



"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''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 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

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

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






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		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					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		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 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

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

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

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	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''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
  raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[Any]          =	'''Hello world! cécé herlolip'''






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   ,	snake_case_  :  bool   )							->	Optional[int]:






   """simple docstring"""


   _lowerCAmelCase					     =		FairseqRobertaModel.from_pretrained(snake_case_   )
   roberta.eval()  # disable dropout
   _lowerCAmelCase					     =		roberta.model.encoder.sentence_encoder
   _lowerCAmelCase					     =		RobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,	hidden_size=roberta.args.encoder_embed_dim   ,	num_hidden_layers=roberta.args.encoder_layers   ,	num_attention_heads=roberta.args.encoder_attention_heads   ,	intermediate_size=roberta.args.encoder_ffn_embed_dim   ,	max_position_embeddings=514   ,	type_vocab_size=1   ,	layer_norm_eps=1e-5   ,	)
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""   ,	snake_case_   )

   _lowerCAmelCase					     =		RobertaForSequenceClassification(snake_case_   ) if classification_head else RobertaForMaskedLM(snake_case_   )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   _lowerCAmelCase					     =		roberta_sent_encoder.embed_tokens.weight
   _lowerCAmelCase					     =		roberta_sent_encoder.embed_positions.weight
   _lowerCAmelCase					     =		torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.
   _lowerCAmelCase					     =		roberta_sent_encoder.emb_layer_norm.weight
   _lowerCAmelCase					     =		roberta_sent_encoder.emb_layer_norm.bias

   for i in range(config.num_hidden_layers   ):
      # Encoder: start of layer
      _lowerCAmelCase					     =		model.roberta.encoder.layer[i]
      _lowerCAmelCase					     =		roberta_sent_encoder.layers[i]

      # self attention
      _lowerCAmelCase					     =		layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)   )
      )

      _lowerCAmelCase					     =		roberta_layer.self_attn.q_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.q_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn.k_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.k_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn.v_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.v_proj.bias

      # self-attention output
      _lowerCAmelCase					     =		layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      _lowerCAmelCase					     =		roberta_layer.self_attn.out_proj.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn.out_proj.bias
      _lowerCAmelCase					     =		roberta_layer.self_attn_layer_norm.weight
      _lowerCAmelCase					     =		roberta_layer.self_attn_layer_norm.bias

      # intermediate
      _lowerCAmelCase					     =		layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      _lowerCAmelCase					     =		roberta_layer.fca.weight
      _lowerCAmelCase					     =		roberta_layer.fca.bias

      # output
      _lowerCAmelCase					     =		layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      _lowerCAmelCase					     =		roberta_layer.fca.weight
      _lowerCAmelCase					     =		roberta_layer.fca.bias
      _lowerCAmelCase					     =		roberta_layer.final_layer_norm.weight
      _lowerCAmelCase					     =		roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].dense.weight
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].dense.bias
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.weight
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""].out_proj.bias
   else:
      # LM Head
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.dense.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.dense.bias
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.layer_norm.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.layer_norm.bias
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.weight
      _lowerCAmelCase					     =		roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   _lowerCAmelCase					     =		roberta.encode(snake_case_   ).unsqueeze(0   )  # batch of size 1

   _lowerCAmelCase					     =		model(snake_case_   )[0]
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""](roberta.extract_features(snake_case_   )   )
   else:
      _lowerCAmelCase					     =		roberta.model(snake_case_   )[0]
   print(our_output.shape   ,	their_output.shape   )
   _lowerCAmelCase					     =		torch.max(torch.abs(our_output - their_output   )   ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
   _lowerCAmelCase					     =		torch.allclose(snake_case_   ,	snake_case_   ,	atol=1e-3   )
   print("""Do both models output the same tensors?"""   ,	"""🔥""" if success else """💩"""   )
   if not success:
      raise Exception("""Something went wRoNg"""   )

   pathlib.Path(snake_case_   ).mkdir(parents=snake_case_   ,	exist_ok=snake_case_   )
   print(F"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Any          =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
  )
  SCREAMING_SNAKE_CASE				:     Any          =	parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )