'''simple docstring'''





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()
lowercase_     :   Dict 				=				logging.get_logger(__name__)

lowercase_     :   Dict 				=				{
    "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",
}
lowercase_     :   Dict 				=				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		List[Any]       ,				snake_case_ :		Tuple       ,				snake_case_ :		List[str]       ,				snake_case_ :		List[Any]      ):



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

 if weight_type is not None:
  SCREAMING_SNAKE_CASE__:   int=      getattr(_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE      ).shape
 else:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      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__:   Optional[Any]=      value
 elif weight_type == "weight_g":
  SCREAMING_SNAKE_CASE__:   Optional[int]=      value
 elif weight_type == "weight_v":
  SCREAMING_SNAKE_CASE__:   str=      value
 elif weight_type == "bias":
  SCREAMING_SNAKE_CASE__:   int=      value
 elif weight_type == "running_mean":
  SCREAMING_SNAKE_CASE__:   int=      value
 elif weight_type == "running_var":
  SCREAMING_SNAKE_CASE__:   Tuple=      value
 elif weight_type == "num_batches_tracked":
  SCREAMING_SNAKE_CASE__:   Dict=      value
 elif weight_type == "inv_freq":
  SCREAMING_SNAKE_CASE__:   List[Any]=      value
 else:
  SCREAMING_SNAKE_CASE__:   Any=      value

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







def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		str       ,				snake_case_ :		Optional[Any]      ):
 SCREAMING_SNAKE_CASE__:   str=      []
 SCREAMING_SNAKE_CASE__:   List[str]=      fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE__:   Tuple=      hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE__:   Tuple=      False
  if "conv_layers" in name:
   load_conv_layer(
       _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				hf_model.config.feat_extract_norm == '''group'''       ,				)
   SCREAMING_SNAKE_CASE__:   Any=      True
  else:
   for key, mapped_key in MAPPING.items():
    SCREAMING_SNAKE_CASE__:   List[str]=      '''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]:
     SCREAMING_SNAKE_CASE__:   List[str]=      True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE__:   str=      name.split(_SCREAMING_SNAKE_CASE      )[0].split('''.'''      )[-2]
      SCREAMING_SNAKE_CASE__:   str=      mapped_key.replace('''*'''       ,				_SCREAMING_SNAKE_CASE      )
     if "pos_bias_u" in name:
      SCREAMING_SNAKE_CASE__:   str=      None
     elif "pos_bias_v" in name:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      None
     elif "weight_g" in name:
      SCREAMING_SNAKE_CASE__:   int=      '''weight_g'''
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE__:   List[Any]=      '''weight_v'''
     elif "bias" in name:
      SCREAMING_SNAKE_CASE__:   List[str]=      '''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      SCREAMING_SNAKE_CASE__:   Optional[int]=      '''weight'''
     elif "running_mean" in name:
      SCREAMING_SNAKE_CASE__:   Tuple=      '''running_mean'''
     elif "inv_freq" in name:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      '''inv_freq'''
     elif "running_var" in name:
      SCREAMING_SNAKE_CASE__:   Tuple=      '''running_var'''
     elif "num_batches_tracked" in name:
      SCREAMING_SNAKE_CASE__:   int=      '''num_batches_tracked'''
     else:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      None
     set_recursively(_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE      )
    continue
  if not is_used:
   unused_weights.append(_SCREAMING_SNAKE_CASE      )

 logger.warning(F'Unused weights: {unused_weights}'      )







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		Tuple       ,				snake_case_ :		List[str]       ,				snake_case_ :		List[Any]       ,				snake_case_ :		Optional[int]      ):
 SCREAMING_SNAKE_CASE__:   int=      full_name.split('''conv_layers.'''      )[-1]
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      name.split('''.'''      )
 SCREAMING_SNAKE_CASE__:   Dict=      int(items[0]      )
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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__:   Tuple=      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__:   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:
   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.'      )
   SCREAMING_SNAKE_CASE__:   Dict=      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.'      )
   SCREAMING_SNAKE_CASE__:   Tuple=      value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
 else:
  unused_weights.append(_SCREAMING_SNAKE_CASE      )







@torch.no_grad()
def 			A__      (	snake_case_ :		int       ,				snake_case_ :		Union[str, Any]       ,				snake_case_ :		int=None       ,				snake_case_ :		Dict=None       ,				snake_case_ :		Any=True      ):



 if config_path is not None:
  SCREAMING_SNAKE_CASE__:   List[Any]=      WavaVecaConformerConfig.from_pretrained(_SCREAMING_SNAKE_CASE       ,				hidden_act='''swish'''      )
 else:
  SCREAMING_SNAKE_CASE__:   Dict=      WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  SCREAMING_SNAKE_CASE__:   Dict=      '''rotary'''

 if is_finetuned:
  if dict_path:
   SCREAMING_SNAKE_CASE__:   Dict=      Dictionary.load(_SCREAMING_SNAKE_CASE      )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   SCREAMING_SNAKE_CASE__:   int=      target_dict.pad_index
   SCREAMING_SNAKE_CASE__:   Any=      target_dict.bos_index
   SCREAMING_SNAKE_CASE__:   Any=      target_dict.eos_index
   SCREAMING_SNAKE_CASE__:   Tuple=      len(target_dict.symbols      )
   SCREAMING_SNAKE_CASE__:   Dict=      os.path.join(_SCREAMING_SNAKE_CASE       ,				'''vocab.json'''      )
   if not os.path.isdir(_SCREAMING_SNAKE_CASE      ):
    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_SCREAMING_SNAKE_CASE      )      )
    return
   os.makedirs(_SCREAMING_SNAKE_CASE       ,				exist_ok=_SCREAMING_SNAKE_CASE      )
   SCREAMING_SNAKE_CASE__:   str=      target_dict.indices

