






"""simple docstring"""




import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


UpperCamelCase							       =    logging.getLogger(__name__)

UpperCamelCase							       =    50  # max width of layer names
UpperCamelCase							       =    70  # max width of quantizer names






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	parser.add_argument_group("quant_trainer arguments"					)
  group.add_argument("--wprec"     , type=_lowerCamelCase     , default=8     , help="weight precision"					)
  group.add_argument("--aprec"     , type=_lowerCamelCase     , default=8     , help="activation precision"					)
  group.add_argument("--quant-per-tensor"     , action="store_true"     , help="per tensor weight scaling"					)
  group.add_argument("--quant-disable"     , action="store_true"     , help="disable all quantizers"					)
  group.add_argument("--quant-disable-embeddings"     , action="store_true"     , help="disable all embeddings quantizers"					)
  group.add_argument("--quant-disable-keyword"     , type=_lowerCamelCase     , nargs="+"     , help="disable quantizers by keyword"					)
  group.add_argument("--quant-disable-layer-module"     , type=_lowerCamelCase     , help="disable quantizers by keyword under layer."					)
  group.add_argument("--quant-enable-layer-module"     , type=_lowerCamelCase     , help="enable quantizers by keyword under layer"					)
  group.add_argument("--calibrator"     , default="max"     , help="which quantization range calibrator to use"					)
  group.add_argument("--percentile"     , default=_lowerCamelCase     , type=_lowerCamelCase     , help="percentile for PercentileCalibrator"					)
  group.add_argument("--fuse-qkv"     , action="store_true"     , help="use the same scale factor for qkv"					)
  group.add_argument("--clip-gelu"     , metavar="N"     , type=_lowerCamelCase     , help="clip gelu output maximum value to N"					)
  group.add_argument(
      "--recalibrate-weights"     , action="store_true"     , help=(
          "recalibrate weight amaxes by taking the max of the weights."
          " amaxes will be computed with the current quantization granularity (axis)."
      )     , )






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							Dict:







  '''simple docstring'''







  if args.calibrator == "max":
    _a							=	"max"
  elif args.calibrator == "percentile":
    if args.percentile is None:
      raise ValueError("Specify --percentile when using percentile calibrator"					)
    _a							=	"histogram"
  elif args.calibrator == "mse":
    _a							=	"histogram"
  else:
    raise ValueError(f'''Invalid calibrator {args.calibrator}'''					)

  _a							=	QuantDescriptor(num_bits=args.aprec     , calib_method=_lowerCamelCase					)
  _a							=	QuantDescriptor(num_bits=args.wprec     , axis=(None if args.quant_per_tensor else (0,))					)
  quant_nn.QuantLinear.set_default_quant_desc_input(_lowerCamelCase					)
  quant_nn.QuantLinear.set_default_quant_desc_weight(_lowerCamelCase					)






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							int=False     , UpperCamelCase_:							List[Any]=False					)       ->							int:







  '''simple docstring'''







  logger.info("Configuring Model for Quantization"					)
  logger.info(f'''using quantization package {pytorch_quantization.__file__}'''					)

  if not calib:
    if args.quant_disable_embeddings:
      set_quantizer_by_name(_lowerCamelCase     , ["embeddings"]     , which="weight"     , _disabled=_lowerCamelCase					)

    if args.quant_disable:
      set_quantizer_by_name(_lowerCamelCase     , [""]     , _disabled=_lowerCamelCase					)

    if args.quant_disable_keyword:
      set_quantizer_by_name(_lowerCamelCase     , args.quant_disable_keyword     , _disabled=_lowerCamelCase					)

    if args.quant_disable_layer_module:
      set_quantizer_by_name(_lowerCamelCase     , [r"layer.\d+." + args.quant_disable_layer_module]     , _disabled=_lowerCamelCase					)

    if args.quant_enable_layer_module:
      set_quantizer_by_name(_lowerCamelCase     , [r"layer.\d+." + args.quant_enable_layer_module]     , _disabled=_lowerCamelCase					)

    if args.recalibrate_weights:
      recalibrate_weights(_lowerCamelCase					)

    if args.fuse_qkv:
      fuse_qkv(_lowerCamelCase     , _lowerCamelCase					)

  if args.clip_gelu:
    clip_gelu(_lowerCamelCase     , args.clip_gelu					)

  # if args.local_rank in [-1, 0] and not calib:
  print_quant_summary(_lowerCamelCase					)






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							Optional[Any]:







