




'''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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a		:  Optional[int]   			=			logging.get_logger(__name__)

a		:  Optional[int]   			=			{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''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						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    for attribute in key.split("""."""	):
        _a							:       Union[str, Any]       					=			getattr(UpperCamelCase__      ,  UpperCamelCase__	)

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

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





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[Any]       					=			[]
    _a							:       Optional[int]       					=			fairseq_model.state_dict()

    _a							:       Any       					=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _a							:       Optional[int]       					=			False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  hf_model.config.feat_extract_norm == """group"""      ,  )
            _a							:       Optional[Any]       					=			True
        else:
            for key, mapped_key in MAPPING.items():
                _a							:       str       					=			"sew_d." + 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]:
                    _a							:       Dict       					=			True
                    if "*" in mapped_key:
                        _a							:       Tuple       					=			name.split(UpperCamelCase__	)[0].split("""."""	)[-2]
                        if not layer_index.isnumeric():
                            continue
                        _a							:       List[Any]       					=			mapped_key.replace("""*"""      ,  UpperCamelCase__	)
                    if "weight_g" in name:
                        _a							:       Dict       					=			"weight_g"
                    elif "weight_v" in name:
                        _a							:       Dict       					=			"weight_v"
                    elif "weight" in name:
                        _a							:       Optional[int]       					=			"weight"
                    elif "bias" in name:
                        _a							:       Any       					=			"bias"
                    else:
                        _a							:       List[str]       					=			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						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Any       					=			full_name.split("""conv_layers."""	)[-1]
    _a							:       Union[str, Any]       					=			name.split("""."""	)
    _a							:       Union[str, Any]       					=			int(items[0]	)
    _a							:       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.'''
            )
            _a							:       Optional[Any]       					=			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							:       Optional[int]       					=			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							:       Union[str, 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.'''
            )
            _a							:       int       					=			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						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Any       					=			SEWDConfig()
    if is_finetuned:
        _a							:       List[str]       					=			model.wav_encoder.wav_model.cfg
    else:
        _a							:       Tuple       					=			model.cfg

    _a							:       Optional[Any]       					=			fs_config.conv_bias
    _a							:       Dict       					=			eval(fs_config.conv_feature_layers	)
    _a							:       str       					=			[x[0] for x in conv_layers]
    _a							:       List[str]       					=			[x[1] for x in conv_layers]
    _a							:       int       					=			[x[2] for x in conv_layers]
    _a							:       Optional[Any]       					=			"gelu"
    _a							:       Optional[Any]       					=			"layer" if fs_config.extractor_mode == "layer_norm" else "group"
    _a							:       List[str]       					=			0.0
    _a							:       List[str]       					=			fs_config.activation_fn.name
    _a							:       List[Any]       					=			fs_config.encoder_embed_dim
    _a							:       Tuple       					=			0.02
    _a							:       Any       					=			fs_config.encoder_ffn_embed_dim
    _a							:       int       					=			1e-5
    _a							:       List[Any]       					=			fs_config.encoder_layerdrop
    _a							:       Dict       					=			fs_config.encoder_attention_heads
    _a							:       int       					=			fs_config.conv_pos_groups
    _a							:       Any       					=			fs_config.conv_pos
    _a							:       Optional[int]       					=			len(UpperCamelCase__	)
    _a							:       Union[str, Any]       					=			fs_config.encoder_layers
    _a							:       List[Any]       					=			fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    _a							:       List[Any]       					=			fs_config.max_position_embeddings
    _a							:       int       					=			fs_config.position_buckets
    _a							:       Optional[int]       					=			fs_config.share_att_key
    _a							:       str       					=			fs_config.relative_attention
    _a							:       Union[str, Any]       					=			fs_config.position_biased_input
    _a							:       Optional[Any]       					=			tuple(fs_config.pos_att_type.split("""|"""	)	)
    _a							:       str       					=			fs_config.norm_rel_ebd

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

    _a							:       Dict       					=			"Wav2Vec2FeatureExtractor"
    _a							:       List[str]       					=			"Wav2Vec2CTCTokenizer"

    return config





@torch.no_grad()
def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=True	):

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

    if config_path is not None:
        _a							:       Dict       					=			SEWDConfig.from_pretrained(UpperCamelCase__	)
    else:
        _a							:       Optional[int]       					=			convert_config(model[0]      ,  UpperCamelCase__	)
    _a							:       Any       					=			model[0].eval()

    _a							:       Tuple       					=			True if config.feat_extract_norm == "layer" else False
    _a							:       List[str]       					=			WavaVecaFeatureExtractor(
        feature_size=1      ,  sampling_rate=16_000      ,  padding_value=0      ,  do_normalize=UpperCamelCase__      ,  return_attention_mask=UpperCamelCase__      ,  )

    if is_finetuned:
        if dict_path:
            _a							:       Tuple       					=			Dictionary.load(UpperCamelCase__	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _a							:       List[str]       					=			target_dict.pad_index
            _a							:       Dict       					=			target_dict.bos_index
            _a							:       Union[str, Any]       					=			target_dict.pad_index
            _a							:       str       					=			target_dict.bos_index
            _a							:       Any       					=			target_dict.eos_index
            _a							:       Any       					=			len(target_dict.symbols	)
            _a							:       Tuple       					=			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__	)
            _a							:       int       					=			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__      ,  )
            _a							:       List[Any]       					=			WavaVecaProcessor(feature_extractor=UpperCamelCase__      ,  tokenizer=UpperCamelCase__	)
            processor.save_pretrained(UpperCamelCase__	)

        _a							:       Dict       					=			SEWDForCTC(UpperCamelCase__	)
    else:
        _a							:       Any       					=			SEWDModel(UpperCamelCase__	)
        feature_extractor.save_pretrained(UpperCamelCase__	)

    recursively_load_weights(UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__	)

    hf_model.save_pretrained(UpperCamelCase__	)


if __name__ == "__main__":
      a		:  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(
          '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
      )
      a		:  Optional[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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


