'''simple docstring'''





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Union[str, 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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE_:     Optional[int]  =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def 	lowerCAmelCase_				(					snake_case_       : Any					,    snake_case_       : List[str]					,    snake_case_       : str					,    snake_case_       : int					,    snake_case_       : int			)    ->      Optional[int]:


     '''simple docstring'''




     for attribute in key.split("."			):
          UpperCAmelCase_           =   getattr(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE			)

     if weight_type is not None:
          UpperCAmelCase_           =   getattr(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE			).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_				(					snake_case_       : List[Any]					,    snake_case_       : Dict			)    ->      str:


     '''simple docstring'''




     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(
                   _SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE					,    hf_model.config.feat_extract_norm == "group"					,    )
               UpperCAmelCase_           =   True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
                         UpperCAmelCase_           =   True
                         if "*" in mapped_key:
                              UpperCAmelCase_           =   name.split(_SCREAMING_SNAKE_CASE			)[0].split("."			)[-2]
                              UpperCAmelCase_           =   mapped_key.replace("*"					,    _SCREAMING_SNAKE_CASE			)
                         if "weight_g" in name:
                              UpperCAmelCase_           =   "weight_g"
                         elif "weight_v" in name:
                              UpperCAmelCase_           =   "weight_v"
                         elif "bias" in name and "relative_attention_bias" not in name:
                              UpperCAmelCase_           =   "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              UpperCAmelCase_           =   "weight"
                         else:
                              UpperCAmelCase_           =   None

                         set_recursively(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE			)
                    continue
          if not is_used:
               unused_weights.append(_SCREAMING_SNAKE_CASE			)

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





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : int					,    snake_case_       : List[Any]					,    snake_case_       : Tuple					,    snake_case_       : Optional[int]			)    ->      Tuple:


     '''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(_SCREAMING_SNAKE_CASE			)





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


     '''simple docstring'''




     UpperCAmelCase_           =   torch.load(_SCREAMING_SNAKE_CASE			)
     UpperCAmelCase_           =   WavLMConfigOrig(checkpoint["cfg"]			)
     UpperCAmelCase_           =   WavLMOrig(_SCREAMING_SNAKE_CASE			)
     model.load_state_dict(checkpoint["model"]			)
     model.eval()

     if config_path is not None:
          UpperCAmelCase_           =   WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
     else:
          UpperCAmelCase_           =   WavLMConfig()

     UpperCAmelCase_           =   WavLMModel(_SCREAMING_SNAKE_CASE			)

     recursively_load_weights(_SCREAMING_SNAKE_CASE					,    _SCREAMING_SNAKE_CASE			)

     hf_wavlm.save_pretrained(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Tuple  =   {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__       :      int  =   {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE__       :      str  =   {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE__       :      Dict  =   0
SCREAMING_SNAKE_CASE__       :      Tuple  =   1
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   2
SCREAMING_SNAKE_CASE__       :      List[str]  =   3
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   4




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	Optional[Any]	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	Optional[int]	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	List[str]	     =      """left"""







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  UpperCamelCase__=False     ,  UpperCamelCase__="<s>"     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__="<sep>"     ,  UpperCamelCase__="<pad>"     ,  UpperCamelCase__="<cls>"     ,  UpperCamelCase__="<mask>"     ,  UpperCamelCase__=["<eop>", "<eod>"]     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      None:
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase   :      str			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else mask_token

           lowerCamelCase   :      Dict			 =					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  additional_special_tokens=UpperCamelCase__     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **UpperCamelCase__     ,  )

           lowerCamelCase   :      Any			 =					3

           lowerCamelCase   :      Optional[Any]			 =					do_lower_case
           lowerCamelCase   :      List[Any]			 =					remove_space
           lowerCamelCase   :      str			 =					keep_accents
           lowerCamelCase   :      List[Any]			 =					vocab_file

           lowerCamelCase   :      int			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(UpperCamelCase__     )







