

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def        __lowercase   (  _A    ,     _A    ,     _A=0      )  ->    Optional[Any]:
      # Format the message.
      if name is None:
            SCREAMING_SNAKE_CASE			:   str	  = None
      else:
            SCREAMING_SNAKE_CASE			:   Tuple	  = "." * max(0    ,     spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = fmt.format(__lowerCamelCase      )

      # Print and recurse (if needed).
      if isinstance(__lowerCamelCase    ,     __lowerCamelCase      ):
            if msg is not None:
                  print(__lowerCamelCase      )
            for k in val.keys():
                  recursive_print(__lowerCamelCase    ,     val[k]    ,     spaces + 2      )
      elif isinstance(__lowerCamelCase    ,     torch.Tensor      ):
            print(__lowerCamelCase    ,     """:"""    ,     val.size()      )
      else:
            print(__lowerCamelCase    ,     """:"""    ,     __lowerCamelCase      )






def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A      )  ->    Optional[int]:
      # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
      # for compatibility with later versions of NVIDIA Megatron-LM.
      # The inverse operation is performed inside Megatron-LM to read checkpoints:
      # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
      # If param is the weight tensor of the self-attention block, the returned tensor
      # will have to be transposed one more time to be read by HuggingFace GPT2.
      SCREAMING_SNAKE_CASE			:   Any	  = param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      SCREAMING_SNAKE_CASE			:   List[Any]	  = (num_heads, num_splits, hidden_size) + input_shape[1:]
      SCREAMING_SNAKE_CASE			:   List[Any]	  = param.view(*__lowerCamelCase      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = param.transpose(0    ,     1      ).contiguous()
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = param.view(*__lowerCamelCase      )
      return param






def        __lowercase   (  _A    ,     _A      )  ->    List[str]:
      SCREAMING_SNAKE_CASE			:   Dict	  = config.n_positions
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = config.n_layer
      SCREAMING_SNAKE_CASE			:   Any	  = config.vocab_size
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = config.n_head
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = config.n_embd // config.n_head

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = {
          "transformer.wte.weight": word_embeddings,
          "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
          "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
          "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
      }

      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = "model.language_model.encoder.layers."
      for i in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE			:   Tuple	  = torch.tril(torch.ones((n_positions, n_positions)    ,     dtype=torch.bool      )      )
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = causal_mask.view(1    ,     1    ,     __lowerCamelCase    ,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE			:   Any	  = causal_mask
            SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.tensor(-1e4    ,     dtype=torch.bfloataa      )

            SCREAMING_SNAKE_CASE			:   List[str]	  = sd_megatron[F"{pf}{i}.input_layernorm.weight"]
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = sd_megatron[F"{pf}{i}.input_layernorm.bias"]

            SCREAMING_SNAKE_CASE			:   List[Any]	  = sd_megatron[F"{pf}{i}.self_attention.query_key_value.weight"]
            SCREAMING_SNAKE_CASE			:   List[Any]	  = fix_query_key_value_ordering(__lowerCamelCase    ,     3    ,     __lowerCamelCase    ,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = vala.transpose(0    ,     1      ).contiguous()

            SCREAMING_SNAKE_CASE			:   Any	  = sd_megatron[F"{pf}{i}.self_attention.query_key_value.bias"]
            SCREAMING_SNAKE_CASE			:   Dict	  = fix_query_key_value_ordering(__lowerCamelCase    ,     3    ,     __lowerCamelCase    ,     __lowerCamelCase      )
            SCREAMING_SNAKE_CASE			:   List[str]	  = vala

            SCREAMING_SNAKE_CASE			:   List[Any]	  = sd_megatron[F"{pf}{i}.self_attention.dense.weight"].transpose(
                0    ,     1      )
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = sd_megatron[F"{pf}{i}.self_attention.dense.bias"]
            SCREAMING_SNAKE_CASE			:   str	  = sd_megatron[F"{pf}{i}.post_attention_layernorm.weight"]
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = sd_megatron[F"{pf}{i}.post_attention_layernorm.bias"]
            SCREAMING_SNAKE_CASE			:   List[Any]	  = sd_megatron[F"{pf}{i}.mlp.dense_h_to_4h.weight"].transpose(0    ,     1      )
            SCREAMING_SNAKE_CASE			:   Any	  = sd_megatron[F"{pf}{i}.mlp.dense_h_to_4h.bias"]
            SCREAMING_SNAKE_CASE			:   int	  = sd_megatron[F"{pf}{i}.mlp.dense_4h_to_h.weight"].transpose(
                0    ,     1      )
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = sd_megatron[F"{pf}{i}.mlp.dense_4h_to_h.bias"]

