





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()
_lowerCamelCase			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      {
    "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    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      ):


       for attribute in key.split('''.'''      ):
              _lowerCamelCase							:     Tuple	=		getattr(_UpperCamelCase ,    _UpperCamelCase      )

       if weight_type is not None:
              _lowerCamelCase							:     int	=		getattr(_UpperCamelCase ,    _UpperCamelCase      ).shape
       else:
              _lowerCamelCase							:     List[str]	=		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":
              _lowerCamelCase							:     Optional[int]	=		value
       elif weight_type == "weight_g":
              _lowerCamelCase							:     Dict	=		value
       elif weight_type == "weight_v":
              _lowerCamelCase							:     Dict	=		value
       elif weight_type == "bias":
              _lowerCamelCase							:     List[Any]	=		value
       else:
              _lowerCamelCase							:     int	=		value

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




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[Any]	=		[]
       _lowerCamelCase							:     Dict	=		fairseq_model.state_dict()

       _lowerCamelCase							:     List[Any]	=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _lowerCamelCase							:     List[Any]	=		False
              if "conv_layers" in name:
                     load_conv_layer(
                         _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    hf_model.config.feat_extract_norm == '''group''' ,    )
                     _lowerCamelCase							:     Dict	=		True
              else:
                     for key, mapped_key in MAPPING.items():
                            _lowerCamelCase							:     List[Any]	=		'''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]:
                                   _lowerCamelCase							:     Dict	=		True
                                   if "*" in mapped_key:
                                          _lowerCamelCase							:     Tuple	=		name.split(_UpperCamelCase      )[0].split('''.'''      )[-2]
                                          _lowerCamelCase							:     str	=		mapped_key.replace('''*''' ,    _UpperCamelCase      )
                                   if "weight_g" in name:
                                          _lowerCamelCase							:     Union[str, Any]	=		'''weight_g'''
                                   elif "weight_v" in name:
                                          _lowerCamelCase							:     Union[str, Any]	=		'''weight_v'''
                                   elif "weight" in name:
                                          _lowerCamelCase							:     List[Any]	=		'''weight'''
                                   elif "bias" in name:
                                          _lowerCamelCase							:     Any	=		'''bias'''
                                   else:
                                          _lowerCamelCase							:     List[Any]	=		None
                                   set_recursively(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )
                            continue
              if not is_used:
                     unused_weights.append(_UpperCamelCase      )

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




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Optional[int]	=		full_name.split('''conv_layers.'''      )[-1]
       _lowerCamelCase							:     Tuple	=		name.split('''.'''      )
       _lowerCamelCase							:     Tuple	=		int(items[0]      )
       _lowerCamelCase							:     Optional[int]	=		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."""
                     )
                     _lowerCamelCase							:     int	=		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."""
                     )
                     _lowerCamelCase							:     Optional[Any]	=		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."
                     )
                     _lowerCamelCase							:     Optional[Any]	=		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."""
                     )
                     _lowerCamelCase							:     Dict	=		value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
       else:
              unused_weights.append(_UpperCamelCase      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     int	=		SEWConfig()
       if is_finetuned:
              _lowerCamelCase							:     Optional[Any]	=		model.wav_encoder.wav_model.cfg
       else:
              _lowerCamelCase							:     Dict	=		model.cfg

       _lowerCamelCase							:     List[str]	=		fs_config.conv_bias
       _lowerCamelCase							:     List[str]	=		eval(fs_config.conv_feature_layers      )
       _lowerCamelCase							:     Dict	=		[x[0] for x in conv_layers]
       _lowerCamelCase							:     Union[str, Any]	=		[x[1] for x in conv_layers]
       _lowerCamelCase							:     Optional[Any]	=		[x[2] for x in conv_layers]
       _lowerCamelCase							:     List[str]	=		'''gelu'''
       _lowerCamelCase							:     str	=		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       _lowerCamelCase							:     List[str]	=		0.0
       _lowerCamelCase							:     Tuple	=		fs_config.activation_fn.name
       _lowerCamelCase							:     List[Any]	=		fs_config.encoder_embed_dim
       _lowerCamelCase							:     Any	=		0.0_2
       _lowerCamelCase							:     List[Any]	=		fs_config.encoder_ffn_embed_dim
       _lowerCamelCase							:     Tuple	=		1e-5
       _lowerCamelCase							:     List[Any]	=		fs_config.encoder_layerdrop
       _lowerCamelCase							:     Union[str, Any]	=		fs_config.encoder_attention_heads
       _lowerCamelCase							:     Union[str, Any]	=		fs_config.conv_pos_groups
       _lowerCamelCase							:     Union[str, Any]	=		fs_config.conv_pos
       _lowerCamelCase							:     Optional[int]	=		len(_UpperCamelCase      )
       _lowerCamelCase							:     str	=		fs_config.encoder_layers
       _lowerCamelCase							:     Tuple	=		fs_config.squeeze_factor

