





'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

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

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


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :str       =				{
    '''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							:      List[Any]			,     __lowercase							:      str			,     __lowercase							:      Union[str, Any]			,     __lowercase							:      Optional[int]			,     __lowercase							:      List[Any]       )		->       int:


     '''simple docstring'''



     for attribute in key.split("."       ):
          _UpperCAmelCase     							=   getattr(__lowercase			,     __lowercase       )

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

     logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )
def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      List[str]			,     __lowercase							:      int       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   []
     _UpperCAmelCase     							=   fairseq_model.state_dict()

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

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

     logger.warning(f'Unused weights: {unused_weights}'       )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      Tuple			,     __lowercase							:      Dict			,     __lowercase							:      Union[str, Any]			,     __lowercase							:      Union[str, Any]       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   full_name.split("conv_layers."       )[-1]
     _UpperCAmelCase     							=   name.split("."       )
     _UpperCAmelCase     							=   int(items[0]       )
     _UpperCAmelCase     							=   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.'
               )
               _UpperCAmelCase     							=   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.'
               )
               _UpperCAmelCase     							=   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."
               )
               _UpperCAmelCase     							=   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.'
               )
               _UpperCAmelCase     							=   value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
     else:
          unused_weights.append(__lowercase       )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   SEWConfig()
     if is_finetuned:
          _UpperCAmelCase     							=   model.wav_encoder.wav_model.cfg
     else:
          _UpperCAmelCase     							=   model.cfg

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

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

     _UpperCAmelCase     							=   "Wav2Vec2FeatureExtractor"
     _UpperCAmelCase     							=   "Wav2Vec2CTCTokenizer"

     return config
@torch.no_grad()
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      Tuple			,     __lowercase							:      Optional[int]=None			,     __lowercase							:      List[str]=None			,     __lowercase							:      List[str]=True       )		->       List[Any]:


     '''simple docstring'''



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

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

     _UpperCAmelCase     							=   True if config.feat_extract_norm == "layer" else False
     _UpperCAmelCase     							=   WavaVecaFeatureExtractor(
         feature_size=1			,     sampling_rate=1_6000			,     padding_value=0			,     do_normalize=__lowercase			,     return_attention_mask=__lowercase			,     )

     if is_finetuned:
          if dict_path:
               _UpperCAmelCase     							=   Dictionary.load(__lowercase       )

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

          _UpperCAmelCase     							=   SEWForCTC(__lowercase       )
     else:
          _UpperCAmelCase     							=   SEWModel(__lowercase       )
          feature_extractor.save_pretrained(__lowercase       )

     recursively_load_weights(__lowercase			,     __lowercase			,     __lowercase       )

     hf_model.save_pretrained(__lowercase       )


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











'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




