"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_a   :	Union[str, Any] 					=     logging.get_logger(__name__)

_a   :	Tuple 					=     {
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
_a   :	Dict 					=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict			) ->   List[str]:
  _lowerCAmelCase		: Any						    =  {}
  with open(_lowerCamelCase    ,"""r"""			) as file:
    for line_number, line in enumerate(_lowerCamelCase			):
      _lowerCAmelCase		: List[str]						    =  line.strip()
      if line:
        _lowerCAmelCase		: Dict						    =  line.split()
        _lowerCAmelCase		: List[str]						    =  line_number
        _lowerCAmelCase		: Tuple						    =  words[0]
        _lowerCAmelCase		: List[str]						    =  value
  return result





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: int    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Any			) ->   Dict:
  for attribute in key.split("""."""			):
    _lowerCAmelCase		: Dict						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			)

  _lowerCAmelCase		: str						    =  None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(_lowerCamelCase			):
      _lowerCAmelCase		: str						    =  PARAM_MAPPING[full_name.split("""."""			)[-1]]
      _lowerCAmelCase		: str						    =  """param"""

  if weight_type is not None and weight_type != "param":
    _lowerCAmelCase		: List[Any]						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			).shape
  elif weight_type is not None and weight_type == "param":
    _lowerCAmelCase		: List[str]						    =  hf_pointer
    for attribute in hf_param_name.split("""."""			):
      _lowerCAmelCase		: Any						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			)
    _lowerCAmelCase		: Union[str, Any]						    =  shape_pointer.shape

    # let's reduce dimension
    _lowerCAmelCase		: List[Any]						    =  value[0]
  else:
    _lowerCAmelCase		: Any						    =  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		: int						    =  value
  elif weight_type == "weight_g":
    _lowerCAmelCase		: Dict						    =  value
  elif weight_type == "weight_v":
    _lowerCAmelCase		: int						    =  value
  elif weight_type == "bias":
    _lowerCAmelCase		: Union[str, Any]						    =  value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""			):
      _lowerCAmelCase		: str						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			)
    _lowerCAmelCase		: Optional[Any]						    =  value
  else:
    _lowerCAmelCase		: str						    =  value

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[str]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: int			) ->   Optional[int]:
  _lowerCAmelCase		: str						    =  None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(_lowerCamelCase			):
      _lowerCAmelCase		: List[Any]						    =  PARAM_MAPPING[full_name.split("""."""			)[-1]]
      _lowerCAmelCase		: Union[str, Any]						    =  """param"""

  if weight_type is not None and weight_type != "param":
    _lowerCAmelCase		: Tuple						    =  """.""".join([key, weight_type]			)
  elif weight_type is not None and weight_type == "param":
    _lowerCAmelCase		: Union[str, Any]						    =  """.""".join([key, hf_param_name]			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  key

  _lowerCAmelCase		: List[Any]						    =  value if """lm_head""" in full_key else value[0]


_a   :	Union[str, Any] 					=     {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: List[Any]=None    ,_lowerCamelCase		: Union[str, Any]=None			) ->   Any:
  _lowerCAmelCase		: Any						    =  False
  for key, mapped_key in MAPPING.items():
    _lowerCAmelCase		: Optional[int]						    =  """wav2vec2.""" + 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		: Optional[int]						    =  True
      if "*" in mapped_key:
        _lowerCAmelCase		: List[Any]						    =  name.split(_lowerCamelCase			)[0].split("""."""			)[-2]
        _lowerCAmelCase		: Dict						    =  mapped_key.replace("""*"""    ,_lowerCamelCase			)
      if "weight_g" in name:
        _lowerCAmelCase		: List[str]						    =  """weight_g"""
      elif "weight_v" in name:
        _lowerCAmelCase		: int						    =  """weight_v"""
      elif "bias" in name:
        _lowerCAmelCase		: Optional[Any]						    =  """bias"""
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _lowerCAmelCase		: List[str]						    =  """weight"""
      else:
        _lowerCAmelCase		: Optional[Any]						    =  None
      if hf_dict is not None:
        rename_dict(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
      else:
        set_recursively(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
      return is_used
  return is_used





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Optional[int]			) ->   Optional[Any]:
  _lowerCAmelCase		: Any						    =  []
  _lowerCAmelCase		: str						    =  fairseq_model.state_dict()

