos_token_id=snake_case     ,is_encoder_decoder=snake_case     ,forced_eos_token_id=snake_case     ,**snake_case     ,)







class 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      @property
      def 			_lowerCAmelCase	( self	:				str							):
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
               ]							)


      @property
      def 			_lowerCAmelCase	( self	:				List[Any]							):
           if self.use_past:
                return OrderedDict(
                    [
                        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                        ('past_keys', {0: 'batch', 2: 'sequence'}),
                        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                    ]							)
           else:
                return OrderedDict(
                    [
                        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                    ]							)




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase							=logging.get_logger(__name__)

_lowerCamelCase							={
    "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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for attribute in key.split('.'     ):
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     )

     if weight_type is not None:
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     ).shape
     else:
          SCREAMING_SNAKE_CASE 		=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":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "weight_g":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "weight_v":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "bias":
          SCREAMING_SNAKE_CASE 		=value
     else:
          SCREAMING_SNAKE_CASE 		=value

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     SCREAMING_SNAKE_CASE 		=fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE 		=hf_model.feature_extractor

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

                    if key in name:
                         SCREAMING_SNAKE_CASE 		=True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE 		=name.split(lowerCAmelCase_     )[0].split('.'     )[-2]
                              SCREAMING_SNAKE_CASE 		=mapped_key.replace('*',       lowerCAmelCase_     )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE 		='weight_g'
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE 		='weight_v'
                         elif "weight" in name:
                              SCREAMING_SNAKE_CASE 		='weight'
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE 		='bias'
                         else:
                              SCREAMING_SNAKE_CASE 		=None
                         set_recursively(lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     )
                    continue
          if not is_used:
               unused_weights.append(lowerCAmelCase_     )

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=full_name.split('conv_layers.'     )[-1]
     SCREAMING_SNAKE_CASE 		=name.split('.'     )
     SCREAMING_SNAKE_CASE 		=int(items[0]     )
     SCREAMING_SNAKE_CASE 		=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.'
               )
               SCREAMING_SNAKE_CASE 		=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.'
               )
               SCREAMING_SNAKE_CASE 		=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."
               )
               SCREAMING_SNAKE_CASE 		=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.'
               )
               SCREAMING_SNAKE_CASE 		=value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
     else:
          unused_weights.append(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=HubertConfig()
     SCREAMING_SNAKE_CASE 		=model.config

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

     return config







@torch.no_grad()
def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=distilhubert().model.model

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

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

     recursively_load_weights(lowerCAmelCase_,       lowerCAmelCase_     )

     feature_extractor.save_pretrained(lowerCAmelCase_     )
     hf_model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
 _lowerCamelCase							=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")
 _lowerCamelCase							=parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