  '''simple docstring'''







  logger.info("Enabling Calibration"					)
  for name, module in model.named_modules():
    if name.endswith("_quantizer"					):
      if module._calibrator is not None:
        module.disable_quant()
        module.enable_calib()
      else:
        module.disable()
      logger.info(f'''{name:80}: {module}'''					)






def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							List[Any]					)       ->							Dict:







  '''simple docstring'''







  logger.info("Loading calibrated amax"					)
  for name, module in model.named_modules():
    if name.endswith("_quantizer"					):
      if module._calibrator is not None:
        if isinstance(module._calibrator     , calib.MaxCalibrator					):
          module.load_calib_amax()
        else:
          module.load_calib_amax("percentile"     , percentile=args.percentile					)
        module.enable_quant()
        module.disable_calib()
      else:
        module.enable()
  model.cuda()
  print_quant_summary(_lowerCamelCase					)






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Optional[int]					)       ->							str:







  '''simple docstring'''







  def fusea(UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int     , UpperCamelCase_:							str					):
    for mod in [qq, qk, qv]:
      if not hasattr(_lowerCamelCase     , "_amax"					):
        print("          WARNING: NO AMAX BUFFER"					)
        return
    _a							=	qq._amax.detach().item()
    _a							=	qk._amax.detach().item()
    _a							=	qv._amax.detach().item()

    _a							=	max(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase					)
    qq._amax.fill_(_lowerCamelCase					)
    qk._amax.fill_(_lowerCamelCase					)
    qv._amax.fill_(_lowerCamelCase					)
    logger.info(f'''          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'''					)

  for name, mod in model.named_modules():
    if name.endswith(".attention.self"					):
      logger.info(f'''FUSE_QKV: {name:{name_width}}'''					)
      fusea(mod.matmul_q_input_quantizer     , mod.matmul_k_input_quantizer     , mod.matmul_v_input_quantizer					)
      if args.quant_per_tensor:
        fusea(mod.query._weight_quantizer     , mod.key._weight_quantizer     , mod.value._weight_quantizer					)






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]     , UpperCamelCase_:							Tuple					)       ->							Tuple:







  '''simple docstring'''







  for name, mod in model.named_modules():
    if name.endswith(".output.dense"					) and not name.endswith("attention.output.dense"					):
      _a							=	mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=_lowerCamelCase					)
      _a							=	mod._input_quantizer._amax.data.detach().item()
      logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'''					)






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							Dict:







  '''simple docstring'''







  for name, mod in model.named_modules():
    if hasattr(_lowerCamelCase     , "_weight_quantizer"					) and mod._weight_quantizer.axis is not None:
      _a							=	mod.weight.shape[0]
      _a							=	mod._weight_quantizer._amax.detach()
      _a							=	torch.ones(_lowerCamelCase     , dtype=amax.dtype     , device=amax.device					) * amax
      print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}'''					)






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]					)       ->							int:







  '''simple docstring'''







  for name, mod in model.named_modules():
    if hasattr(_lowerCamelCase     , "_weight_quantizer"					):
      if not hasattr(mod.weight_quantizer     , "_amax"					):
        print("RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"					)
        continue

      # determine which axes to reduce across
      # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
      _a							=	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis					)
      _a							=	set(range(len(mod.weight.size()					)					)					) - axis_set
      _a							=	pytorch_quantization.utils.reduce_amax(mod.weight     , axis=_lowerCamelCase     , keepdims=_lowerCamelCase					).detach()
      logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''					)
      _a							=	amax






def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							List[Any]=25     , UpperCamelCase_:							List[Any]=180     , UpperCamelCase_:							str=None					)       ->							Any:







  '''simple docstring'''







  if ignore is None:
    _a							=	[]
  elif not isinstance(_lowerCamelCase     , _lowerCamelCase					):
    _a							=	[ignore]

