'''simple docstring'''


import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
       import jax.numpy as jnp





class       _a    (		__a      ):


  """simple docstring"""

  def __get__(		self      :       List[Any]   ,				lowercase_      :       List[Any]   ,				lowercase_      :       Optional[Any]=None      ):







    '''simple docstring'''


    if obj is None:
      return self
    if self.fget is None:
      raise AttributeError("""unreadable attribute"""      )
    lowercase_     =       """__cached_""" + self.fget.__name__
    lowercase_     =       getattr(lowercase_   ,				lowercase_   ,				lowercase_      )
    if cached is None:
      lowercase_     =       self.fget(lowercase_      )
      setattr(lowercase_   ,				lowercase_   ,				lowercase_      )
    return cached






def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       val.lower()
  if val in {"y", "yes", "t", "true", "on", "1"}:
    return 1
  if val in {"n", "no", "f", "false", "off", "0"}:
    return 0
  raise ValueError(f"""invalid truth value {val!r}"""						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  if is_torch_fx_proxy(SCREAMING_SNAKE_CASE_						):
    return True
  if is_torch_available():
    import torch

    if isinstance(SCREAMING_SNAKE_CASE_ ,	torch.Tensor						):
      return True
  if is_tf_available():
    import tensorflow as tf

    if isinstance(SCREAMING_SNAKE_CASE_ ,	tf.Tensor						):
      return True

  if is_flax_available():
    import jax.numpy as jnp
    from jax.core import Tracer

    if isinstance(SCREAMING_SNAKE_CASE_ ,	(jnp.ndarray, Tracer)						):
      return True

  return isinstance(SCREAMING_SNAKE_CASE_ ,	np.ndarray						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  return isinstance(SCREAMING_SNAKE_CASE_ ,	np.ndarray						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  return _is_numpy(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  import torch

  return isinstance(SCREAMING_SNAKE_CASE_ ,	torch.Tensor						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->str:
  return False if not is_torch_available() else _is_torch(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[str]:
  import torch

  return isinstance(SCREAMING_SNAKE_CASE_ ,	torch.device						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  return False if not is_torch_available() else _is_torch_device(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[str]:
  import torch

  if isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    if hasattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
      lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return False
  return isinstance(SCREAMING_SNAKE_CASE_ ,	torch.dtype						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  return False if not is_torch_available() else _is_torch_dtype(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Tuple:
  import tensorflow as tf

  return isinstance(SCREAMING_SNAKE_CASE_ ,	tf.Tensor						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  return False if not is_tf_available() else _is_tensorflow(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  import tensorflow as tf

  # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
  if hasattr(SCREAMING_SNAKE_CASE_ ,	"""is_symbolic_tensor"""						):
    return tf.is_symbolic_tensor(SCREAMING_SNAKE_CASE_						)
  return type(SCREAMING_SNAKE_CASE_						) == tf.Tensor

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Tuple:
  return False if not is_tf_available() else _is_tf_symbolic_tensor(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  import jax.numpy as jnp  # noqa: F811

  return isinstance(SCREAMING_SNAKE_CASE_ ,	jnp.ndarray						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  return False if not is_flax_available() else _is_jax(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  if isinstance(SCREAMING_SNAKE_CASE_ ,	(dict, UserDict)						):
    return {k: to_py_obj(SCREAMING_SNAKE_CASE_						) for k, v in obj.items()}
  elif isinstance(SCREAMING_SNAKE_CASE_ ,	(list, tuple)						):
    return [to_py_obj(SCREAMING_SNAKE_CASE_						) for o in obj]
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    return obj.numpy().tolist()
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return obj.detach().cpu().tolist()
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return np.asarray(SCREAMING_SNAKE_CASE_						).tolist()
  elif isinstance(SCREAMING_SNAKE_CASE_ ,	(np.ndarray, np.number)						):  # tolist also works on 0d np arrays
    return obj.tolist()
  else:
    return obj

