



import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
a__    =	pytest.mark.integration


a__    =	{"""comet"""}
a__    =	importlib.util.find_spec("""fairseq""") is not None

a__    =	{"""code_eval"""}
a__    =	os.name == """nt"""

a__    =	{"""bertscore""", """frugalscore""", """perplexity"""}
a__    =	importlib.util.find_spec("""transformers""") is not None



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				)  ->     int:
 @wraps(SCREAMING_SNAKE_CASE__				)
 def wrapper(self       :	Any				,       SCREAMING_SNAKE_CASE__       :	int				):
  if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
   self.skipTest("""\"test requires Fairseq\""""				)
  else:
   test_case(self				,       SCREAMING_SNAKE_CASE__				)

 return wrapper



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     int:
 @wraps(SCREAMING_SNAKE_CASE__				)
 def wrapper(self       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
   self.skipTest("""\"test requires transformers\""""				)
  else:
   test_case(self				,       SCREAMING_SNAKE_CASE__				)

 return wrapper



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     List[Any]:
 @wraps(SCREAMING_SNAKE_CASE__				)
 def wrapper(self       :	str				,       SCREAMING_SNAKE_CASE__       :	str				):
  if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
   self.skipTest("""\"test not supported on Windows\""""				)
  else:
   test_case(self				,       SCREAMING_SNAKE_CASE__				)

 return wrapper



def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Optional[Any]					=					[metric_dir.split(os.sep				)[-2] for metric_dir in glob.glob("""./metrics/*/"""				)]
 return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished







@parameterized.named_parameters(get_local_metric_names()  )
@for_all_test_methods(
    SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_  )
@local
class 		snake_case						(	parameterized.TestCase  ):



   '''simple docstring'''






   snake_case_					: Dict          =		{}
   snake_case_					: Union[str, Any]          =		None

   @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""")
   @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""")
   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			str)				->				Union[str, Any]:

    """simple docstring"""







    _snake_case    :  int					=					"""[...]"""
    _snake_case    :  Optional[Any]					=					importlib.import_module(
        datasets.load.metric_module_factory(os.path.join("""metrics"""				,		lowerCAmelCase)).module_path)
    _snake_case    :  str					=					datasets.load.import_main_class(metric_module.__name__				,		dataset=lowerCAmelCase)
    # check parameters
    _snake_case    :  List[Any]					=					inspect.signature(metric._compute).parameters
    self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()))  # no **kwargs
    # run doctest
    with self.patch_intensive_calls(lowerCAmelCase				,		metric_module.__name__):
     with self.use_local_metrics():
      try:
       _snake_case    :  str					=					doctest.testmod(lowerCAmelCase				,		verbose=lowerCAmelCase				,		raise_on_error=lowerCAmelCase)
      except doctest.UnexpectedException as e:
       raise e.exc_info[1]  # raise the exception that doctest caught
    self.assertEqual(results.failed				,		0)
    self.assertGreater(results.attempted				,		1)

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

    """simple docstring"""







    _snake_case    :  List[str]					=					"""[...]"""
    _snake_case    :  int					=					importlib.import_module(
        datasets.load.metric_module_factory(os.path.join("""metrics"""				,		lowerCAmelCase)).module_path)
    # run doctest
    with self.use_local_metrics():
     _snake_case    :  List[str]					=					doctest.testmod(lowerCAmelCase				,		verbose=lowerCAmelCase				,		raise_on_error=lowerCAmelCase)
    self.assertEqual(results.failed				,		0)
    self.assertGreater(results.attempted				,		1)

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

    """simple docstring"""







    if metric_name in self.INTENSIVE_CALLS_PATCHER:
     with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase):
      yield
    else:
     yield

   @contextmanager
   def      UpperCamelCase_  (					self							:			List[Any])				->				Optional[int]:

    """simple docstring"""







    def load_local_metric(lowerCAmelCase							:			List[str]				,		*lowerCAmelCase							:			str				,		**lowerCAmelCase							:			List[Any]):
     return load_metric(os.path.join("""metrics"""				,		lowerCAmelCase)				,		*lowerCAmelCase				,		**lowerCAmelCase)

    with patch("""datasets.load_metric""") as mock_load_metric:
     _snake_case    :  List[Any]					=					load_local_metric
     yield


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

    """simple docstring"""







    def wrapper(lowerCAmelCase							:			Optional[int]):
     _snake_case    :  List[str]					=					contextmanager(lowerCAmelCase)
     _snake_case    :  int					=					patcher
     return patcher

    return wrapper



@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""				)
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     List[Any]:
 import tensorflow.compat.va as tf
 from bleurt.score import Predictor

 tf.flags.DEFINE_string("""sv"""				,       """"""				,       """"""				)  # handle pytest cli flags







 class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



    '''simple docstring'''






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

     """simple docstring"""







     assert len(input_dict["""input_ids"""]) == 2
     return np.array([1.03, 1.04])

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
 with patch("""bleurt.score._create_predictor"""				) as mock_create_predictor:
  _snake_case    :  Dict					=					MockedPredictor()
  yield



@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""				)
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     Optional[int]:
 import torch

 def bert_cos_score_idf(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Any				,       *SCREAMING_SNAKE_CASE__       :	str				,       **SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  return torch.tensor([[1.0, 1.0, 1.0]] * len(SCREAMING_SNAKE_CASE__				)				)

 # mock get_model which is supposed to do download a bert model
 # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
 with patch("""bert_score.scorer.get_model"""				), patch(
     """bert_score.scorer.bert_cos_score_idf"""				) as mock_bert_cos_score_idf:
  _snake_case    :  Any					=					bert_cos_score_idf
  yield



@LocalMetricTest.register_intensive_calls_patcher("""comet"""				)
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     str:
 def load_from_checkpoint(SCREAMING_SNAKE_CASE__       :	int				):







  class 		snake_case						:



     '''simple docstring'''






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

      """simple docstring"""







      assert len(lowerCAmelCase) == 2
      _snake_case    :  Any					=					[0.19, 0.92]
      return scores, sum(lowerCAmelCase) / len(lowerCAmelCase)
  return Model()

 # mock load_from_checkpoint which is supposed to do download a bert model
 # mock load_from_checkpoint which is supposed to do download a bert model
 with patch("""comet.download_model"""				) as mock_download_model:
  _snake_case    :  Dict					=					None
  with patch("""comet.load_from_checkpoint"""				) as mock_load_from_checkpoint:
   _snake_case    :  Any					=					load_from_checkpoint
   yield



def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Optional[int]					=					load_metric(os.path.join("""metrics"""				,       """seqeval"""				)				)
 _snake_case    :  Optional[int]					=					"""ERROR"""
 _snake_case    :  Dict					=					F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
 with pytest.raises(SCREAMING_SNAKE_CASE__				,       match=re.escape(SCREAMING_SNAKE_CASE__				)				):
  metric.compute(predictions=[]				,       references=[]				,       scheme=SCREAMING_SNAKE_CASE__				)





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,
  )