  _a							=	0
  for name, mod in model.named_modules():
    if not hasattr(_lowerCamelCase     , "weight"					):
      continue
    _a							=	max(_lowerCamelCase     , len(_lowerCamelCase					)					)

  for name, mod in model.named_modules():
    _a							=	getattr(_lowerCamelCase     , "_input_quantizer"     , _lowerCamelCase					)
    _a							=	getattr(_lowerCamelCase     , "_weight_quantizer"     , _lowerCamelCase					)
    if not hasattr(_lowerCamelCase     , "weight"					):
      continue
    if type(_lowerCamelCase					) in ignore:
      continue
    if [True for s in ignore if type(_lowerCamelCase					) is str and s in name]:
      continue
    _a							=	f'''Act:{input_q.extra_repr()}'''
    _a							=	f'''Wgt:{weight_q.extra_repr()}'''
    _a							=	f'''{name:{name_width}} {act_str} {wgt_str}'''
    if len(_lowerCamelCase					) <= line_width:
      logger.info(_lowerCamelCase					)
    else:
      logger.info(f'''{name:{name_width}} {act_str}'''					)
      logger.info(f'''{'  ':{name_width}} {wgt_str}'''					)






def        lowerCAmelCase (      UpperCamelCase_:							str					)       ->							Tuple:







  '''simple docstring'''







  _a							=	0
  for name, mod in model.named_modules():
    if isinstance(_lowerCamelCase     , pytorch_quantization.nn.TensorQuantizer					):
      print(f'''{name:80} {mod}'''					)
      count += 1
  print(f'''{count} TensorQuantizers found in model'''					)






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







  '''simple docstring'''







  _a							=	getattr(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase					)
  if quantizer_mod is not None:
    assert hasattr(_lowerCamelCase     , _lowerCamelCase					)
    setattr(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase					)
  else:
    logger.warning(f'''{name} has no {quantizer}'''					)






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							int     , UpperCamelCase_:							Union[str, Any]="both"     , **UpperCamelCase_:							Dict					)       ->							Any:







  '''simple docstring'''







  _a							=	f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
  for k, v in kwargs.items():
    s += f''' {k}={v}'''
    if which in ["input", "both"]:
      set_quantizer(_lowerCamelCase     , _lowerCamelCase     , "_input_quantizer"     , _lowerCamelCase     , _lowerCamelCase					)
    if which in ["weight", "both"]:
      set_quantizer(_lowerCamelCase     , _lowerCamelCase     , "_weight_quantizer"     , _lowerCamelCase     , _lowerCamelCase					)
  logger.info(_lowerCamelCase					)






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int     , **UpperCamelCase_:							List[Any]					)       ->							Optional[Any]:







  '''simple docstring'''







  for name, mod in model.named_modules():
    if hasattr(_lowerCamelCase     , "_input_quantizer"					) or hasattr(_lowerCamelCase     , "_weight_quantizer"					):
      for n in names:
        if re.search(_lowerCamelCase     , _lowerCamelCase					):
          set_quantizers(_lowerCamelCase     , _lowerCamelCase     , **_lowerCamelCase					)
    elif name.endswith("_quantizer"					):
      for n in names:
        if re.search(_lowerCamelCase     , _lowerCamelCase					):
          _a							=	f'''Warning: changing {name:{name_width}}'''
          for k, v in kwargs.items():
            s += f''' {k}={v}'''
            setattr(_lowerCamelCase     , _lowerCamelCase     , _lowerCamelCase					)
          logger.info(_lowerCamelCase					)










"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCamelCase							       =    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	{}
  with open(UpperCamelCase_     , "r"					) as file:
    for line_number, line in enumerate(UpperCamelCase_					):
      _a							=	line.strip()
      if line:
        _a							=	line.split()
        _a							=	line_number
        _a							=	words[0]
        _a							=	value
  return result






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Union[str, Any]					)       ->							Union[str, Any]:







  '''simple docstring'''







  for attribute in key.split("."					):
    _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)

  _a							=	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase_					):
      _a							=	PARAM_MAPPING[full_name.split("."					)[-1]]
      _a							=	"param"

  if weight_type is not None and weight_type != "param":
    _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					).shape
  elif weight_type is not None and weight_type == "param":
    _a							=	hf_pointer
    for attribute in hf_param_name.split("."					):
      _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)
    _a							=	shape_pointer.shape

    # let's reduce dimension
    _a							=	value[0]
  else:
    _a							=	hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        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":
    _a							=	value
  elif weight_type == "weight_g":
    _a							=	value
  elif weight_type == "weight_v":
    _a							=	value
  elif weight_type == "bias":
    _a							=	value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."					):
      _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)
    _a							=	value
  else:
    _a							=	value

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






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]					)       ->							str:







  '''simple docstring'''







  _a							=	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase_					):
      _a							=	PARAM_MAPPING[full_name.split("."					)[-1]]
      _a							=	"param"

  if weight_type is not None and weight_type != "param":
    _a							=	".".join([key, weight_type]					)
  elif weight_type is not None and weight_type == "param":
    _a							=	".".join([key, hf_param_name]					)
  else:
    _a							=	key

