






'''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()
UpperCamelCase__ =			logging.get_logger(__name__)

UpperCamelCase__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for attribute in key.split("."							):
      lowercase_	:    int           =						getattr(_UpperCamelCase						,		_UpperCamelCase							)

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

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =						[]
   lowercase_	:    Any           =						fairseq_model.state_dict()

   lowercase_	:    List[str]           =						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase_	:    Union[str, Any]           =						False
      if "conv_layers" in name:
         load_conv_layer(
             _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		hf_model.config.feat_extract_norm == "group"						,		)
         lowercase_	:    List[str]           =						True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase_	:    str           =						"""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]:
               lowercase_	:    Union[str, Any]           =						True
               if "*" in mapped_key:
                  lowercase_	:    Optional[Any]           =						name.split(_UpperCamelCase							)[0].split("."							)[-2]
                  lowercase_	:    int           =						mapped_key.replace("*"						,		_UpperCamelCase							)
               if "weight_g" in name:
                  lowercase_	:    Optional[int]           =						"""weight_g"""
               elif "weight_v" in name:
                  lowercase_	:    List[Any]           =						"""weight_v"""
               elif "weight" in name:
                  lowercase_	:    List[str]           =						"""weight"""
               elif "bias" in name:
                  lowercase_	:    Optional[int]           =						"""bias"""
               else:
                  lowercase_	:    List[Any]           =						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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Tuple           =						full_name.split("conv_layers."							)[-1]
   lowercase_	:    Optional[Any]           =						name.split("."							)
   lowercase_	:    int           =						int(items[0]							)
   lowercase_	:    Dict           =						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."""
         )
         lowercase_	:    List[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."""
         )
         lowercase_	:    Any           =						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."
         )
         lowercase_	:    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."""
         )
         lowercase_	:    Optional[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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =						SEWConfig()
   if is_finetuned:
      lowercase_	:    Union[str, Any]           =						model.wav_encoder.wav_model.cfg
   else:
      lowercase_	:    Tuple           =						model.cfg

   lowercase_	:    List[str]           =						fs_config.conv_bias
   lowercase_	:    List[Any]           =						eval(fs_config.conv_feature_layers							)
   lowercase_	:    Optional[Any]           =						[x[0] for x in conv_layers]
   lowercase_	:    Dict           =						[x[1] for x in conv_layers]
   lowercase_	:    List[str]           =						[x[2] for x in conv_layers]
   lowercase_	:    str           =						"""gelu"""
   lowercase_	:    Tuple           =						"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase_	:    int           =						0.0
   lowercase_	:    Union[str, Any]           =						fs_config.activation_fn.name
   lowercase_	:    str           =						fs_config.encoder_embed_dim
   lowercase_	:    Union[str, Any]           =						0.02
   lowercase_	:    str           =						fs_config.encoder_ffn_embed_dim
   lowercase_	:    List[str]           =						1e-5
   lowercase_	:    List[Any]           =						fs_config.encoder_layerdrop
   lowercase_	:    Union[str, Any]           =						fs_config.encoder_attention_heads
   lowercase_	:    Any           =						fs_config.conv_pos_groups
   lowercase_	:    Optional[Any]           =						fs_config.conv_pos
   lowercase_	:    List[str]           =						len(_UpperCamelCase							)
   lowercase_	:    int           =						fs_config.encoder_layers
   lowercase_	:    List[Any]           =						fs_config.squeeze_factor

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

   lowercase_	:    List[str]           =						"""Wav2Vec2FeatureExtractor"""
   lowercase_	:    Tuple           =						"""Wav2Vec2CTCTokenizer"""

   return config




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=True							):


   """simple docstring"""




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

   if config_path is not None:
      lowercase_	:    int           =						SEWConfig.from_pretrained(_UpperCamelCase							)
   else:
      lowercase_	:    Dict           =						convert_config(model[0]						,		_UpperCamelCase							)
   lowercase_	:    Any           =						model[0].eval()

   lowercase_	:    Optional[int]           =						True if config.feat_extract_norm == """layer""" else False
   lowercase_	:    Optional[int]           =						WavaVecaFeatureExtractor(
       feature_size=1						,		sampling_rate=1_6000						,		padding_value=0						,		do_normalize=_UpperCamelCase						,		return_attention_mask=_UpperCamelCase						,		)

   if is_finetuned:
      if dict_path:
         lowercase_	:    int           =						Dictionary.load(_UpperCamelCase							)

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

      lowercase_	:    Optional[int]           =						SEWForCTC(_UpperCamelCase							)
   else:
      lowercase_	:    int           =						SEWModel(_UpperCamelCase							)
      feature_extractor.save_pretrained(_UpperCamelCase							)

   recursively_load_weights(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)

   hf_model.save_pretrained(_UpperCamelCase							)


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
 )












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     0
   lowercase_	:    Optional[Any]           =     len(_UpperCamelCase							)  # No of vertices in graph
   lowercase_	:    Union[str, Any]           =     [0] * n
   lowercase_	:    Optional[int]           =     [False] * n

   def dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     True
      lowercase_	:    Dict           =     id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		id_							)
            lowercase_	:    str           =     min(low[at]						,		low[to]							)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)							)
         else:
            # This edge is a back edge and cannot be a bridge
            lowercase_	:    Optional[int]           =     min(low[at]						,		low[to]							)

   lowercase_	:    list[tuple[int, int]]           =     []
   for i in range(_UpperCamelCase							):
      if not visited[i]:
         dfs(_UpperCamelCase						,		-1						,		_UpperCamelCase						,		id_							)
   return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()




