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()
UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """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							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Union[str, Any]:
     for attribute in key.split('''.'''							):
          a	:     Any						      =    getattr(lowerCAmelCase_    ,   lowerCAmelCase_							)

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

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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[str]:
     a	:     Union[str, Any]						      =    []
     a	:     Union[str, Any]						      =    fairseq_model.state_dict()

     a	:     List[str]						      =    hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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







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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						int:
     a	:     List[str]						      =    SEWDConfig()
     if is_finetuned:
          a	:     Optional[int]						      =    model.wav_encoder.wav_model.cfg
     else:
          a	:     Union[str, Any]						      =    model.cfg

     a	:     Optional[int]						      =    fs_config.conv_bias
     a	:     Any						      =    eval(fs_config.conv_feature_layers							)
     a	:     Optional[int]						      =    [x[0] for x in conv_layers]
     a	:     Optional[int]						      =    [x[1] for x in conv_layers]
     a	:     Dict						      =    [x[2] for x in conv_layers]
     a	:     Optional[Any]						      =    '''gelu'''
     a	:     str						      =    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
     a	:     Union[str, Any]						      =    0.0
     a	:     str						      =    fs_config.activation_fn.name
     a	:     str						      =    fs_config.encoder_embed_dim
     a	:     Any						      =    0.02
     a	:     List[Any]						      =    fs_config.encoder_ffn_embed_dim
     a	:     Any						      =    1e-5
     a	:     Optional[int]						      =    fs_config.encoder_layerdrop
     a	:     str						      =    fs_config.encoder_attention_heads
     a	:     str						      =    fs_config.conv_pos_groups
     a	:     Union[str, Any]						      =    fs_config.conv_pos
     a	:     Any						      =    len(lowerCAmelCase_							)
     a	:     Dict						      =    fs_config.encoder_layers
     a	:     Union[str, Any]						      =    fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     a	:     str						      =    fs_config.max_position_embeddings
     a	:     List[Any]						      =    fs_config.position_buckets
     a	:     List[Any]						      =    fs_config.share_att_key
     a	:     Dict						      =    fs_config.relative_attention
     a	:     Tuple						      =    fs_config.position_biased_input
     a	:     Dict						      =    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	:     Union[str, Any]						      =    model.cfg
          a	:     str						      =    fs_config.final_dropout
          a	:     Dict						      =    fs_config.layerdrop
     a	:     Optional[Any]						      =    fs_config.activation_dropout
     a	:     int						      =    fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     a	:     List[str]						      =    fs_config.attention_dropout
     a	:     Tuple						      =    fs_config.dropout_input
     a	:     int						      =    fs_config.dropout
     a	:     List[str]						      =    fs_config.mask_channel_length
     a	:     Union[str, Any]						      =    fs_config.mask_channel_prob
     a	:     Tuple						      =    fs_config.mask_length
     a	:     Optional[Any]						      =    fs_config.mask_prob

     a	:     Optional[Any]						      =    '''Wav2Vec2FeatureExtractor'''
     a	:     Optional[Any]						      =    '''Wav2Vec2CTCTokenizer'''

     return config







@torch.no_grad()
def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True							)						->						str:

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

     if config_path is not None:
          a	:     int						      =    SEWDConfig.from_pretrained(lowerCAmelCase_							)
     else:
          a	:     Optional[int]						      =    convert_config(model[0]    ,   lowerCAmelCase_							)
     a	:     str						      =    model[0].eval()

     a	:     Union[str, Any]						      =    True if config.feat_extract_norm == '''layer''' else False
     a	:     Any						      =    WavaVecaFeatureExtractor(
         feature_size=1    ,   sampling_rate=1_6_0_0_0    ,   padding_value=0    ,   do_normalize=lowerCAmelCase_    ,   return_attention_mask=lowerCAmelCase_    ,   )

     if is_finetuned:
          if dict_path:
               a	:     Any						      =    Dictionary.load(lowerCAmelCase_							)

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

          a	:     Optional[Any]						      =    SEWDForCTC(lowerCAmelCase_							)
     else:
          a	:     List[Any]						      =    SEWDModel(lowerCAmelCase_							)
          feature_extractor.save_pretrained(lowerCAmelCase_							)

     recursively_load_weights(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

     hf_model.save_pretrained(lowerCAmelCase_							)


if __name__ == "__main__":
  UpperCAmelCase				 =   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"""
  )
  UpperCAmelCase				 =   parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm