



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_			=					logging.get_logger(__name__)

a_			=					{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
}
a_			=					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: int ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Tuple							):
   for attribute in key.split('''.'''							):
      __lowerCamelCase													=    getattr(_UpperCamelCase ,_UpperCamelCase							)

   if weight_type is not None:
      __lowerCamelCase													=    getattr(_UpperCamelCase ,_UpperCamelCase							).shape
   else:
      __lowerCamelCase													=    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":
      __lowerCamelCase													=    value
   elif weight_type == "weight_g":
      __lowerCamelCase													=    value
   elif weight_type == "weight_v":
      __lowerCamelCase													=    value
   elif weight_type == "bias":
      __lowerCamelCase													=    value
   elif weight_type == "running_mean":
      __lowerCamelCase													=    value
   elif weight_type == "running_var":
      __lowerCamelCase													=    value
   elif weight_type == "num_batches_tracked":
      __lowerCamelCase													=    value
   elif weight_type == "inv_freq":
      __lowerCamelCase													=    value
   else:
      __lowerCamelCase													=    value

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





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    []
   __lowerCamelCase													=    fairseq_model.state_dict()

   __lowerCamelCase													=    hf_model.wavaveca_conformer.feature_extractor

   for name, value in fairseq_dict.items():
      __lowerCamelCase													=    False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
         __lowerCamelCase													=    True
      else:
         for key, mapped_key in MAPPING.items():
            __lowerCamelCase													=    '''wav2vec2_conformer.''' + 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]:
               __lowerCamelCase													=    True
               if "*" in mapped_key:
                  __lowerCamelCase													=    name.split(_UpperCamelCase							)[0].split('''.'''							)[-2]
                  __lowerCamelCase													=    mapped_key.replace('''*''' ,_UpperCamelCase							)
               if "pos_bias_u" in name:
                  __lowerCamelCase													=    None
               elif "pos_bias_v" in name:
                  __lowerCamelCase													=    None
               elif "weight_g" in name:
                  __lowerCamelCase													=    '''weight_g'''
               elif "weight_v" in name:
                  __lowerCamelCase													=    '''weight_v'''
               elif "bias" in name:
                  __lowerCamelCase													=    '''bias'''
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  __lowerCamelCase													=    '''weight'''
               elif "running_mean" in name:
                  __lowerCamelCase													=    '''running_mean'''
               elif "inv_freq" in name:
                  __lowerCamelCase													=    '''inv_freq'''
               elif "running_var" in name:
                  __lowerCamelCase													=    '''running_var'''
               elif "num_batches_tracked" in name:
                  __lowerCamelCase													=    '''num_batches_tracked'''
               else:
                  __lowerCamelCase													=    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 		a__			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    full_name.split('''conv_layers.'''							)[-1]
   __lowerCamelCase													=    name.split('''.'''							)
   __lowerCamelCase													=    int(items[0]							)
   __lowerCamelCase													=    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."""							)
         __lowerCamelCase													=    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."""							)
         __lowerCamelCase													=    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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""							)
         __lowerCamelCase													=    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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""							)
         __lowerCamelCase													=    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 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Dict ,_UpperCamelCase			: Any=None ,_UpperCamelCase			: int=None ,_UpperCamelCase			: Dict=True							):
   if config_path is not None:
      __lowerCamelCase													=    WavaVecaConformerConfig.from_pretrained(_UpperCamelCase ,hidden_act='''swish'''							)
   else:
      __lowerCamelCase													=    WavaVecaConformerConfig()

   if "rope" in checkpoint_path:
      __lowerCamelCase													=    '''rotary'''

   if is_finetuned:
      if dict_path:
         __lowerCamelCase													=    Dictionary.load(_UpperCamelCase							)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __lowerCamelCase													=    target_dict.pad_index
         __lowerCamelCase													=    target_dict.bos_index
         __lowerCamelCase													=    target_dict.eos_index
         __lowerCamelCase													=    len(target_dict.symbols							)
         __lowerCamelCase													=    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							)
         __lowerCamelCase													=    target_dict.indices

         # fairseq has the <pad> and <s> switched
         __lowerCamelCase													=    0
         __lowerCamelCase													=    1
         with open(_UpperCamelCase ,'''w''' ,encoding='''utf-8'''							) as vocab_handle:
            json.dump(_UpperCamelCase ,_UpperCamelCase							)
         __lowerCamelCase													=    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 ,)
         __lowerCamelCase													=    True if config.feat_extract_norm == '''layer''' else False
         __lowerCamelCase													=    WavaVecaFeatureExtractor(
             feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=_UpperCamelCase ,return_attention_mask=_UpperCamelCase ,)
         __lowerCamelCase													=    WavaVecaProcessor(feature_extractor=_UpperCamelCase ,tokenizer=_UpperCamelCase							)
         processor.save_pretrained(_UpperCamelCase							)

      __lowerCamelCase													=    WavaVecaConformerForCTC(_UpperCamelCase							)
   else:
      __lowerCamelCase													=    WavaVecaConformerForPreTraining(_UpperCamelCase							)

   if is_finetuned:
      __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
   else:
      __lowerCamelCase													=    argparse.Namespace(task='''audio_pretraining'''							)
      __lowerCamelCase													=    fairseq.tasks.setup_task(_UpperCamelCase							)

      __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=_UpperCamelCase							)

   __lowerCamelCase													=    model[0].eval()

   recursively_load_weights(_UpperCamelCase ,_UpperCamelCase ,not is_finetuned							)

   hf_wavavec.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
  a_			=					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"""
  )
  a_			=					parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