  _lowerCAmelCase		: Optional[Any]						    =  hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase		: Optional[int]						    =  False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,hf_model.config.feat_extract_norm == """group"""    ,)
      _lowerCAmelCase		: Union[str, Any]						    =  True
    else:
      _lowerCAmelCase		: Optional[int]						    =  load_wavaveca_layer(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
    if not is_used:
      unused_weights.append(_lowerCamelCase			)

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





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





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: str=None    ,_lowerCamelCase		: Union[str, Any]=None    ,_lowerCamelCase		: Optional[Any]=True    ,_lowerCamelCase		: List[Any]=False			) ->   Union[str, Any]:
  if config_path is not None:
    _lowerCAmelCase		: Tuple						    =  WavaVecaConfig.from_pretrained(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: List[Any]						    =  WavaVecaConfig()

  if is_seq_class:
    _lowerCAmelCase		: str						    =  read_txt_into_dict(_lowerCamelCase			)
    _lowerCAmelCase		: Optional[Any]						    =  idalabel
    _lowerCAmelCase		: Optional[int]						    =  WavaVecaForSequenceClassification(_lowerCamelCase			)
    _lowerCAmelCase		: List[Any]						    =  WavaVecaFeatureExtractor(
        feature_size=1    ,sampling_rate=16000    ,padding_value=0    ,do_normalize=_lowerCamelCase    ,return_attention_mask=_lowerCamelCase    ,)
    feature_extractor.save_pretrained(_lowerCamelCase			)

  elif is_finetuned:
    if dict_path:
      _lowerCAmelCase		: Tuple						    =  Dictionary.load(_lowerCamelCase			)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowerCAmelCase		: Optional[Any]						    =  target_dict.pad_index
      _lowerCAmelCase		: str						    =  target_dict.bos_index
      _lowerCAmelCase		: Dict						    =  target_dict.eos_index
      _lowerCAmelCase		: List[Any]						    =  len(target_dict.symbols			)
      _lowerCAmelCase		: Tuple						    =  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			)
      _lowerCAmelCase		: List[Any]						    =  target_dict.indices

      # fairseq has the <pad> and <s> switched
      _lowerCAmelCase		: Optional[int]						    =  0
      _lowerCAmelCase		: List[str]						    =  1
      with open(_lowerCamelCase    ,"""w"""    ,encoding="""utf-8"""			) as vocab_handle:
        json.dump(_lowerCamelCase    ,_lowerCamelCase			)
      _lowerCAmelCase		: List[Any]						    =  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    ,)
      _lowerCAmelCase		: List[str]						    =  True if config.feat_extract_norm == """layer""" else False
      _lowerCAmelCase		: Any						    =  WavaVecaFeatureExtractor(
          feature_size=1    ,sampling_rate=16000    ,padding_value=0    ,do_normalize=_lowerCamelCase    ,return_attention_mask=_lowerCamelCase    ,)
      _lowerCAmelCase		: Dict						    =  WavaVecaProcessor(feature_extractor=_lowerCamelCase    ,tokenizer=_lowerCamelCase			)
      processor.save_pretrained(_lowerCamelCase			)

    _lowerCAmelCase		: int						    =  WavaVecaForCTC(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: str						    =  WavaVecaForPreTraining(_lowerCamelCase			)

  if is_finetuned or is_seq_class:
    _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			)}			)
  else:
    _lowerCAmelCase		: List[Any]						    =  argparse.Namespace(task="""audio_pretraining"""			)
    _lowerCAmelCase		: Tuple						    =  fairseq.tasks.setup_task(_lowerCamelCase			)

    _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,task=_lowerCamelCase			)

  _lowerCAmelCase		: List[Any]						    =  model[0].eval()

  recursively_load_weights(_lowerCamelCase    ,_lowerCamelCase    ,not is_finetuned			)

  hf_wavavec.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
     _a   :	Tuple 					=     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'
     )
     parser.add_argument(
         '--is_seq_class',
         action='store_true',
         help='Whether the model to convert is a fine-tuned sequence classification model or not',
     )
     _a   :	Tuple 					=     parser.parse_args()

     _a   :	str 					=     not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )





"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  *a__       ,  **a__						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""       ,  a__       ,  )
       super().__init__(*a__       ,  **a__						)