   # fairseq has the <pad> and <s> switched
   SCREAMING_SNAKE_CASE__:   Dict=      0
   SCREAMING_SNAKE_CASE__:   Dict=      1
   with open(_SCREAMING_SNAKE_CASE       ,				'''w'''       ,				encoding='''utf-8'''      ) as vocab_handle:
    json.dump(_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE      )
   SCREAMING_SNAKE_CASE__:   List[str]=      WavaVecaCTCTokenizer(
       _SCREAMING_SNAKE_CASE       ,				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=_SCREAMING_SNAKE_CASE       ,				)
   SCREAMING_SNAKE_CASE__:   Union[str, Any]=      True if config.feat_extract_norm == '''layer''' else False
   SCREAMING_SNAKE_CASE__:   List[Any]=      WavaVecaFeatureExtractor(
       feature_size=1       ,				sampling_rate=16_000       ,				padding_value=0       ,				do_normalize=_SCREAMING_SNAKE_CASE       ,				return_attention_mask=_SCREAMING_SNAKE_CASE       ,				)
   SCREAMING_SNAKE_CASE__:   Any=      WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE       ,				tokenizer=_SCREAMING_SNAKE_CASE      )
   processor.save_pretrained(_SCREAMING_SNAKE_CASE      )

  SCREAMING_SNAKE_CASE__:   Optional[Any]=      WavaVecaConformerForCTC(_SCREAMING_SNAKE_CASE      )
 else:
  SCREAMING_SNAKE_CASE__:   Dict=      WavaVecaConformerForPreTraining(_SCREAMING_SNAKE_CASE      )

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

  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,				task=_SCREAMING_SNAKE_CASE      )

 SCREAMING_SNAKE_CASE__:   List[str]=      model[0].eval()

 recursively_load_weights(_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				not is_finetuned      )

 hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      lowercase_     :   Dict 				=				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'
      )
      lowercase_     :   Optional[Any] 				=				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
      )


def 			A__      (	snake_case_ :		str      ):
 if not head:
  return True
 # split the list to two parts
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      head.next, head
 while fast and fast.next:
  SCREAMING_SNAKE_CASE__:   Dict=      fast.next.next
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      slow.next
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      slow.next
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None  # Don't forget here! But forget still works!
 # reverse the second part
 SCREAMING_SNAKE_CASE__:   Optional[int]=      None
 while second:
  SCREAMING_SNAKE_CASE__:   Any=      second.next
  SCREAMING_SNAKE_CASE__:   int=      node
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      second
  SCREAMING_SNAKE_CASE__:   Any=      nxt
 # compare two parts
 # second part has the same or one less node
 while node:
  if node.val != head.val:
   return False
  SCREAMING_SNAKE_CASE__:   Tuple=      node.next
  SCREAMING_SNAKE_CASE__:   Optional[int]=      head.next
 return True







def 			A__      (	snake_case_ :		Optional[Any]      ):
 if not head or not head.next:
  return True

 # 1. Get the midpoint (slow)
 SCREAMING_SNAKE_CASE__:   List[Any]=      head
 while fast and fast.next:
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Any=      fast.next.next, slow.next

 # 2. Push the second half into the stack
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      [slow.val]
 while slow.next:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      slow.next
  stack.append(slow.val      )

 # 3. Comparison
 while stack:
  if stack.pop() != cur.val:
   return False
  SCREAMING_SNAKE_CASE__:   Tuple=      cur.next

 return True







def 			A__      (	snake_case_ :		Any      ):
 if not head or not head.next:
  return True
 SCREAMING_SNAKE_CASE__:   Optional[int]=      {}
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      0
 while head:
  if head.val in d:
   d[head.val].append(snake_case_      )
  else:
   SCREAMING_SNAKE_CASE__:   Optional[int]=      [pos]
  SCREAMING_SNAKE_CASE__:   Dict=      head.next
  pos += 1
 SCREAMING_SNAKE_CASE__:   Dict=      pos - 1
 SCREAMING_SNAKE_CASE__:   str=      0
 for v in d.values():
  if len(snake_case_      ) % 2 != 0:
   middle += 1
  else:
   SCREAMING_SNAKE_CASE__:   List[Any]=      0
   for i in range(0       ,				len(snake_case_      )      ):
    if v[i] + v[len(snake_case_      ) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True