    @property
    def     _lowercase     (    self     )     ->      Optional[Any]:
           return len(self.sp_model     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					{self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
           vocab.update(self.added_tokens_encoder     )
           return vocab







    def __getstate__(    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[int]			 =					self.__dict__.copy()
           lowerCamelCase   :      Union[str, Any]			 =					None
           return state







    def __setstate__(    self     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      int			 =					d

           # for backward compatibility
           if not hasattr(self     ,  "sp_model_kwargs"     ):
                  lowerCamelCase   :      Any			 =					{}

           lowerCamelCase   :      Optional[int]			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(self.vocab_file     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Any:
           if self.remove_space:
                  lowerCamelCase   :      Dict			 =					" ".join(inputs.strip().split()     )
           else:
                  lowerCamelCase   :      Union[str, Any]			 =					inputs
           lowerCamelCase   :      Optional[Any]			 =					outputs.replace("``"     ,  "\""     ).replace("''"     ,  "\""     )

           if not self.keep_accents:
                  lowerCamelCase   :      Optional[int]			 =					unicodedata.normalize("NFKD"     ,  UpperCamelCase__     )
                  lowerCamelCase   :      List[Any]			 =					"".join([c for c in outputs if not unicodedata.combining(UpperCamelCase__     )]     )
           if self.do_lower_case:
                  lowerCamelCase   :      List[str]			 =					outputs.lower()

           return outputs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					self.preprocess_text(UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					self.sp_model.encode(UpperCamelCase__     ,  out_type=UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					[]
           for piece in pieces:
                  if len(UpperCamelCase__     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
                         lowerCamelCase   :      List[Any]			 =					self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__     ,  ""     )     )
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]     ) == 1:
                                       lowerCamelCase   :      Union[str, Any]			 =					cur_pieces[1:]
                                else:
                                       lowerCamelCase   :      Optional[int]			 =					cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]     )
                         new_pieces.extend(UpperCamelCase__     )
                  else:
                         new_pieces.append(UpperCamelCase__     )

           return new_pieces







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      int:
           return self.sp_model.PieceToId(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           return self.sp_model.IdToPiece(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					"".join(UpperCamelCase__     ).replace(UpperCamelCase__     ,  " "     ).strip()
           return out_string







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = True     ,  **UpperCamelCase__     ,  )     ->      str:
           lowerCamelCase   :      Optional[int]			 =					kwargs.pop("use_source_tokenizer"     ,  UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					self.convert_ids_to_tokens(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     )

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           lowerCamelCase   :      Any			 =					[]
           lowerCamelCase   :      Any			 =					[]
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__     )     )
                                lowerCamelCase   :      int			 =					[]
                         sub_texts.append(UpperCamelCase__     )
                  else:
                         current_sub_text.append(UpperCamelCase__     )
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__     )     )

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           lowerCamelCase   :      Union[str, Any]			 =					"".join(UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowerCamelCase   :      int			 =					self.clean_up_tokenization(UpperCamelCase__     )
                  return clean_text
           else:
                  return text







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      str			 =					[self.sep_token_id]
           lowerCamelCase   :      Optional[int]			 =					[self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     )     ->      List[int]:

           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCamelCase__     ,  token_ids_a=UpperCamelCase__     ,  already_has_special_tokens=UpperCamelCase__     )

           if token_ids_a is not None:
                  return ([0] * len(UpperCamelCase__     )) + [1] + ([0] * len(UpperCamelCase__     )) + [1, 1]
           return ([0] * len(UpperCamelCase__     )) + [1, 1]







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      Any			 =					[self.sep_token_id]
           lowerCamelCase   :      List[str]			 =					[2]

           if token_ids_a is None:
                  return len(token_ids_a + sep     ) * [0] + cls_segment_id
           return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      Union[str, Any]			 =					os.path.join(
               UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

           if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase__     ) and os.path.isfile(self.vocab_file     ):
                  copyfile(self.vocab_file     ,  UpperCamelCase__     )
           elif not os.path.isfile(self.vocab_file     ):
                  with open(UpperCamelCase__     ,  "wb"     ) as fi:
                         lowerCamelCase   :      str			 =					self.sp_model.serialized_model_proto()
                         fi.write(UpperCamelCase__     )

           return (out_vocab_file,)