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  if isinstance(SCREAMING_SNAKE_CASE_ ,	(dict, UserDict)						):
    return {k: to_numpy(SCREAMING_SNAKE_CASE_						) for k, v in obj.items()}
  elif isinstance(SCREAMING_SNAKE_CASE_ ,	(list, tuple)						):
    return np.array(SCREAMING_SNAKE_CASE_						)
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    return obj.numpy()
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return obj.detach().cpu().numpy()
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return np.asarray(SCREAMING_SNAKE_CASE_						)
  else:
    return obj





class       _a    (		__a      ):


  """simple docstring"""

  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       fields(self      )

    # Safety and consistency checks
    if not len(lowercase_      ):
      raise ValueError(F"""{self.__class__.__name__} has no fields."""      )
    if not all(field.default is None for field in class_fields[1:]      ):
      raise ValueError(F"""{self.__class__.__name__} should not have more than one required field."""      )

    lowercase_     =       getattr(self   ,				class_fields[0].name      )
    lowercase_     =       all(getattr(self   ,				field.name      ) is None for field in class_fields[1:]      )

    if other_fields_are_none and not is_tensor(lowercase_      ):
      if isinstance(lowercase_   ,				lowercase_      ):
        lowercase_     =       first_field.items()
        lowercase_     =       True
      else:
        try:
          lowercase_     =       iter(lowercase_      )
          lowercase_     =       True
        except TypeError:
          lowercase_     =       False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
      if first_field_iterator:
        for idx, element in enumerate(lowercase_      ):
          if (
              not isinstance(lowercase_   ,				(list, tuple)      )
              or not len(lowercase_      ) == 2
              or not isinstance(element[0]   ,				lowercase_      )
          ):
            if idx == 0:
              # If we do not have an iterator of key/values, set it as attribute
              lowercase_     =       first_field
            else:
              # If we have a mixed iterator, raise an error
              raise ValueError(
                  F"""Cannot set key/value for {element}. It needs to be a tuple (key, value)."""      )
            break
          setattr(self   ,				element[0]   ,				element[1]      )
          if element[1] is not None:
            lowercase_     =       element[1]
      elif first_field is not None:
        lowercase_     =       first_field
    else:
      for field in class_fields:
        lowercase_     =       getattr(self   ,				field.name      )
        if v is not None:
          lowercase_     =       v




  def __delitem__(		self      :       Any   ,				*lowercase_      :       Dict   ,				**lowercase_      :       List[str]      ):







    '''simple docstring'''


    raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""      )




  def 		lowerCamelCase__  (		self      :       int   ,				*lowercase_      :       Any   ,				**lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				*lowercase_      :       Union[str, Any]   ,				**lowercase_      :       Tuple      ):







    '''simple docstring'''


    raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				*lowercase_      :       List[str]   ,				**lowercase_      :       Tuple      ):







    '''simple docstring'''


    raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""      )




  def __getitem__(		self      :       Union[str, Any]   ,				lowercase_      :       Dict      ):







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       dict(self.items()      )
      return inner_dict[k]
    else:
      return self.to_tuple()[k]




  def __setattr__(		self      :       List[Any]   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    if name in self.keys() and value is not None:
      # Don't call self.__setitem__ to avoid recursion errors
      super().__setitem__(lowercase_   ,				lowercase_      )
    super().__setattr__(lowercase_   ,				lowercase_      )




  def __setitem__(		self      :       str   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


    super().__setitem__(lowercase_   ,				lowercase_      )
    # Don't call self.__setattr__ to avoid recursion errors
    super().__setattr__(lowercase_   ,				lowercase_      )





  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return tuple(self[k] for k in self.keys()      )






class       _a    (		__a ,				__a      ):


  """simple docstring"""

  @classmethod
  def 		lowerCamelCase__  (		cls      :       Any   ,				lowercase_      :       Dict      ):







    '''simple docstring'''


    raise ValueError(
        F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()      )}"""      )






class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''longest'''
  A_    							=						'''max_length'''
  A_    							=						'''do_not_pad'''






class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''pt'''
  A_    							=						'''tf'''
  A_    							=						'''np'''
  A_    							=						'''jax'''






class       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       List[ContextManager]      ):







    '''simple docstring'''


    lowercase_     =       context_managers
    lowercase_     =       ExitStack()




  def __enter__(		self      :       Dict      ):







    '''simple docstring'''


    for context_manager in self.context_managers:
      self.stack.enter_context(lowercase_      )





  def __exit__(		self      :       List[str]   ,				*lowercase_      :       Optional[int]   ,				**lowercase_      :       Dict      ):







    '''simple docstring'''


    self.stack.__exit__(*lowercase_   ,				**lowercase_      )






def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       infer_framework(SCREAMING_SNAKE_CASE_						)
  if framework == "tf":
    lowercase_     =       inspect.signature(model_class.call						)  # TensorFlow models
  elif framework == "pt":
    lowercase_     =       inspect.signature(model_class.forward						)  # PyTorch models
  else:
    lowercase_     =       inspect.signature(model_class.__call__						)  # Flax models

  for p in signature.parameters:
    if p == "return_loss" and signature.parameters[p].default is True:
      return True

  return False

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       model_class.__name__
  lowercase_     =       infer_framework(SCREAMING_SNAKE_CASE_						)
  if framework == "tf":
    lowercase_     =       inspect.signature(model_class.call						)  # TensorFlow models
  elif framework == "pt":
    lowercase_     =       inspect.signature(model_class.forward						)  # PyTorch models
  else:
    lowercase_     =       inspect.signature(model_class.__call__						)  # Flax models

  if "QuestionAnswering" in model_name:
    return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
  else:
    return [p for p in signature.parameters if "label" in p]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = "" ,	SCREAMING_SNAKE_CASE_ = "."						)			->Tuple:

  def _flatten_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_="" ,	SCREAMING_SNAKE_CASE_="."						):
    for k, v in d.items():
      lowercase_     =       str(SCREAMING_SNAKE_CASE_						) + delimiter + str(SCREAMING_SNAKE_CASE_						) if parent_key else k
      if v and isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
        yield from flatten_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	delimiter=SCREAMING_SNAKE_CASE_						).items()
      else:
        yield key, v

  return dict(_flatten_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)						)

@contextmanager
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = False						)			->Any:
  if use_temp_dir:
    with tempfile.TemporaryDirectory() as tmp_dir:
      yield tmp_dir
  else:
    yield working_dir

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None						)			->Dict:
  if is_numpy_array(SCREAMING_SNAKE_CASE_						):
    return np.transpose(SCREAMING_SNAKE_CASE_ ,	axes=SCREAMING_SNAKE_CASE_						)
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return array.T if axes is None else array.permute(*SCREAMING_SNAKE_CASE_						)
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    import tensorflow as tf

    return tf.transpose(SCREAMING_SNAKE_CASE_ ,	perm=SCREAMING_SNAKE_CASE_						)
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return jnp.transpose(SCREAMING_SNAKE_CASE_ ,	axes=SCREAMING_SNAKE_CASE_						)
  else:
    raise ValueError(f"""Type not supported for transpose: {type(SCREAMING_SNAKE_CASE_						)}."""						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[Any]:
  if is_numpy_array(SCREAMING_SNAKE_CASE_						):
    return np.reshape(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return array.reshape(*SCREAMING_SNAKE_CASE_						)
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    import tensorflow as tf

    return tf.reshape(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return jnp.reshape(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    raise ValueError(f"""Type not supported for reshape: {type(SCREAMING_SNAKE_CASE_						)}."""						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  if is_numpy_array(SCREAMING_SNAKE_CASE_						):
    return np.squeeze(SCREAMING_SNAKE_CASE_ ,	axis=SCREAMING_SNAKE_CASE_						)
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return array.squeeze() if axis is None else array.squeeze(dim=SCREAMING_SNAKE_CASE_						)
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    import tensorflow as tf

    return tf.squeeze(SCREAMING_SNAKE_CASE_ ,	axis=SCREAMING_SNAKE_CASE_						)
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return jnp.squeeze(SCREAMING_SNAKE_CASE_ ,	axis=SCREAMING_SNAKE_CASE_						)
  else:
    raise ValueError(f"""Type not supported for squeeze: {type(SCREAMING_SNAKE_CASE_						)}."""						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  if is_numpy_array(SCREAMING_SNAKE_CASE_						):
    return np.expand_dims(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return array.unsqueeze(dim=SCREAMING_SNAKE_CASE_						)
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    import tensorflow as tf

    return tf.expand_dims(SCREAMING_SNAKE_CASE_ ,	axis=SCREAMING_SNAKE_CASE_						)
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return jnp.expand_dims(SCREAMING_SNAKE_CASE_ ,	axis=SCREAMING_SNAKE_CASE_						)
  else:
    raise ValueError(f"""Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE_						)}."""						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->str:
  if is_numpy_array(SCREAMING_SNAKE_CASE_						):
    return np.size(SCREAMING_SNAKE_CASE_						)
  elif is_torch_tensor(SCREAMING_SNAKE_CASE_						):
    return array.numel()
  elif is_tf_tensor(SCREAMING_SNAKE_CASE_						):
    import tensorflow as tf

    return tf.size(SCREAMING_SNAKE_CASE_						)
  elif is_jax_tensor(SCREAMING_SNAKE_CASE_						):
    return array.size
  else:
    raise ValueError(f"""Type not supported for expand_dims: {type(SCREAMING_SNAKE_CASE_						)}."""						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  for key, value in auto_map.items():
    if isinstance(SCREAMING_SNAKE_CASE_ ,	(tuple, list)						):
      lowercase_     =       [f"""{repo_id}--{v}""" if (v is not None and """--""" not in v) else v for v in value]
    elif value is not None and "--" not in value:
      lowercase_     =       f"""{repo_id}--{value}"""

  return auto_map




def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  for base_class in inspect.getmro(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       base_class.__module__
    lowercase_     =       base_class.__name__
    if module.startswith("""tensorflow"""						) or module.startswith("""keras"""						) or name == "TFPreTrainedModel":
      return "tf"
    elif module.startswith("""torch"""						) or name == "PreTrainedModel":
      return "pt"
    elif module.startswith("""flax"""						) or module.startswith("""jax"""						) or name == "FlaxPreTrainedModel":
      return "flax"
  else:
    raise TypeError(f"""Could not infer framework from class {model_class}."""						)


'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__snake_case						  =		{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  for attribute in key.split("""."""						):
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

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

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       []
  lowercase_     =       fairseq_model.state_dict()

  lowercase_     =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    lowercase_     =       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""" ,	)
      lowercase_     =       True
    else:
      for key, mapped_key in MAPPING.items():
        lowercase_     =       """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

        if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
          lowercase_     =       True
          if "*" in mapped_key:
            lowercase_     =       name.split(SCREAMING_SNAKE_CASE_						)[0].split("""."""						)[-2]
            lowercase_     =       mapped_key.replace("""*""" ,	SCREAMING_SNAKE_CASE_						)
          if "weight_g" in name:
            lowercase_     =       """weight_g"""
          elif "weight_v" in name:
            lowercase_     =       """weight_v"""
          elif "weight" in name:
            lowercase_     =       """weight"""
          elif "bias" in name:
            lowercase_     =       """bias"""
          else:
            lowercase_     =       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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       full_name.split("""conv_layers."""						)[-1]
  lowercase_     =       name.split("""."""						)
  lowercase_     =       int(items[0]						)
  lowercase_     =       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."""
      )
      lowercase_     =       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."""
      )
      lowercase_     =       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."
      )
      lowercase_     =       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."""
      )
      lowercase_     =       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_						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       SEWConfig()
  if is_finetuned:
    lowercase_     =       model.wav_encoder.wav_model.cfg
  else:
    lowercase_     =       model.cfg

  lowercase_     =       fs_config.conv_bias
  lowercase_     =       eval(fs_config.conv_feature_layers						)
  lowercase_     =       [x[0] for x in conv_layers]
  lowercase_     =       [x[1] for x in conv_layers]
  lowercase_     =       [x[2] for x in conv_layers]
  lowercase_     =       """gelu"""
  lowercase_     =       """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  lowercase_     =       0.0
  lowercase_     =       fs_config.activation_fn.name
  lowercase_     =       fs_config.encoder_embed_dim
  lowercase_     =       0.02
  lowercase_     =       fs_config.encoder_ffn_embed_dim
  lowercase_     =       1e-5
  lowercase_     =       fs_config.encoder_layerdrop
  lowercase_     =       fs_config.encoder_attention_heads
  lowercase_     =       fs_config.conv_pos_groups
  lowercase_     =       fs_config.conv_pos
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       fs_config.encoder_layers
  lowercase_     =       fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    lowercase_     =       model.cfg
    lowercase_     =       fs_config.final_dropout
    lowercase_     =       fs_config.layerdrop
  lowercase_     =       fs_config.activation_dropout
  lowercase_     =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  lowercase_     =       fs_config.attention_dropout
  lowercase_     =       fs_config.dropout_input
  lowercase_     =       fs_config.dropout
  lowercase_     =       fs_config.mask_channel_length
  lowercase_     =       fs_config.mask_channel_prob
  lowercase_     =       fs_config.mask_length
  lowercase_     =       fs_config.mask_prob

  lowercase_     =       """Wav2Vec2FeatureExtractor"""
  lowercase_     =       """Wav2Vec2CTCTokenizer"""

  return config

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=True						)			->Optional[Any]:

  if is_finetuned:
    lowercase_     ,	lowercase_     ,	lowercase_     =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path] ,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
  else:
    lowercase_     ,	lowercase_     ,	lowercase_     =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

  if config_path is not None:
    lowercase_     =       SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       convert_config(model[0] ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     =       model[0].eval()

  lowercase_     =       True if config.feat_extract_norm == """layer""" else False
  lowercase_     =       WavaVecaFeatureExtractor(
      feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0 ,	do_normalize=SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_ ,	)

  if is_finetuned:
    if dict_path:
      lowercase_     =       Dictionary.load(SCREAMING_SNAKE_CASE_						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowercase_     =       target_dict.pad_index
      lowercase_     =       target_dict.bos_index
      lowercase_     =       target_dict.pad_index
      lowercase_     =       target_dict.bos_index
      lowercase_     =       target_dict.eos_index
      lowercase_     =       len(target_dict.symbols						)
      lowercase_     =       os.path.join(SCREAMING_SNAKE_CASE_ ,	"""vocab.json"""						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE_						):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE_						)						)
        return
      os.makedirs(SCREAMING_SNAKE_CASE_ ,	exist_ok=SCREAMING_SNAKE_CASE_						)
      with open(SCREAMING_SNAKE_CASE_ ,	"""w""" ,	encoding="""utf-8"""						) as vocab_handle:
        json.dump(target_dict.indices ,	SCREAMING_SNAKE_CASE_						)
      lowercase_     =       WavaVecaCTCTokenizer(
          SCREAMING_SNAKE_CASE_ ,	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=SCREAMING_SNAKE_CASE_ ,	)
      lowercase_     =       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ ,	tokenizer=SCREAMING_SNAKE_CASE_						)
      processor.save_pretrained(SCREAMING_SNAKE_CASE_						)

    lowercase_     =       SEWForCTC(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       SEWModel(SCREAMING_SNAKE_CASE_						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)

  recursively_load_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


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

