

'''simple docstring'''
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCamelCase :      Any					 =     logging.get_logger(__name__)

lowerCamelCase :      Optional[int]					 =     {
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
lowerCamelCase :      Any					 =     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Any							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any				)      ->			Any:
     """simple docstring"""




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

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

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     Dict				)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =fairseq_model.state_dict()

     _SCREAMING_SNAKE_CASE							    =hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          _SCREAMING_SNAKE_CASE							    =False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, hf_model.config.feat_extract_norm == 'group'							, )
               _SCREAMING_SNAKE_CASE							    =True
          else:
               for key, mapped_key in MAPPING.items():
                    _SCREAMING_SNAKE_CASE							    ='unispeech_sat.' + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'				)[:-1]				) != key):
                              # special case since naming is very similar
                              continue
                         _SCREAMING_SNAKE_CASE							    =True
                         if "*" in mapped_key:
                              _SCREAMING_SNAKE_CASE							    =name.split(_UpperCamelCase				)[0].split('.'				)[-2]
                              _SCREAMING_SNAKE_CASE							    =mapped_key.replace('*'							, _UpperCamelCase				)
                         if "weight_g" in name:
                              _SCREAMING_SNAKE_CASE							    ='weight_g'
                         elif "weight_v" in name:
                              _SCREAMING_SNAKE_CASE							    ='weight_v'
                         elif "bias" in name:
                              _SCREAMING_SNAKE_CASE							    ='bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _SCREAMING_SNAKE_CASE							    ='weight'
                         else:
                              _SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]				)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =full_name.split('conv_layers.'				)[-1]
     _SCREAMING_SNAKE_CASE							    =name.split('.'				)
     _SCREAMING_SNAKE_CASE							    =int(items[0]				)
     _SCREAMING_SNAKE_CASE							    =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."				)
               _SCREAMING_SNAKE_CASE							    =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."				)
               _SCREAMING_SNAKE_CASE							    =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[layer_id].layer_norm.bias.data.shape} was found."				)
               _SCREAMING_SNAKE_CASE							    =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[layer_id].layer_norm.weight.data.shape} was found."				)
               _SCREAMING_SNAKE_CASE							    =value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
     else:
          unused_weights.append(_UpperCamelCase				)


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     List[str]							, _UpperCamelCase						:     List[Any]=None							, _UpperCamelCase						:     str=None							, _UpperCamelCase						:     Tuple=True				)      ->			str:
     """simple docstring"""




     if config_path is not None:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatConfig.from_pretrained(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatConfig()

     _SCREAMING_SNAKE_CASE							    =''

     if is_finetuned:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatForCTC(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatForPreTraining(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]							, arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
     _SCREAMING_SNAKE_CASE							    =model[0].eval()

     recursively_load_weights(_UpperCamelCase							, _UpperCamelCase				)

     hf_wavavec.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
     lowerCamelCase :      List[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 :      Union[str, Any]					 =     parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )



"""simple docstring"""





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_a       =							_symbol_database.Default()


_a       =							_descriptor_pool.Default().AddSerializedFile(
    b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

_a       =							globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
   _a       =							None
   _a       =							b'H\003'
   # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
   # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
   # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
   # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
   # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
   _a       =							45
   _a       =							15_81
   _a       =							15_17
   _a       =							15_70
   _a       =							15_84
   _a       =							17_93
   _a       =							17_95
   _a       =							19_16
   _a       =							18_64
   _a       =							19_05
   _a       =							19_19
   _a       =							24_29
   _a       =							22_08
   _a       =							24_18
   _a       =							23_23
   _a       =							24_07
# @@protoc_insertion_point(module_scope)




