




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()
_lowerCAmelCase  :      Optional[int]						=      logging.get_logger(__name__)

_lowerCAmelCase  :      Optional[Any]						=      {
    """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""",
}
_lowerCAmelCase  :      str						=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def   lowerCAmelCase						(						_lowerCAmelCase							:      Union[str, Any]		):




 """simple docstring"""







 UpperCAmelCase__							       =  {}
 with open(_lowerCAmelCase  ,							"r"		) as file:
  for line_number, line in enumerate(_lowerCAmelCase		):
   UpperCAmelCase__							       =  line.strip()
   if line:
    UpperCAmelCase__							       =  line.split()
    UpperCAmelCase__							       =  line_number
    UpperCAmelCase__							       =  words[0]
    UpperCAmelCase__							       =  value
 return result



def   lowerCAmelCase						(						_lowerCAmelCase							:      Dict  ,							_lowerCAmelCase							:      List[str]  ,							_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      List[Any]		):




 """simple docstring"""







 for attribute in key.split("."		):
  UpperCAmelCase__							       =  getattr(_lowerCAmelCase  ,							_lowerCAmelCase		)

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

 if weight_type is not None and weight_type != "param":
  UpperCAmelCase__							       =  getattr(_lowerCAmelCase  ,							_lowerCAmelCase		).shape
 elif weight_type is not None and weight_type == "param":
  UpperCAmelCase__							       =  hf_pointer
  for attribute in hf_param_name.split("."		):
   UpperCAmelCase__							       =  getattr(_lowerCAmelCase  ,							_lowerCAmelCase		)
  UpperCAmelCase__							       =  shape_pointer.shape

  # let's reduce dimension
  UpperCAmelCase__							       =  value[0]
 else:
  UpperCAmelCase__							       =  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":
  UpperCAmelCase__							       =  value
 elif weight_type == "weight_g":
  UpperCAmelCase__							       =  value
 elif weight_type == "weight_v":
  UpperCAmelCase__							       =  value
 elif weight_type == "bias":
  UpperCAmelCase__							       =  value
 elif weight_type == "param":
  for attribute in hf_param_name.split("."		):
   UpperCAmelCase__							       =  getattr(_lowerCAmelCase  ,							_lowerCAmelCase		)
  UpperCAmelCase__							       =  value
 else:
  UpperCAmelCase__							       =  value

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      int  ,							_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Optional[Any]  ,							_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      List[Any]		):




 """simple docstring"""







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

 if weight_type is not None and weight_type != "param":
  UpperCAmelCase__							       =  ".".join([key, weight_type]		)
 elif weight_type is not None and weight_type == "param":
  UpperCAmelCase__							       =  ".".join([key, hf_param_name]		)
 else:
  UpperCAmelCase__							       =  key

 UpperCAmelCase__							       =  value if "lm_head" in full_key else value[0]


_lowerCAmelCase  :      int						=      {
    """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   lowerCAmelCase						(						_lowerCAmelCase							:      Optional[Any]  ,							_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      Optional[int]=None  ,							_lowerCAmelCase							:      Optional[int]=None		):




 """simple docstring"""







 UpperCAmelCase__							       =  False
 for key, mapped_key in MAPPING.items():
  UpperCAmelCase__							       =  "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]:
   UpperCAmelCase__							       =  True
   if "*" in mapped_key:
    UpperCAmelCase__							       =  name.split(_lowerCAmelCase		)[0].split("."		)[-2]
    UpperCAmelCase__							       =  mapped_key.replace("*"  ,							_lowerCAmelCase		)
   if "weight_g" in name:
    UpperCAmelCase__							       =  "weight_g"
   elif "weight_v" in name:
    UpperCAmelCase__							       =  "weight_v"
   elif "bias" in name:
    UpperCAmelCase__							       =  "bias"
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    UpperCAmelCase__							       =  "weight"
   else:
    UpperCAmelCase__							       =  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   lowerCAmelCase						(						_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Optional[int]		):




 """simple docstring"""







 UpperCAmelCase__							       =  []
 UpperCAmelCase__							       =  fairseq_model.state_dict()

