


'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

A__		:					Any												=  {"""vocab_file""": """spiece.model"""}

A__		:					Optional[int]												=  {
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

A__		:					Tuple												=  {"""bert_for_seq_generation""": 512}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					str							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Any							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					List[int]							     =			[]
    lowerCamelCase  :					Dict							     =			['input_ids', 'attention_mask']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_="<::::>"			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         __lowerCamelCase   :	Tuple								=		{} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     sep_token=SCREAMING_SNAKE_CASE_			,     sp_model_kwargs=self.sp_model_kwargs			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	List[Any]								=		vocab_file

         __lowerCamelCase   :	Any								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     Any:
         return self.sp_model.get_piece_size()







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[str]								=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab







    def __getstate__(							self	)				->     Dict:
         __lowerCamelCase   :	Optional[int]								=		self.__dict__.copy()
         __lowerCamelCase   :	int								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     Tuple:
         __lowerCamelCase   :	List[Any]								=		d

         # for backward compatibility
         if not hasattr(self			,     'sp_model_kwargs'	):
              __lowerCamelCase   :	int								=		{}

         __lowerCamelCase   :	List[Any]								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase   :	Optional[Any]								=		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
         return token







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         __lowerCamelCase   :	Optional[int]								=		[]
         __lowerCamelCase   :	Tuple								=		''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                   __lowerCamelCase   :	Dict								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Optional[int]								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

         return (out_vocab_file,)










'''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()
A__		:					Optional[int]												=  logging.get_logger(__name__)

A__		:					List[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""",
}
A__		:					Tuple												=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]							)     ->		Tuple:
     for attribute in key.split('.'							):
          __lowerCamelCase   :	List[Any]								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							)

     if weight_type is not None:
          __lowerCamelCase   :	Any								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							).shape
     else:
          __lowerCamelCase   :	Any								=		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   :	Tuple								=		value
     elif weight_type == "weight_g":
          __lowerCamelCase   :	Optional[int]								=		value
     elif weight_type == "weight_v":
          __lowerCamelCase   :	str								=		value
     elif weight_type == "bias":
          __lowerCamelCase   :	List[Any]								=		value
     else:
          __lowerCamelCase   :	List[str]								=		value

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Optional[int]:
     __lowerCamelCase   :	Optional[Any]								=		[]
     __lowerCamelCase   :	Dict								=		fairseq_model.state_dict()

     __lowerCamelCase   :	List[str]								=		hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __lowerCamelCase   :	Union[str, Any]								=		False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    hf_model.config.feat_extract_norm == 'group'      ,    )
               __lowerCamelCase   :	str								=		True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
                         __lowerCamelCase   :	int								=		True
                         if "*" in mapped_key:
                              __lowerCamelCase   :	Optional[int]								=		name.split(UpperCAmelCase_							)[0].split('.'							)[-2]
                              __lowerCamelCase   :	List[str]								=		mapped_key.replace('*'      ,    UpperCAmelCase_							)
                         if "weight_g" in name:
                              __lowerCamelCase   :	Dict								=		'weight_g'
                         elif "weight_v" in name:
                              __lowerCamelCase   :	Any								=		'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __lowerCamelCase   :	Any								=		'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __lowerCamelCase   :	List[Any]								=		'weight'
                         else:
                              __lowerCamelCase   :	str								=		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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				List[Any]							)     ->		Tuple:
     __lowerCamelCase   :	List[str]								=		full_name.split('conv_layers.'							)[-1]
     __lowerCamelCase   :	List[Any]								=		name.split('.'							)
     __lowerCamelCase   :	Any								=		int(items[0]							)
     __lowerCamelCase   :	Optional[Any]								=		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   :	Union[str, Any]								=		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   :	Tuple								=		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   :	str								=		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   :	List[Any]								=		value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
     else:
          unused_weights.append(UpperCAmelCase_							)



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Optional[Any]=None							)     ->		Optional[int]:
     # load the pre-trained checkpoints
     __lowerCamelCase   :	Optional[Any]								=		torch.load(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		WavLMConfigOrig(checkpoint['cfg']							)
     __lowerCamelCase   :	Union[str, Any]								=		WavLMOrig(UpperCAmelCase_							)
     model.load_state_dict(checkpoint['model']							)
     model.eval()

     if config_path is not None:
          __lowerCamelCase   :	Optional[int]								=		WavLMConfig.from_pretrained(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	Any								=		WavLMConfig()

     __lowerCamelCase   :	Optional[int]								=		WavLMModel(UpperCAmelCase_							)

     recursively_load_weights(UpperCAmelCase_      ,    UpperCAmelCase_							)

     hf_wavlm.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					List[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""")
      A__		:					List[str]												=  parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






