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

__lowerCamelCase      :			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.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''',
}
__lowerCamelCase      :			str							       =		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Optional[int]     ):







  """simple docstring"""





  for attribute in key.split("""."""     ):
    SCREAMING_SNAKE_CASE__      							=				getattr(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )

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

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	str     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE__      							=				hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE__      							=				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"""			,					)
      SCREAMING_SNAKE_CASE__      							=				True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""     )[-1] == name.split("""."""     )[0]:
          SCREAMING_SNAKE_CASE__      							=				True
          if "*" in mapped_key:
            SCREAMING_SNAKE_CASE__      							=				name.split(SCREAMING_SNAKE_CASE_     )[0].split("""."""     )[-2]
            SCREAMING_SNAKE_CASE__      							=				mapped_key.replace("""*"""			,					SCREAMING_SNAKE_CASE_     )
          if "weight_g" in name:
            SCREAMING_SNAKE_CASE__      							=				"""weight_g"""
          elif "weight_v" in name:
            SCREAMING_SNAKE_CASE__      							=				"""weight_v"""
          elif "bias" in name and "relative_attention_bias" not in name:
            SCREAMING_SNAKE_CASE__      							=				"""bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            SCREAMING_SNAKE_CASE__      							=				"""weight"""
          else:
            SCREAMING_SNAKE_CASE__      							=				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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	Dict     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				full_name.split("""conv_layers."""     )[-1]
  SCREAMING_SNAKE_CASE__      							=				name.split("""."""     )
  SCREAMING_SNAKE_CASE__      							=				int(items[0]     )
  SCREAMING_SNAKE_CASE__      							=				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."""
      )
      SCREAMING_SNAKE_CASE__      							=				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."""
      )
      SCREAMING_SNAKE_CASE__      							=				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."
      )
      SCREAMING_SNAKE_CASE__      							=				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."""
      )
      SCREAMING_SNAKE_CASE__      							=				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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	Union[str, Any]=None     ):







  """simple docstring"""





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

  if config_path is not None:
    SCREAMING_SNAKE_CASE__      							=				WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
  else:
    SCREAMING_SNAKE_CASE__      							=				WavLMConfig()

  SCREAMING_SNAKE_CASE__      							=				WavLMModel(SCREAMING_SNAKE_CASE_     )

  recursively_load_weights(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_     )

  hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE_     )


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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   __lowerCamelCase      :			List[str]							       =		parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase      :			int							       =		logging.get_logger(__name__)


__lowerCamelCase      :			Tuple							       =		{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
    raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()     )}."""     )

  if tokenizer_name is None:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES
  else:
    SCREAMING_SNAKE_CASE__      							=				{tokenizer_name: getattr(__UpperCamelCase			,					tokenizer_name + """Fast"""     )}

  logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""     )

  for tokenizer_name in tokenizer_names:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES[tokenizer_name]

    SCREAMING_SNAKE_CASE__      							=				True
    if checkpoint_name is None:
      SCREAMING_SNAKE_CASE__      							=				list(tokenizer_class.max_model_input_sizes.keys()     )
    else:
      SCREAMING_SNAKE_CASE__      							=				[checkpoint_name]

    logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""     )

    for checkpoint in checkpoint_names:
      logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""     )

      # Load tokenizer
      SCREAMING_SNAKE_CASE__      							=				tokenizer_class.from_pretrained(__UpperCamelCase			,					force_download=__UpperCamelCase     )

      # Save fast tokenizer
      logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""     )

      # For organization names we create sub-directories
      if "/" in checkpoint:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint.split("""/"""     )
        SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
      elif add_prefix:
        SCREAMING_SNAKE_CASE__      							=				checkpoint
        SCREAMING_SNAKE_CASE__      							=				dump_path
      else:
        SCREAMING_SNAKE_CASE__      							=				None
        SCREAMING_SNAKE_CASE__      							=				dump_path

      logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()     )[0]:
        SCREAMING_SNAKE_CASE__      							=				list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
        SCREAMING_SNAKE_CASE__      							=				file_path.split(__UpperCamelCase     )[-1][0]
        if next_char == "/":
          SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
          SCREAMING_SNAKE_CASE__      							=				None

        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      SCREAMING_SNAKE_CASE__      							=				tokenizer.save_pretrained(
          __UpperCamelCase			,					legacy_format=__UpperCamelCase			,					filename_prefix=__UpperCamelCase     )
      logger.info(f"""=> File names {file_names}"""     )

      for file_name in file_names:
        if not file_name.endswith("""tokenizer.json"""     ):
          os.remove(__UpperCamelCase     )
          logger.info(f"""=> removing {file_name}"""     )


if __name__ == "__main__":
   __lowerCamelCase      :			str							       =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   __lowerCamelCase      :			Any							       =		parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