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              _lowerCamelCase							:     Tuple	=		model.cfg
              _lowerCamelCase							:     int	=		fs_config.final_dropout
              _lowerCamelCase							:     Optional[int]	=		fs_config.layerdrop
       _lowerCamelCase							:     int	=		fs_config.activation_dropout
       _lowerCamelCase							:     Dict	=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       _lowerCamelCase							:     Optional[Any]	=		fs_config.attention_dropout
       _lowerCamelCase							:     Dict	=		fs_config.dropout_input
       _lowerCamelCase							:     Union[str, Any]	=		fs_config.dropout
       _lowerCamelCase							:     List[str]	=		fs_config.mask_channel_length
       _lowerCamelCase							:     int	=		fs_config.mask_channel_prob
       _lowerCamelCase							:     Any	=		fs_config.mask_length
       _lowerCamelCase							:     Any	=		fs_config.mask_prob

       _lowerCamelCase							:     str	=		'''Wav2Vec2FeatureExtractor'''
       _lowerCamelCase							:     List[str]	=		'''Wav2Vec2CTCTokenizer'''

       return config




@torch.no_grad()
def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_=None ,    lowercase_=None ,    lowercase_=True      ):


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

       if config_path is not None:
              _lowerCamelCase							:     Any	=		SEWConfig.from_pretrained(_UpperCamelCase      )
       else:
              _lowerCamelCase							:     Union[str, Any]	=		convert_config(model[0] ,    _UpperCamelCase      )
       _lowerCamelCase							:     List[str]	=		model[0].eval()

       _lowerCamelCase							:     Optional[int]	=		True if config.feat_extract_norm == '''layer''' else False
       _lowerCamelCase							:     Tuple	=		WavaVecaFeatureExtractor(
           feature_size=1 ,    sampling_rate=1_60_00 ,    padding_value=0 ,    do_normalize=_UpperCamelCase ,    return_attention_mask=_UpperCamelCase ,    )

       if is_finetuned:
              if dict_path:
                     _lowerCamelCase							:     Dict	=		Dictionary.load(_UpperCamelCase      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _lowerCamelCase							:     Dict	=		target_dict.pad_index
                     _lowerCamelCase							:     Optional[Any]	=		target_dict.bos_index
                     _lowerCamelCase							:     List[Any]	=		target_dict.pad_index
                     _lowerCamelCase							:     Any	=		target_dict.bos_index
                     _lowerCamelCase							:     Optional[int]	=		target_dict.eos_index
                     _lowerCamelCase							:     Tuple	=		len(target_dict.symbols      )
                     _lowerCamelCase							:     Union[str, Any]	=		os.path.join(_UpperCamelCase ,    '''vocab.json'''      )
                     if not os.path.isdir(_UpperCamelCase      ):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCamelCase      )      )
                            return
                     os.makedirs(_UpperCamelCase ,    exist_ok=_UpperCamelCase      )
                     with open(_UpperCamelCase ,    '''w''' ,    encoding='''utf-8'''      ) as vocab_handle:
                            json.dump(target_dict.indices ,    _UpperCamelCase      )
                     _lowerCamelCase							:     Optional[Any]	=		WavaVecaCTCTokenizer(
                         _UpperCamelCase ,    unk_token=target_dict.unk_word ,    pad_token=target_dict.pad_word ,    bos_token=target_dict.bos_word ,    eos_token=target_dict.eos_word ,    word_delimiter_token='''|''' ,    do_lower_case=_UpperCamelCase ,    )
                     _lowerCamelCase							:     str	=		WavaVecaProcessor(feature_extractor=_UpperCamelCase ,    tokenizer=_UpperCamelCase      )
                     processor.save_pretrained(_UpperCamelCase      )

              _lowerCamelCase							:     str	=		SEWForCTC(_UpperCamelCase      )
       else:
              _lowerCamelCase							:     Any	=		SEWModel(_UpperCamelCase      )
              feature_extractor.save_pretrained(_UpperCamelCase      )

       recursively_load_weights(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )

       hf_model.save_pretrained(_UpperCamelCase      )


if __name__ == "__main__":
       _lowerCamelCase			       =      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'
       )
       _lowerCamelCase			       =      parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )










import pytest

import datasets


# Import fixture modules as plugins
_lowerCamelCase			       =      ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
       for item in items:
              if any(marker in item.keywords for marker in ['''integration''', '''unit''']      ):
                     continue
              item.add_marker(pytest.mark.unit      )




def    __UpperCAmelCase(      lowercase_      ):
       config.addinivalue_line('''markers''' ,    '''torchaudio_latest: mark test to run with torchaudio>=0.12'''      )




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
       _lowerCamelCase							:     Optional[Any]	=		tmp_path_factory.getbasetemp() / '''cache'''
       _lowerCamelCase							:     Optional[Any]	=		test_hf_cache_home / '''datasets'''
       _lowerCamelCase							:     Union[str, Any]	=		test_hf_cache_home / '''metrics'''
       _lowerCamelCase							:     Dict	=		test_hf_cache_home / '''modules'''
       monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     str	=		test_hf_datasets_cache / '''downloads'''
       monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     Optional[int]	=		test_hf_datasets_cache / '''downloads''' / '''extracted'''
       monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' ,    str(lowercase_      )      )




@pytest.fixture(autouse=lowercase_ ,    scope='''session'''      )
def    __UpperCAmelCase(      ):
       datasets.disable_progress_bar()




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_      ):
       # don't take tests into account when counting downloads
       monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' ,    lowercase_      )




@pytest.fixture
def    __UpperCAmelCase(      lowercase_      ):
       # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
       # To be removed once SQLAlchemy 2.0 supported
       monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' ,    lowercase_      )

