




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

_lowerCAmelCase  :      Union[str, 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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
_lowerCAmelCase  :      List[Any]						=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]



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




 """simple docstring"""







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

 if weight_type is not None:
  UpperCAmelCase__							       =  getattr(_lowerCAmelCase  ,							_lowerCAmelCase		).shape
 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
 else:
  UpperCAmelCase__							       =  value

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Any		):




 """simple docstring"""







 UpperCAmelCase__							       =  []
 UpperCAmelCase__							       =  fairseq_model.state_dict()

 UpperCAmelCase__							       =  hf_model.unispeech_sat.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:
   for key, mapped_key in MAPPING.items():
    UpperCAmelCase__							       =  "unispeech_sat." + 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]:
     if "layer_norm_for_extract" in name and (".".join(name.split("."		)[:-1]		) != key):
      # special case since naming is very similar
      continue
     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
     set_recursively(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)
    continue
  if not is_used:
   unused_weights.append(_lowerCAmelCase		)

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



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




 """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[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[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							:      Optional[Any]  ,							_lowerCAmelCase							:      List[Any]  ,							_lowerCAmelCase							:      List[Any]=None  ,							_lowerCAmelCase							:      Optional[int]=None  ,							_lowerCAmelCase							:      Any=True		):




 """simple docstring"""







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

 UpperCAmelCase__							       =  ""

 if is_finetuned:
  UpperCAmelCase__							       =  UniSpeechSatForCTC(_lowerCAmelCase		)
 else:
  UpperCAmelCase__							       =  UniSpeechSatForPreTraining(_lowerCAmelCase		)

 UpperCAmelCase__							       =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]  ,							arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
 UpperCAmelCase__							       =  model[0].eval()

 recursively_load_weights(_lowerCAmelCase  ,							_lowerCAmelCase		)

 hf_wavavec.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
  _lowerCAmelCase  :      Union[str, 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"
  )
  _lowerCAmelCase  :      Union[str, Any]						=      parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )












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							)






