




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase  :				Union[str, Any]						=				logging.get_logger(__name__)

_lowerCAmelCase  :				Any						=				{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}



def   lowerCAmelCase						(						_lowerCAmelCase							:				List[Any]  ,							_lowerCAmelCase							:				Dict  ,							_lowerCAmelCase							:				Union[str, Any]  ,							_lowerCAmelCase							:				Optional[Any]  ,							_lowerCAmelCase							:				Dict		):




 """simple docstring"""







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

 if weight_type is not None:
  UpperCAmelCase__								=  getattr(_lowerCAmelCase  ,							_lowerCAmelCase		).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						(						_lowerCAmelCase							:				List[str]  ,							_lowerCAmelCase							:				Any  ,							_lowerCAmelCase							:				Union[str, Any]		):




 """simple docstring"""







 UpperCAmelCase__								=  []
 UpperCAmelCase__								=  fairseq_model.state_dict()

 UpperCAmelCase__								=  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase__								=  False
  if "conv_layers" in name:
   load_conv_layer(
       _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							hf_model.config.feat_extract_norm == "group"  ,							)
   UpperCAmelCase__								=  True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCAmelCase__								=  "sew_d." + 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]:
     UpperCAmelCase__								=  True
     if "*" in mapped_key:
      UpperCAmelCase__								=  name.split(_lowerCAmelCase		)[0].split("."		)[-2]
      if not layer_index.isnumeric():
       continue
      UpperCAmelCase__								=  mapped_key.replace("*"  ,							_lowerCAmelCase		)
     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(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)
    continue
  if not is_used:
   unused_weights.append(_lowerCAmelCase		)

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



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				Optional[Any]  ,							_lowerCAmelCase							:				List[Any]  ,							_lowerCAmelCase							:				Dict  ,							_lowerCAmelCase							:				Optional[Any]		):




 """simple docstring"""







 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(_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				List[Any]  ,							_lowerCAmelCase							:				str		):




 """simple docstring"""







 UpperCAmelCase__								=  SEWDConfig()
 if is_finetuned:
  UpperCAmelCase__								=  model.wav_encoder.wav_model.cfg
 else:
  UpperCAmelCase__								=  model.cfg

 UpperCAmelCase__								=  fs_config.conv_bias
 UpperCAmelCase__								=  eval(fs_config.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__								=  0.0
 UpperCAmelCase__								=  fs_config.activation_fn.name
 UpperCAmelCase__								=  fs_config.encoder_embed_dim
 UpperCAmelCase__								=  0.02
 UpperCAmelCase__								=  fs_config.encoder_ffn_embed_dim
 UpperCAmelCase__								=  1E-5
 UpperCAmelCase__								=  fs_config.encoder_layerdrop
 UpperCAmelCase__								=  fs_config.encoder_attention_heads
 UpperCAmelCase__								=  fs_config.conv_pos_groups
 UpperCAmelCase__								=  fs_config.conv_pos
 UpperCAmelCase__								=  len(_lowerCAmelCase		)
 UpperCAmelCase__								=  fs_config.encoder_layers
 UpperCAmelCase__								=  fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 UpperCAmelCase__								=  fs_config.max_position_embeddings
 UpperCAmelCase__								=  fs_config.position_buckets
 UpperCAmelCase__								=  fs_config.share_att_key
 UpperCAmelCase__								=  fs_config.relative_attention
 UpperCAmelCase__								=  fs_config.position_biased_input
 UpperCAmelCase__								=  tuple(fs_config.pos_att_type.split("|"		)		)
 UpperCAmelCase__								=  fs_config.norm_rel_ebd

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  UpperCAmelCase__								=  model.cfg
  UpperCAmelCase__								=  fs_config.final_dropout
  UpperCAmelCase__								=  fs_config.layerdrop
 UpperCAmelCase__								=  fs_config.activation_dropout
 UpperCAmelCase__								=  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 UpperCAmelCase__								=  fs_config.attention_dropout
 UpperCAmelCase__								=  fs_config.dropout_input
 UpperCAmelCase__								=  fs_config.dropout
 UpperCAmelCase__								=  fs_config.mask_channel_length
 UpperCAmelCase__								=  fs_config.mask_channel_prob
 UpperCAmelCase__								=  fs_config.mask_length
 UpperCAmelCase__								=  fs_config.mask_prob

 UpperCAmelCase__								=  "Wav2Vec2FeatureExtractor"
 UpperCAmelCase__								=  "Wav2Vec2CTCTokenizer"

 return config



@torch.no_grad()
def   lowerCAmelCase						(						_lowerCAmelCase							:				List[str]  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				List[str]=None  ,							_lowerCAmelCase							:				int=None  ,							_lowerCAmelCase							:				List[Any]=True		):




 """simple docstring"""







