



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__		:				Dict          =	logging.get_logger(__name__)

lowercase__		:				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_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""",
}
lowercase__		:				Optional[Any]          =	[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Optional[int]						)						->   Tuple:

  """simple docstring"""




  for attribute in key.split('.'						):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        lowerCAmelCase_   :				Tuple								= '''lm_head'''

    lowerCAmelCase_   :				Tuple								= getattr(__lowerCamelCase    ,   __lowerCamelCase						)

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

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Optional[int]						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= []
  lowerCAmelCase_   :				int								= fairseq_model.state_dict()

  lowerCAmelCase_   :				str								= hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase_   :				Optional[int]								= False
    if "conv_layers" in name:
      load_conv_layer(
          __lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase    ,   hf_model.config.feat_extract_norm == 'group'    ,   )
      lowerCAmelCase_   :				int								= True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCAmelCase_   :				Union[str, Any]								= '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
          lowerCAmelCase_   :				List[str]								= True
          if "*" in mapped_key:
            lowerCAmelCase_   :				Tuple								= name.split(__lowerCamelCase						)[0].split('.'						)[-2]
            lowerCAmelCase_   :				Union[str, Any]								= mapped_key.replace('*'    ,   __lowerCamelCase						)
          if "weight_g" in name:
            lowerCAmelCase_   :				int								= '''weight_g'''
          elif "weight_v" in name:
            lowerCAmelCase_   :				Tuple								= '''weight_v'''
          elif "bias" in name:
            lowerCAmelCase_   :				Tuple								= '''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCAmelCase_   :				List[str]								= '''weight'''
          else:
            lowerCAmelCase_   :				Dict								= None
          set_recursively(__lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase						)
        continue
    if not is_used:
      unused_weights.append(__lowerCamelCase						)

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Union[str, Any]						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Dict								= full_name.split('conv_layers.'						)[-1]
  lowerCAmelCase_   :				int								= name.split('.'						)
  lowerCAmelCase_   :				str								= int(items[0]						)
  lowerCAmelCase_   :				int								= 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_   :				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_   :				int								= 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_   :				Any								= 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[str]								= value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
  else:
    unused_weights.append(__lowerCamelCase						)

@torch.no_grad()
def 				UpperCamelCase_    (    lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Optional[int]=None    ,   lowerCAmelCase__      :				Optional[int]=None    ,   lowerCAmelCase__      :				Union[str, Any]=True						)						->   List[Any]:

  """simple docstring"""




  if config_path is not None:
    lowerCAmelCase_   :				Union[str, Any]								= UniSpeechConfig.from_pretrained(__lowerCamelCase						)
  else:
    lowerCAmelCase_   :				Optional[int]								= UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      lowerCAmelCase_   :				Union[str, Any]								= Dictionary.load_from_json(__lowerCamelCase						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCAmelCase_   :				Optional[int]								= target_dict.pad_index
      lowerCAmelCase_   :				Optional[Any]								= target_dict.bos_index
      lowerCAmelCase_   :				Optional[int]								= target_dict.eos_index
      lowerCAmelCase_   :				Tuple								= len(target_dict.symbols						)
      lowerCAmelCase_   :				Optional[int]								= 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						)
      lowerCAmelCase_   :				Tuple								= target_dict.indices

      # fairseq has the <pad> and <s> switched
      lowerCAmelCase_   :				Any								= 42
      lowerCAmelCase_   :				Union[str, Any]								= 43
      with open(__lowerCamelCase    ,   'w'    ,   encoding='utf-8'						) as vocab_handle:
        json.dump(__lowerCamelCase    ,   __lowerCamelCase						)
      lowerCAmelCase_   :				Tuple								= WavaVecaPhonemeCTCTokenizer(
          __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    ,   )
      lowerCAmelCase_   :				str								= True if config.feat_extract_norm == '''layer''' else False
      lowerCAmelCase_   :				Union[str, Any]								= WavaVecaFeatureExtractor(
          feature_size=1    ,   sampling_rate=1_6000    ,   padding_value=0    ,   do_normalize=__lowerCamelCase    ,   return_attention_mask=__lowerCamelCase    ,   )
      lowerCAmelCase_   :				Tuple								= WavaVecaProcessor(feature_extractor=__lowerCamelCase    ,   tokenizer=__lowerCamelCase						)
      processor.save_pretrained(__lowerCamelCase						)

    lowerCAmelCase_   :				List[str]								= UniSpeechForCTC(__lowerCamelCase						)
  else:
    lowerCAmelCase_   :				List[Any]								= UniSpeechForPreTraining(__lowerCamelCase						)

  if is_finetuned:
    lowerCAmelCase_   :				Dict								= fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,   arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						), 'w2v_path': checkpoint_path}						)
  else:
    lowerCAmelCase_   :				Tuple								= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

  lowerCAmelCase_   :				Union[str, Any]								= model[0].eval()

  recursively_load_weights(__lowerCamelCase    ,   __lowerCamelCase    ,   __lowerCamelCase						)

  hf_unispeech.save_pretrained(__lowerCamelCase						)


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










"""simple docstring"""







import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				debug_launcher(test_script.main						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Tuple:






				"""simple docstring"""



				debug_launcher(test_ops.main						)
