

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase__ :     Dict				    =	logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =	{
    """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""",
}
UpperCAmelCase__ :     Any				    =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]






def 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    _A			)     ->						Optional[int]:
      for attribute in key.split("""."""			):
            SCREAMING_SNAKE_CASE :      List[str]       =   getattr(lowerCamelCase_						,    lowerCamelCase_			)

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

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






def 					__lowercase      (     _A						,    _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Dict       =   []
      SCREAMING_SNAKE_CASE :      str       =   fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE :      List[str]       =   hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE :      List[str]       =   False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    hf_model.config.feat_extract_norm == """group"""						,    )
                  SCREAMING_SNAKE_CASE :      List[Any]       =   True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   """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
                              SCREAMING_SNAKE_CASE :      Optional[Any]       =   True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE :      str       =   name.split(lowerCamelCase_			)[0].split("""."""			)[-2]
                                    SCREAMING_SNAKE_CASE :      Tuple       =   mapped_key.replace("""*"""						,    lowerCamelCase_			)
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE :      Optional[int]       =   """weight_g"""
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE :      Tuple       =   """weight_v"""
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE :      int       =   """bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    SCREAMING_SNAKE_CASE :      str       =   """weight"""
                              else:
                                    SCREAMING_SNAKE_CASE :      Tuple       =   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 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      int       =   full_name.split("""conv_layers."""			)[-1]
      SCREAMING_SNAKE_CASE :      Optional[int]       =   name.split("""."""			)
      SCREAMING_SNAKE_CASE :      List[str]       =   int(items[0]			)
      SCREAMING_SNAKE_CASE :      Dict       =   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 :      Union[str, 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."			)
                  SCREAMING_SNAKE_CASE :      str       =   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."			)
                  SCREAMING_SNAKE_CASE :      Any       =   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."			)
                  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(lowerCamelCase_			)






@torch.no_grad()
def 					__lowercase      (     _A						,    _A						,    _A=None						,    _A=None						,    _A=True			)     ->						List[str]:
      if config_path is not None:
            SCREAMING_SNAKE_CASE :      Dict       =   UniSpeechSatConfig.from_pretrained(lowerCamelCase_			)
      else:
            SCREAMING_SNAKE_CASE :      int       =   UniSpeechSatConfig()

      SCREAMING_SNAKE_CASE :      Optional[int]       =   """"""

      if is_finetuned:
            SCREAMING_SNAKE_CASE :      int       =   UniSpeechSatForCTC(lowerCamelCase_			)
      else:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   UniSpeechSatForPreTraining(lowerCamelCase_			)

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

      recursively_load_weights(lowerCamelCase_						,    lowerCamelCase_			)

      hf_wavavec.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
  UpperCAmelCase__ :     Optional[int]				    =	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"""
  )
  UpperCAmelCase__ :     str				    =	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :     Dict				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       ="""gpt_neox_japanese"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : Any=3_2_0_0_0				,				UpperCAmelCase__    : Dict=2_5_6_0				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3_2				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : List[str]="gelu"				,				UpperCAmelCase__    : Optional[int]=1.00				,				UpperCAmelCase__    : List[Any]=1_0_0_0_0				,				UpperCAmelCase__    : Tuple=2_0_4_8				,				UpperCAmelCase__    : Optional[int]=0.02				,				UpperCAmelCase__    : Dict=1e-5				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Tuple=3_1_9_9_6				,				UpperCAmelCase__    : Tuple=3_1_9_9_9				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : Union[str, Any]=0.0				,				**UpperCAmelCase__    : Optional[Any]				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(bos_token_id=UpperCAmelCase__				,				eos_token_id=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[str]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Any       =   intermediate_multiple_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_act
        SCREAMING_SNAKE_CASE :      Any       =   rotary_pct
        SCREAMING_SNAKE_CASE :      Tuple       =   rotary_emb_base
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      str       =   use_cache
        SCREAMING_SNAKE_CASE :      Any       =   attention_dropout
        SCREAMING_SNAKE_CASE :      str       =   hidden_dropout