  _a							=	value if "lm_head" in full_key else value[0]


UpperCamelCase							       =    {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[int]=None					)       ->							Tuple:







  '''simple docstring'''







  _a							=	False
  for key, mapped_key in MAPPING.items():
    _a							=	"wav2vec2." + 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]:
      _a							=	True
      if "*" in mapped_key:
        _a							=	name.split(UpperCamelCase_					)[0].split("."					)[-2]
        _a							=	mapped_key.replace("*"     , UpperCamelCase_					)
      if "weight_g" in name:
        _a							=	"weight_g"
      elif "weight_v" in name:
        _a							=	"weight_v"
      elif "bias" in name:
        _a							=	"bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _a							=	"weight"
      else:
        _a							=	None
      if hf_dict is not None:
        rename_dict(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      else:
        set_recursively(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      return is_used
  return is_used






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[]
  _a							=	fairseq_model.state_dict()

  _a							=	hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _a							=	False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , hf_model.config.feat_extract_norm == "group"     , )
      _a							=	True
    else:
      _a							=	load_wavaveca_layer(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
    if not is_used:
      unused_weights.append(UpperCamelCase_					)

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






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







  '''simple docstring'''







  _a							=	full_name.split("conv_layers."					)[-1]
  _a							=	name.split("."					)
  _a							=	int(items[0]					)
  _a							=	int(items[1]					)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''					)
      _a							=	value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''					)
      _a							=	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:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''					)
      _a							=	value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''					)
      _a							=	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        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[Any]=True     , UpperCamelCase_:							Any=False					)       ->							Optional[Any]:







  '''simple docstring'''







  if config_path is not None:
    _a							=	WavaVecaConfig.from_pretrained(UpperCamelCase_					)
  else:
    _a							=	WavaVecaConfig()

  if is_seq_class:
    _a							=	read_txt_into_dict(UpperCamelCase_					)
    _a							=	idalabel
    _a							=	WavaVecaForSequenceClassification(UpperCamelCase_					)
    _a							=	WavaVecaFeatureExtractor(
        feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
    feature_extractor.save_pretrained(UpperCamelCase_					)

  elif is_finetuned:
    if dict_path:
      _a							=	Dictionary.load(UpperCamelCase_					)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _a							=	target_dict.pad_index
      _a							=	target_dict.bos_index
      _a							=	target_dict.eos_index
      _a							=	len(target_dict.symbols					)
      _a							=	os.path.join(UpperCamelCase_     , "vocab.json"					)
      if not os.path.isdir(UpperCamelCase_					):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCamelCase_					)					)
        return
      os.makedirs(UpperCamelCase_     , exist_ok=UpperCamelCase_					)
      _a							=	target_dict.indices

      # fairseq has the <pad> and <s> switched
      _a							=	0
      _a							=	1
      with open(UpperCamelCase_     , "w"     , encoding="utf-8"					) as vocab_handle:
        json.dump(UpperCamelCase_     , UpperCamelCase_					)
      _a							=	WavaVecaCTCTokenizer(
          UpperCamelCase_     , 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=UpperCamelCase_     , )
      _a							=	True if config.feat_extract_norm == "layer" else False
      _a							=	WavaVecaFeatureExtractor(
          feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
      _a							=	WavaVecaProcessor(feature_extractor=UpperCamelCase_     , tokenizer=UpperCamelCase_					)
      processor.save_pretrained(UpperCamelCase_					)

    _a							=	WavaVecaForCTC(UpperCamelCase_					)
  else:
    _a							=	WavaVecaForPreTraining(UpperCamelCase_					)

  if is_finetuned or is_seq_class:
    _a			,	_a			,	_a							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     , arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
  else:
    _a							=	argparse.Namespace(task="audio_pretraining"					)
    _a							=	fairseq.tasks.setup_task(UpperCamelCase_					)

    _a			,	_a			,	_a							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     , task=UpperCamelCase_					)

  _a							=	model[0].eval()

  recursively_load_weights(UpperCamelCase_     , UpperCamelCase_     , not is_finetuned					)

  hf_wavavec.save_pretrained(UpperCamelCase_					)


if __name__ == "__main__":
     UpperCamelCase							       =    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"""
     )
     parser.add_argument(
         """--is_seq_class""",
         action="""store_true""",
         help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
     )
     UpperCamelCase							       =    parser.parse_args()

     UpperCamelCase							       =    not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )


