



import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__    =	logging.get_logger(__name__)

a__    =	"""▁"""

a__    =	{"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

a__    =	{
    """sentencepiece_model_file""": """sentencepiece.bpe.model""",
    """vocab_file""": """vocab.txt""",
}

a__    =	{
    """vocab_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
    },
    """sentencepiece_model_file""": {
        """ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
        """ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
    },
}

a__    =	{
    """ernie-m-base""": 5_14,
    """ernie-m-large""": 5_14,
}

a__    =	{
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["input_ids"]

   snake_case_					: Optional[int]          =		VOCAB_FILES_NAMES
   snake_case_					: Dict          =		PRETRAINED_INIT_CONFIGURATION
   snake_case_					: int          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[str]          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: int          =		RESOURCE_FILES_NAMES

   def __init__(					self							:			str				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			List[Any]=None				,		lowerCAmelCase							:			Union[str, Any]=False				,		lowerCAmelCase							:			str="utf8"				,		lowerCAmelCase							:			Optional[int]="[UNK]"				,		lowerCAmelCase							:			Dict="[SEP]"				,		lowerCAmelCase							:			str="[PAD]"				,		lowerCAmelCase							:			Union[str, Any]="[CLS]"				,		lowerCAmelCase							:			Dict="[MASK]"				,		lowerCAmelCase							:			Optional[Dict[str, Any]] = None				,		**lowerCAmelCase							:			Optional[int]				,		)				->				None:

    """simple docstring"""







    _snake_case    :  Tuple					=					{} if sp_model_kwargs is None else sp_model_kwargs
    super().__init__(
        do_lower_case=lowerCAmelCase				,		unk_token=lowerCAmelCase				,		sep_token=lowerCAmelCase				,		pad_token=lowerCAmelCase				,		cls_token=lowerCAmelCase				,		mask_token=lowerCAmelCase				,		vocab_file=lowerCAmelCase				,		encoding=lowerCAmelCase				,		sp_model_kwargs=self.sp_model_kwargs				,		**lowerCAmelCase				,		)
    _snake_case    :  Any					=					do_lower_case
    _snake_case    :  int					=					sentencepiece_model_ckpt
    _snake_case    :  Optional[Any]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.Load(lowerCAmelCase)

    # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
    if vocab_file is not None:
     _snake_case    :  Dict					=					self.load_vocab(filepath=lowerCAmelCase)
    else:
     _snake_case    :  Tuple					=					{self.sp_model.id_to_piece(lowerCAmelCase): id for id in range(self.sp_model.get_piece_size())}
    _snake_case    :  str					=					{v: k for k, v in self.vocab.items()}

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

    """simple docstring"""







    if text is None:
     return None

    _snake_case    :  List[Any]					=					self.tokenize(lowerCAmelCase)
    _snake_case	,    _snake_case    :  str					=					"""""", []

    for i, ch in enumerate(lowerCAmelCase):
     if ch in self.SP_CHAR_MAPPING:
      _snake_case    :  List[Any]					=					self.SP_CHAR_MAPPING.get(lowerCAmelCase)
     else:
      _snake_case    :  List[str]					=					unicodedata.normalize("""NFKC"""				,		lowerCAmelCase)
     if self.is_whitespace(lowerCAmelCase):
      continue
     normalized_text += ch
     char_mapping.extend([i] * len(lowerCAmelCase))

    _snake_case	,    _snake_case	,    _snake_case    :  List[str]					=					normalized_text, [], 0

    if self.do_lower_case:
     _snake_case    :  Union[str, Any]					=					text.lower()

    for token in split_tokens:
     if token[:1] == "▁":
      _snake_case    :  Dict					=					token[1:]
     _snake_case    :  List[str]					=					text[offset:].index(lowerCAmelCase) + offset
     _snake_case    :  Optional[Any]					=					start + len(lowerCAmelCase)

     token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
     _snake_case    :  Optional[Any]					=					end
    return token_mapping

   @property
   def      UpperCamelCase_  (					self							:			List[Any])				->				Tuple:

    """simple docstring"""







    return len(self.vocab)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Optional[Any]:

    """simple docstring"""







    return dict(self.vocab				,		**self.added_tokens_encoder)

   def __getstate__(					self							:			Optional[int])				->				Dict:

    """simple docstring"""







    _snake_case    :  Tuple					=					self.__dict__.copy()
    _snake_case    :  List[Any]					=					None
    return state

   def __setstate__(					self							:			str				,		lowerCAmelCase							:			List[str])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					d

    # for backward compatibility
    if not hasattr(self				,		"""sp_model_kwargs"""):
     _snake_case    :  Union[str, Any]					=					{}

    _snake_case    :  Tuple					=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.Load(self.sentencepiece_model_ckpt)

   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			Tuple)				->				Optional[int]:

    """simple docstring"""







    return "".join((self.SP_CHAR_MAPPING.get(lowerCAmelCase				,		lowerCAmelCase) for c in text))

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			int=False				,		lowerCAmelCase							:			Optional[Any]=64				,		lowerCAmelCase							:			str=0.1)				->				Optional[int]:

    """simple docstring"""







    if self.sp_model_kwargs.get("""enable_sampling""") is True:
     _snake_case    :  Tuple					=					True
    if self.sp_model_kwargs.get("""alpha""") is not None:
     _snake_case    :  List[str]					=					self.sp_model_kwargs.get("""alpha""")
    if self.sp_model_kwargs.get("""nbest_size""") is not None:
     _snake_case    :  List[str]					=					self.sp_model_kwargs.get("""nbest_size""")

    if not enable_sampling:
     _snake_case    :  List[str]					=					self.sp_model.EncodeAsPieces(lowerCAmelCase)
    else:
     _snake_case    :  List[Any]					=					self.sp_model.SampleEncodeAsPieces(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Dict					=					[]
    for pi, piece in enumerate(lowerCAmelCase):
     if piece == SPIECE_UNDERLINE:
      if not pieces[pi + 1].startswith(lowerCAmelCase) and pi != 0:
       new_pieces.append(lowerCAmelCase)
       continue
      else:
       continue
     _snake_case    :  Optional[Any]					=					0
     for i, chunk in enumerate(lowerCAmelCase):
      if chunk == SPIECE_UNDERLINE:
       continue
      if self.is_ch_char(lowerCAmelCase) or self.is_punct(lowerCAmelCase):
       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
        new_pieces.append(piece[lst_i:i])
       new_pieces.append(lowerCAmelCase)
       _snake_case    :  Tuple					=					i + 1
      elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
        new_pieces.append(piece[lst_i:i])
       _snake_case    :  List[Any]					=					i
      elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
        new_pieces.append(piece[lst_i:i])
       _snake_case    :  List[str]					=					i
     if len(lowerCAmelCase) > lst_i:
      new_pieces.append(piece[lst_i:])
    return new_pieces

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

    """simple docstring"""







    _snake_case    :  Dict					=					"""""".join(lowerCAmelCase).replace(lowerCAmelCase				,		""" """).strip()
    return out_string

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

    """simple docstring"""







    _snake_case    :  List[str]					=					self.convert_ids_to_tokens(lowerCAmelCase)
    _snake_case    :  List[Any]					=					"""""".join(lowerCAmelCase).replace(lowerCAmelCase				,		""" """).strip()
    return out_string

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

    """simple docstring"""







    return self.vocab.get(lowerCAmelCase				,		self.vocab.get(self.unk_token))

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

    """simple docstring"""







    return self.reverse_vocab.get(lowerCAmelCase				,		self.unk_token)

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

    """simple docstring"""







    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _snake_case    :  Union[str, Any]					=					[self.cls_token_id]
    _snake_case    :  List[Any]					=					[self.sep_token_id]
    return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep

   def      UpperCamelCase_  (					self							:			Dict				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Dict=None)				->				Dict:

