




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase			:	Optional[Any]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Optional[Any]							 =     {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       for attribute in key.split('''.'''		):
              A__   				=					getattr(lowerCAmelCase_				,       lowerCAmelCase_		)

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

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       A__   				=					[]
       A__   				=					fairseq_model.state_dict()

       A__   				=					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              A__   				=					False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase_				,       lowerCAmelCase_				,       lowerCAmelCase_				,       lowerCAmelCase_				,       hf_model.config.feat_extract_norm == '''group'''				,       )
                     A__   				=					True
              else:
                     for key, mapped_key in MAPPING.items():
                            A__   				=					'''hubert.''' + 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] and not is_finetuned):
                                   A__   				=					True
                                   if "*" in mapped_key:
                                          A__   				=					name.split(lowerCAmelCase_		)[0].split('''.'''		)[-2]
                                          A__   				=					mapped_key.replace('''*'''				,       lowerCAmelCase_		)
                                   if "weight_g" in name:
                                          A__   				=					'''weight_g'''
                                   elif "weight_v" in name:
                                          A__   				=					'''weight_v'''
                                   elif "weight" in name:
                                          A__   				=					'''weight'''
                                   elif "bias" in name:
                                          A__   				=					'''bias'''
                                   else:
                                          A__   				=					None
                                   set_recursively(lowerCAmelCase_				,       lowerCAmelCase_				,       lowerCAmelCase_				,       lowerCAmelCase_				,       lowerCAmelCase_		)
                            continue
              if not is_used:
                     unused_weights.append(lowerCAmelCase_		)

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> Any:



       """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:
                     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."""
                     )
                     A__   				=					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."""
                     )
                     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:
                     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."
                     )
                     A__   				=					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."""
                     )
                     A__   				=					value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
       else:
              unused_weights.append(lowerCAmelCase_		)







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=None				,       lowercase_=None				,       lowercase_=True		)     -> Dict:



       """simple docstring"""

       if config_path is not None:
              A__   				=					HubertConfig.from_pretrained(lowerCAmelCase_		)
       else:
              A__   				=					HubertConfig()

       if is_finetuned:
              if dict_path:
                     A__   				=					Dictionary.load(lowerCAmelCase_		)

                     # 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(lowerCAmelCase_				,       '''vocab.json'''		)
                     if not os.path.isdir(lowerCAmelCase_		):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_		)		)
                            return
                     os.makedirs(lowerCAmelCase_				,       exist_ok=lowerCAmelCase_		)
                     with open(lowerCAmelCase_				,       '''w'''				,       encoding='''utf-8'''		) as vocab_handle:
                            json.dump(target_dict.indices				,       lowerCAmelCase_		)
                     A__   				=					WavaVecaCTCTokenizer(
                         lowerCAmelCase_				,       unk_token=target_dict.unk_word				,       pad_token=target_dict.pad_word				,       bos_token=target_dict.bos_word				,       eos_token=target_dict.eos_word				,       word_delimiter_token='''|'''				,       do_lower_case=lowerCAmelCase_				,       )
                     A__   				=					True if config.feat_extract_norm == '''layer''' else False
                     A__   				=					WavaVecaFeatureExtractor(
                         feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=lowerCAmelCase_				,       return_attention_mask=lowerCAmelCase_				,       )
                     A__   				=					WavaVecaProcessor(feature_extractor=lowerCAmelCase_				,       tokenizer=lowerCAmelCase_		)
                     processor.save_pretrained(lowerCAmelCase_		)

              A__   				=					HubertForCTC(lowerCAmelCase_		)
       else:
              A__   				=					HubertModel(lowerCAmelCase_		)

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

       A__   				=					model[0].eval()

       recursively_load_weights(lowerCAmelCase_				,       lowerCAmelCase_				,       lowerCAmelCase_		)

       hf_wavavec.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     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"""
  )
  _lowerCamelCase			:	List[str]							 =     parser.parse_args()
  convert_hubert_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )





import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''				,       [False, True]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_				,       keep_in_memory=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@pytest.mark.parametrize(
    '''features'''				,       [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,       )
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					features.copy() if features else default_expected_features
       A__   				=					(
           Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader(lowercase_				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_				,       split=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('''path_type'''				,       [str, list]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       if issubclass(lowercase_				,       lowercase_		):
              A__   				=					parquet_path
       elif issubclass(lowercase_				,       lowercase_		):
              A__   				=					[parquet_path]
       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=("train",)		)     -> List[str]:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       for split in splits:
              A__   				=					dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('''keep_in_memory'''				,       [False, True]		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              A__   				=					ParquetDatasetReader(
                  {'''train''': parquet_path}				,       cache_dir=lowercase_				,       keep_in_memory=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_		)







@pytest.mark.parametrize(
    '''features'''				,       [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,       )
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					features.copy() if features else default_expected_features
       A__   				=					(
           Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader({'''train''': parquet_path}				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       if split:
              A__   				=					{split: parquet_path}
       else:
              A__   				=					'''train'''
              A__   				=					{'''train''': parquet_path, '''test''': parquet_path}
       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					ParquetDatasetReader(lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_datasetdict(lowercase_				,       lowercase_				,       splits=list(path.keys()		)		)
       assert all(dataset[split].split == split for split in path.keys()		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					ParquetDatasetWriter(lowercase_				,       tmp_path / '''foo.parquet'''		)
       assert writer.write() > 0
       A__   				=					pq.ParquetFile(tmp_path / '''foo.parquet'''		)
       A__   				=					pf.read()
       assert dataset.data.table == output_table







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					str(shared_datadir / '''test_image_rgb.jpg'''		)
       A__   				=					{'''image''': [image_path]}
       A__   				=					Features({'''image''': Image()}		)
       A__   				=					Dataset.from_dict(lowercase_				,       features=lowercase_		)
       A__   				=					ParquetDatasetWriter(lowercase_				,       tmp_path / '''foo.parquet'''		)
       assert writer.write() > 0

       A__   				=					Dataset.from_parquet(str(tmp_path / '''foo.parquet'''		)		)
       assert dataset.features == reloaded_dataset.features

       A__   				=					ParquetDatasetReader(str(tmp_path / '''foo.parquet'''		)				,       streaming=lowercase_		).read()
       assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    '''feature, expected'''				,       [
        (Features({'''foo''': Value('''int32'''		)}		), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]				,       )
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert get_writer_batch_size(lowercase_		) == expected


