

"""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()
__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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[Any]:
 for attribute in key.split('''.'''						):
  __magic_name__     =      getattr(__A      ,   __A						)

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

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 __magic_name__     =      []
 __magic_name__     =      fairseq_model.state_dict()

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

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

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      full_name.split('''conv_layers.'''						)[-1]
 __magic_name__     =      name.split('''.'''						)
 __magic_name__     =      int(items[0]						)
 __magic_name__     =      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.'''
   )
   __magic_name__     =      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.'''
   )
   __magic_name__     =      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."
   )
   __magic_name__     =      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.'''
   )
   __magic_name__     =      value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
 else:
  unused_weights.append(__A						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      SEWConfig()
 if is_finetuned:
  __magic_name__     =      model.wav_encoder.wav_model.cfg
 else:
  __magic_name__     =      model.cfg

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

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

 __magic_name__     =      '''Wav2Vec2FeatureExtractor'''
 __magic_name__     =      '''Wav2Vec2CTCTokenizer'''

 return config





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None      ,   __UpperCamelCase=True						) ->      Union[str, Any]:

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

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

 __magic_name__     =      True if config.feat_extract_norm == '''layer''' else False
 __magic_name__     =      WavaVecaFeatureExtractor(
     feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=__A      ,   return_attention_mask=__A      ,   )

 if is_finetuned:
  if dict_path:
   __magic_name__     =      Dictionary.load(__A						)

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

  __magic_name__     =      SEWForCTC(__A						)
 else:
  __magic_name__     =      SEWModel(__A						)
  feature_extractor.save_pretrained(__A						)

 recursively_load_weights(__A      ,   __A      ,   __A						)

 hf_model.save_pretrained(__A						)


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
  )




"""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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      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}}'''						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)
 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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_info.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfo.from_directory(__UpperCamelCase						)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(__UpperCamelCase      ,   '''dataset_info.json'''						)						)





def 	lowercase      (   ) ->      Any:
 __magic_name__     =      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      ,   )
 __magic_name__     =      dataset_info._to_yaml_dict()
 assert sorted(__UpperCamelCase						) == 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)						)
 __magic_name__     =      yaml.safe_dump(__UpperCamelCase						)
 __magic_name__     =      yaml.safe_load(__UpperCamelCase						)
 assert dataset_info_yaml_dict == reloaded





def 	lowercase      (   ) ->      str:
 __magic_name__     =      DatasetInfo()
 __magic_name__     =      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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_infos_dict.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  __magic_name__     =      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
  __magic_name__     =      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(__UpperCamelCase      ,   '''README.md'''						)						)