    """simple docstring"""







    if offset_mapping_a is None:
     return [(0, 0)] + offset_mapping_a + [(0, 0)]

    return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]

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

    """simple docstring"""







    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 not None:
     return [1] + ([0] * len(lowerCAmelCase)) + [1, 1] + ([0] * len(lowerCAmelCase)) + [1]
    return [1] + ([0] * len(lowerCAmelCase)) + [1]

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    if token_ids_a is None:
     # [CLS] X [SEP]
     return (len(lowerCAmelCase) + 2) * [0]

    # [CLS] A [SEP] [SEP] B [SEP]
    return [0] * (len(lowerCAmelCase) + 1) + [1] * (len(lowerCAmelCase) + 3)

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

    """simple docstring"""







    if "\u4e00" <= char <= "\u9fff":
     return True
    return False

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

    """simple docstring"""







    if ("a" <= char <= "z") or ("A" <= char <= "Z"):
     return True
    return False

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    if char in ",;:.?!~，；：。？！《》【】":
     return True
    return False

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

    """simple docstring"""







    if char == " " or char == "\t" or char == "\n" or char == "\r":
     return True
    if len(lowerCAmelCase) == 1:
     _snake_case    :  List[Any]					=					unicodedata.category(lowerCAmelCase)
     if cat == "Zs":
      return True
    return False

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

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					{}
    with io.open(lowerCAmelCase				,		"""r"""				,		encoding="""utf-8""") as f:
     for index, line in enumerate(lowerCAmelCase):
      _snake_case    :  int					=					line.rstrip("""\n""")
      _snake_case    :  str					=					int(lowerCAmelCase)

    return token_to_idx


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

    """simple docstring"""







    _snake_case    :  Any					=					0
    if os.path.isdir(lowerCAmelCase):
     _snake_case    :  List[str]					=					os.path.join(
         lowerCAmelCase				,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
    else:
     _snake_case    :  List[str]					=					(filename_prefix + """-""" if filename_prefix else """""") + save_directory
    with open(lowerCAmelCase				,		"""w"""				,		encoding="""utf-8""") as writer:
     for token, token_index in sorted(self.vocab.items()				,		key=lambda lowerCAmelCase: kv[1]):
      if index != token_index:
       logger.warning(
           F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
           """ Please check that the vocabulary is not corrupted!""")
       _snake_case    :  Optional[Any]					=					token_index
      writer.write(token + """\n""")
      index += 1

    _snake_case    :  Any					=					os.path.join(lowerCAmelCase				,		"""sentencepiece.bpe.model""")
    with open(lowerCAmelCase				,		"""wb""") as fi:
     _snake_case    :  Optional[Any]					=					self.sp_model.serialized_model_proto()
     fi.write(lowerCAmelCase)

    return (vocab_file,)





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()
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_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""",
}
a__    =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Any:
 _snake_case    :  Union[str, Any]					=					{}
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				) as file:
  for line_number, line in enumerate(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					line.strip()
   if line:
    _snake_case    :  List[str]					=					line.split()
    _snake_case    :  List[Any]					=					line_number
    _snake_case    :  int					=					words[0]
    _snake_case    :  str					=					value
 return result



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     Tuple:
 for attribute in key.split("""."""				):
  _snake_case    :  List[str]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  List[Any]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Any					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				).shape
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  Dict					=					hf_pointer
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Tuple					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Any					=					shape_pointer.shape

  # let's reduce dimension
  _snake_case    :  str					=					value[0]
 else:
  _snake_case    :  List[str]					=					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":
  _snake_case    :  Dict					=					value
 elif weight_type == "weight_g":
  _snake_case    :  Union[str, Any]					=					value
 elif weight_type == "weight_v":
  _snake_case    :  str					=					value
 elif weight_type == "bias":
  _snake_case    :  Dict					=					value
 elif weight_type == "param":
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Union[str, Any]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  int					=					value
 else:
  _snake_case    :  Dict					=					value