 UpperCAmelCase__							       =  hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase__							       =  False
  if "conv_layers" in name:
   load_conv_layer(
       _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							hf_model.config.feat_extract_norm == "group"  ,							)
   UpperCAmelCase__							       =  True
  else:
   UpperCAmelCase__							       =  load_wavaveca_layer(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)
  if not is_used:
   unused_weights.append(_lowerCAmelCase		)

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      str  ,							_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      Dict  ,							_lowerCAmelCase							:      Dict  ,							_lowerCAmelCase							:      Union[str, Any]		):




 """simple docstring"""







 UpperCAmelCase__							       =  full_name.split("conv_layers."		)[-1]
 UpperCAmelCase__							       =  name.split("."		)
 UpperCAmelCase__							       =  int(items[0]		)
 UpperCAmelCase__							       =  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.'''		)
   UpperCAmelCase__							       =  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.'''		)
   UpperCAmelCase__							       =  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.'''		)
   UpperCAmelCase__							       =  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.'''		)
   UpperCAmelCase__							       =  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   lowerCAmelCase						(						_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      str  ,							_lowerCAmelCase							:      Optional[int]=None  ,							_lowerCAmelCase							:      Optional[Any]=None  ,							_lowerCAmelCase							:      List[Any]=True  ,							_lowerCAmelCase							:      int=False		):




 """simple docstring"""







 if config_path is not None:
  UpperCAmelCase__							       =  WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
 else:
  UpperCAmelCase__							       =  WavaVecaConfig()

 if is_seq_class:
  UpperCAmelCase__							       =  read_txt_into_dict(_lowerCAmelCase		)
  UpperCAmelCase__							       =  idalabel
  UpperCAmelCase__							       =  WavaVecaForSequenceClassification(_lowerCAmelCase		)
  UpperCAmelCase__							       =  WavaVecaFeatureExtractor(
      feature_size=1  ,							sampling_rate=1_6000  ,							padding_value=0  ,							do_normalize=_lowerCAmelCase  ,							return_attention_mask=_lowerCAmelCase  ,							)
  feature_extractor.save_pretrained(_lowerCAmelCase		)

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

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

   # fairseq has the <pad> and <s> switched
   UpperCAmelCase__							       =  0
   UpperCAmelCase__							       =  1
   with open(_lowerCAmelCase  ,							"w"  ,							encoding="utf-8"		) as vocab_handle:
    json.dump(_lowerCAmelCase  ,							_lowerCAmelCase		)
   UpperCAmelCase__							       =  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  ,							)
   UpperCAmelCase__							       =  True if config.feat_extract_norm == "layer" else False
   UpperCAmelCase__							       =  WavaVecaFeatureExtractor(
       feature_size=1  ,							sampling_rate=1_6000  ,							padding_value=0  ,							do_normalize=_lowerCAmelCase  ,							return_attention_mask=_lowerCAmelCase  ,							)
   UpperCAmelCase__							       =  WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,							tokenizer=_lowerCAmelCase		)
   processor.save_pretrained(_lowerCAmelCase		)

  UpperCAmelCase__							       =  WavaVecaForCTC(_lowerCAmelCase		)
 else:
  UpperCAmelCase__							       =  WavaVecaForPreTraining(_lowerCAmelCase		)

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

  UpperCAmelCase__							       =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,							task=_lowerCAmelCase		)

 UpperCAmelCase__							       =  model[0].eval()

 recursively_load_weights(_lowerCAmelCase  ,							_lowerCAmelCase  ,							not is_finetuned		)

 hf_wavavec.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
  _lowerCAmelCase  :      Any						=      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",
  )
  _lowerCAmelCase  :      Union[str, Any]						=      parser.parse_args()

  _lowerCAmelCase  :      Optional[Any]						=      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,
  )












from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = "arrow"		,      **lowerCAmelCase__		,      )   ->						Optional[int]:



        '''simple docstring'''




        super().__init__(
            split=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      keep_in_memory=lowerCAmelCase__		,      streaming=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					int										=load_from_cache_file
        a__	:					Tuple										=file_format
        a__	:					List[Any]										=Spark(
            df=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      working_dir=lowerCAmelCase__		,      **lowerCAmelCase__		,      )







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        if self.streaming:
          return self.builder.as_streaming_dataset(split=self.split							)
        a__	:					str										=None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowerCAmelCase__		,      file_format=self._file_format		,      )
        return self.builder.as_dataset(split=self.split							)






