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_		       =   logging.get_logger(__name__)

a_		       =   {
    """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_		       =   [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def    __lowerCAmelCase   (    A_ :			Any    ,						A_ :			List[str]    ,						A_ :			str    ,						A_ :			Optional[int]    ,						A_ :			List[Any]       )     ->	Optional[int]:





  for attribute in key.split("."       ):
    __UpperCAmelCase         =							getattr(a__    ,						a__       )

  if weight_type is not None:
    __UpperCAmelCase         =							getattr(a__    ,						a__       ).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   (    A_ :			str    ,						A_ :			List[str]       )     ->	Dict:
  __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(
          a__    ,						a__    ,						a__    ,						a__    ,						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(a__       )[0].split("."       )[-2]
            __UpperCAmelCase         =							mapped_key.replace("*"    ,						a__       )
          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(a__    ,						a__    ,						a__    ,						a__    ,						a__       )
        continue
    if not is_used:
      unused_weights.append(a__       )

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




def    __lowerCAmelCase   (    A_ :			List[Any]    ,						A_ :			Optional[int]    ,						A_ :			List[Any]    ,						A_ :			Any    ,						A_ :			Any       )     ->	Dict:
  __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(a__       )




@torch.no_grad()
def    __lowerCAmelCase   (    A_ :			List[str]    ,						A_ :			Tuple    ,						A_ :			Optional[int]=None       )     ->	Optional[Any]:
  __UpperCAmelCase         =							torch.load(a__       )
  __UpperCAmelCase         =							WavLMConfigOrig(checkpoint["cfg"]       )
  __UpperCAmelCase         =							WavLMOrig(a__       )
  model.load_state_dict(checkpoint["model"]       )
  model.eval()

  if config_path is not None:
    __UpperCAmelCase         =							WavLMConfig.from_pretrained(a__       )
  else:
    __UpperCAmelCase         =							WavLMConfig()

  __UpperCAmelCase         =							WavLMModel(a__       )

  recursively_load_weights(a__    ,						a__       )

  hf_wavlm.save_pretrained(a__       )


if __name__ == "__main__":
       a_		       =   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_		       =   parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_big_bird import BigBirdTokenizer
else:
					UpperCamelCase__           =			None

UpperCamelCase__           =			logging.get_logger(__name__)
UpperCamelCase__           =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__           =			{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__           =			{
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


UpperCamelCase__           =			"▁"

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							VOCAB_FILES_NAMES
							snake_case  :   List[str]      =							PRETRAINED_VOCAB_FILES_MAP
							snake_case  :   List[str]      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case  :   int      =							BigBirdTokenizer
							snake_case  :   List[Any]      =							["""input_ids""", """attention_mask"""]
							snake_case  :   List[int]      =							[]






							def __init__(    self	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase="<unk>"	,			__lowerCAmelCase="<s>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="<pad>"	,			__lowerCAmelCase="[SEP]"	,			__lowerCAmelCase="[MASK]"	,			__lowerCAmelCase="[CLS]"	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else bos_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else eos_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else unk_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else pad_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else cls_token
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else sep_token

														# Mask token behave like a normal word, i.e. include the space before it
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else mask_token

														super().__init__(
														    __lowerCAmelCase	,			tokenizer_file=__lowerCAmelCase	,			bos_token=__lowerCAmelCase	,			eos_token=__lowerCAmelCase	,			unk_token=__lowerCAmelCase	,			sep_token=__lowerCAmelCase	,			pad_token=__lowerCAmelCase	,			cls_token=__lowerCAmelCase	,			mask_token=__lowerCAmelCase	,			**__lowerCAmelCase	,			)

														UpperCamelCase__					  =      vocab_file
														UpperCamelCase__					  =      False if not self.vocab_file else True






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = False	):

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    """You should not supply a second sequence if the provided sequence of """
																												    """ids is already formatted with special tokens for the model."""	)
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [self.cls_token_id]

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







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""	)

														if not os.path.isdir(__lowerCAmelCase	):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
																					return
														UpperCamelCase__					  =      os.path.join(
														    __lowerCAmelCase	,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

														if os.path.abspath(self.vocab_file	) != os.path.abspath(__lowerCAmelCase	):
																					copyfile(self.vocab_file	,			__lowerCAmelCase	)

														return (out_vocab_file,)