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     str:
 _snake_case    :  List[str]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  List[str]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Optional[Any]					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					""".""".join([key, weight_type]				)
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  List[str]					=					""".""".join([key, hf_param_name]				)
 else:
  _snake_case    :  Optional[int]					=					key

 _snake_case    :  Any					=					value if """lm_head""" in full_key else value[0]


a__    =	{
    """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       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int=None				,       SCREAMING_SNAKE_CASE__       :	Any=None				)  ->     Optional[Any]:
 _snake_case    :  Any					=					False
 for key, mapped_key in MAPPING.items():
  _snake_case    :  List[str]					=					"""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]:
   _snake_case    :  Optional[int]					=					True
   if "*" in mapped_key:
    _snake_case    :  List[str]					=					name.split(SCREAMING_SNAKE_CASE__				)[0].split("""."""				)[-2]
    _snake_case    :  List[Any]					=					mapped_key.replace("""*"""				,       SCREAMING_SNAKE_CASE__				)
   if "weight_g" in name:
    _snake_case    :  Tuple					=					"""weight_g"""
   elif "weight_v" in name:
    _snake_case    :  Dict					=					"""weight_v"""
   elif "bias" in name:
    _snake_case    :  Optional[Any]					=					"""bias"""
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    _snake_case    :  List[Any]					=					"""weight"""
   else:
    _snake_case    :  Dict					=					None
   if hf_dict is not None:
    rename_dict(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   else:
    set_recursively(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   return is_used
 return is_used



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Tuple:
 _snake_case    :  Optional[Any]					=					[]
 _snake_case    :  Tuple					=					fairseq_model.state_dict()

 _snake_case    :  List[str]					=					hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  _snake_case    :  Tuple					=					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"""				,       )
   _snake_case    :  List[str]					=					True
  else:
   _snake_case    :  Dict					=					load_wavaveca_layer(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE__				)

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     str:
 _snake_case    :  List[str]					=					full_name.split("""conv_layers."""				)[-1]
 _snake_case    :  Optional[int]					=					name.split("""."""				)
 _snake_case    :  Optional[Any]					=					int(items[0]				)
 _snake_case    :  Tuple					=					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.'''				)
   _snake_case    :  List[str]					=					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.'''				)
   _snake_case    :  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:
   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.'''				)
   _snake_case    :  Dict					=					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.'''				)
   _snake_case    :  str					=					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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]=None				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=None				,       SCREAMING_SNAKE_CASE__       :	int=True				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=False				)  ->     Optional[Any]:
 if config_path is not None:
  _snake_case    :  List[Any]					=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  Union[str, Any]					=					WavaVecaConfig()

 if is_seq_class:
  _snake_case    :  Union[str, Any]					=					read_txt_into_dict(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  str					=					idalabel
  _snake_case    :  Union[str, Any]					=					WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[str]					=					WavaVecaFeatureExtractor(
      feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
  feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__				)

 elif is_finetuned:
  if dict_path:
   _snake_case    :  Optional[Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)

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

   # fairseq has the <pad> and <s> switched
   _snake_case    :  Optional[int]					=					0
   _snake_case    :  Tuple					=					1
   with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as vocab_handle:
    json.dump(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Dict					=					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__				,       )
   _snake_case    :  Any					=					True if config.feat_extract_norm == """layer""" else False
   _snake_case    :  Optional[int]					=					WavaVecaFeatureExtractor(
       feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  List[Any]					=					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				,       tokenizer=SCREAMING_SNAKE_CASE__				)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

  _snake_case    :  Tuple					=					WavaVecaForCTC(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  List[str]					=					WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__				)

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

  _snake_case	,    _snake_case	,    _snake_case    :  str					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,       task=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					model[0].eval()

 recursively_load_weights(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       not is_finetuned				)

 hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__				)


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("""--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""",
  )
  a__    =	parser.parse_args()

  a__    =	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,
  )