      # For LM head, transformers' wants the matrix to weight embeddings.
      SCREAMING_SNAKE_CASE			:   Tuple	  = word_embeddings

      return sd_hf






def        __lowercase   (  _A    ,     _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   int	  = 64000
      SCREAMING_SNAKE_CASE			:   List[Any]	  = config_megatron["encoder_seq_length"]
      SCREAMING_SNAKE_CASE			:   Dict	  = config_megatron["hidden_size"]
      SCREAMING_SNAKE_CASE			:   List[Any]	  = config_megatron["num_layers"]
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = config_megatron["num_attention_heads"]
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = config_megatron["ffn_hidden_size"]
      SCREAMING_SNAKE_CASE			:   List[str]	  = "gelu"
      SCREAMING_SNAKE_CASE			:   Tuple	  = 0.1
      SCREAMING_SNAKE_CASE			:   Any	  = 0.1
      SCREAMING_SNAKE_CASE			:   Dict	  = 0.1
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = config_megatron["layernorm_epsilon"]  # 1e-5
      SCREAMING_SNAKE_CASE			:   Dict	  = config_megatron["init_method_std"]  # 0.02
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = config_megatron["apply_query_key_layer_scaling"]  # True
      SCREAMING_SNAKE_CASE			:   List[Any]	  = True
      SCREAMING_SNAKE_CASE			:   str	  = True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 4096:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 1  # <|endoftext|>
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 1  # <|endoftext|>
            SCREAMING_SNAKE_CASE			:   List[Any]	  = 0  # <unk>
      else:
            SCREAMING_SNAKE_CASE			:   str	  = 2  # <s>
            SCREAMING_SNAKE_CASE			:   List[str]	  = 3  # <|endoftext|>
            SCREAMING_SNAKE_CASE			:   List[Any]	  = 0  # <pad>

      return config_hf






def        __lowercase   (  _A      )  ->    List[str]:
      print(__lowerCamelCase      )

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = args.checkpoint_path
      SCREAMING_SNAKE_CASE			:   Dict	  = args.save_path
      if isfile(__lowerCamelCase      ):
            raise FileNotFoundError(F"ERROR! could not find file {checkpoint_path}"      )

      # Load the model.
      SCREAMING_SNAKE_CASE			:   List[Any]	  = torch.load(__lowerCamelCase    ,     map_location="""cpu"""      )

      # Load the config.
      SCREAMING_SNAKE_CASE			:   List[str]	  = checkpoint["hyper_parameters"]["cfg"]
      SCREAMING_SNAKE_CASE			:   str	  = GPTaConfig()
      SCREAMING_SNAKE_CASE			:   Tuple	  = copy_config(config_hf=__lowerCamelCase    ,     config_megatron=__lowerCamelCase      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = ["GPT2LMHeadModel"]

      SCREAMING_SNAKE_CASE			:   int	  = checkpoint["state_dict"]

      # Convert.
      print("""Converting"""      )
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = convert_megatron_checkpoint(__lowerCamelCase    ,     __lowerCamelCase      )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(__lowerCamelCase    ,     __lowerCamelCase      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = "GPTSw3Tokenizer"

      # Store the config to file.
      print("""Saving config"""      )
      config_hf.save_pretrained(__lowerCamelCase      )

      # Store the state_dict to file.
      SCREAMING_SNAKE_CASE			:   Tuple	  = os.path.join(__lowerCamelCase    ,     """pytorch_model.bin"""      )
      print(F"Saving checkpoint to \"{output_checkpoint_file}\""      )
      torch.save(__lowerCamelCase    ,     __lowerCamelCase      )


if __name__ == "__main__":
  UpperCAmelCase__			:  Tuple					     =     argparse.ArgumentParser()
  parser.add_argument(
      """--checkpoint_path""",
      type=str,
      required=True,
      help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
  )
  parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
  parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
  UpperCAmelCase__			:  Optional[int]					     =     parser.parse_args()
  main(_args)






"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

   UpperCAmelCase_   :   Any     	=       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"    ):
      UpperCAmelCase_   :   Optional[int]     	=       convert_classification(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForAudioFrameClassification"    ):
      UpperCAmelCase_   :   Dict     	=       convert_diarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForXVector"    ):
      UpperCAmelCase_   :   List[Any]     	=       convert_xvector(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   else:
      raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""    )

   if hf_config.use_weighted_layer_sum:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__lowerCamelCase    )
   hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
    _a									=				argparse.ArgumentParser()
    parser.add_argument(
        '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
    )
    parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
    parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
    _a									=				parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