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

 if config_path is not None:
  UpperCAmelCase__								=  SEWDConfig.from_pretrained(_lowerCAmelCase		)
 else:
  UpperCAmelCase__								=  convert_config(model[0]  ,							_lowerCAmelCase		)
 UpperCAmelCase__								=  model[0].eval()

 UpperCAmelCase__								=  True if config.feat_extract_norm == "layer" else False
 UpperCAmelCase__								=  WavaVecaFeatureExtractor(
     feature_size=1  ,							sampling_rate=1_6000  ,							padding_value=0  ,							do_normalize=_lowerCAmelCase  ,							return_attention_mask=_lowerCAmelCase  ,							)

 if is_finetuned:
  if dict_path:
   UpperCAmelCase__								=  Dictionary.load(_lowerCAmelCase		)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   UpperCAmelCase__								=  target_dict.pad_index
   UpperCAmelCase__								=  target_dict.bos_index
   UpperCAmelCase__								=  target_dict.pad_index
   UpperCAmelCase__								=  target_dict.bos_index
   UpperCAmelCase__								=  target_dict.eos_index
   UpperCAmelCase__								=  len(target_dict.symbols		)
   UpperCAmelCase__								=  os.path.join(_lowerCAmelCase  ,							"vocab.json"		)
   if not os.path.isdir(_lowerCAmelCase		):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase		)		)
    return
   os.makedirs(_lowerCAmelCase  ,							exist_ok=_lowerCAmelCase		)
   with open(_lowerCAmelCase  ,							"w"  ,							encoding="utf-8"		) as vocab_handle:
    json.dump(target_dict.indices  ,							_lowerCAmelCase		)
   UpperCAmelCase__								=  WavaVecaCTCTokenizer(
       _lowerCAmelCase  ,							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=_lowerCAmelCase  ,							)
   UpperCAmelCase__								=  WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,							tokenizer=_lowerCAmelCase		)
   processor.save_pretrained(_lowerCAmelCase		)

  UpperCAmelCase__								=  SEWDForCTC(_lowerCAmelCase		)
 else:
  UpperCAmelCase__								=  SEWDModel(_lowerCAmelCase		)
  feature_extractor.save_pretrained(_lowerCAmelCase		)

 recursively_load_weights(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)

 hf_model.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
  _lowerCAmelCase  :				str						=				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(
      "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  _lowerCAmelCase  :				int						=				parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )






from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase  :				Dict						=				logging.get_logger(__name__)

_lowerCAmelCase  :				Optional[int]						=				{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""rembert"""
      def __init__(     self				:Union[str, Any]	, lowerCamelCase				:Union[str, Any]=25_0300	, lowerCamelCase				:Optional[Any]=1152	, lowerCamelCase				:Optional[int]=32	, lowerCamelCase				:Union[str, Any]=18	, lowerCamelCase				:str=256	, lowerCamelCase				:Any=1664	, lowerCamelCase				:Any=4608	, lowerCamelCase				:List[str]="gelu"	, lowerCamelCase				:Optional[Any]=0.0	, lowerCamelCase				:Optional[int]=0.0	, lowerCamelCase				:Dict=0.1	, lowerCamelCase				:List[str]=512	, lowerCamelCase				:int=2	, lowerCamelCase				:str=0.02	, lowerCamelCase				:Optional[int]=1e-12	, lowerCamelCase				:List[Any]=True	, lowerCamelCase				:Optional[Any]=0	, lowerCamelCase				:Optional[Any]=312	, lowerCamelCase				:int=313	, **lowerCamelCase				:Dict	, )       ->     Any:
       super().__init__(pad_token_id=lowerCamelCase	, bos_token_id=lowerCamelCase	, eos_token_id=lowerCamelCase	, **lowerCamelCase )

       UpperCAmelCase__								=  vocab_size
       UpperCAmelCase__								=  input_embedding_size
       UpperCAmelCase__								=  output_embedding_size
       UpperCAmelCase__								=  max_position_embeddings
       UpperCAmelCase__								=  hidden_size
       UpperCAmelCase__								=  num_hidden_layers
       UpperCAmelCase__								=  num_attention_heads
       UpperCAmelCase__								=  intermediate_size
       UpperCAmelCase__								=  hidden_act
       UpperCAmelCase__								=  hidden_dropout_prob
       UpperCAmelCase__								=  attention_probs_dropout_prob
       UpperCAmelCase__								=  classifier_dropout_prob
       UpperCAmelCase__								=  initializer_range
       UpperCAmelCase__								=  type_vocab_size
       UpperCAmelCase__								=  layer_norm_eps
       UpperCAmelCase__								=  use_cache
       UpperCAmelCase__								=  False



class      _UpperCamelCase (			lowerCAmelCase						):
      @property
      def 		UpperCAmelCase_  (     self				:List[Any] )       ->     Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
        UpperCAmelCase__								=  {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCAmelCase__								=  {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ] )







      @property
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     float:
       return 1e-4





