
"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase=0    ):
   # Format the message.
   if name is None:
      UpperCAmelCase_   :   str     	=       None
   else:
      UpperCAmelCase_   :   Tuple     	=       "." * max(0,						spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
      UpperCAmelCase_   :   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       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   # 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.
   UpperCAmelCase_   :   Any     	=       param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   UpperCAmelCase_   :   List[Any]     	=       (num_heads, num_splits, hidden_size) + input_shape[1:]
   UpperCAmelCase_   :   List[Any]     	=       param.view(*__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       param.transpose(0,						1    ).contiguous()
   UpperCAmelCase_   :   Union[str, Any]     	=       param.view(*__lowerCamelCase    )
   return param







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Dict     	=       config.n_positions
   UpperCAmelCase_   :   Optional[int]     	=       config.n_layer
   UpperCAmelCase_   :   Any     	=       config.vocab_size
   UpperCAmelCase_   :   Optional[Any]     	=       config.n_head
   UpperCAmelCase_   :   Optional[int]     	=       config.n_embd // config.n_head

   UpperCAmelCase_   :   Union[str, Any]     	=       sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
   UpperCAmelCase_   :   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"],
   }

   UpperCAmelCase_   :   Union[str, Any]     	=       "model.language_model.encoder.layers."
   for i in range(__lowerCamelCase    ):
      UpperCAmelCase_   :   Tuple     	=       torch.tril(torch.ones((n_positions, n_positions),						dtype=torch.bool    )    )
      UpperCAmelCase_   :   Optional[int]     	=       causal_mask.view(1,						1,						__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Any     	=       causal_mask
      UpperCAmelCase_   :   List[Any]     	=       torch.tensor(-1E4,						dtype=torch.bfloataa    )

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

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

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

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

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

   return sd_hf







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   int     	=       6_4000
   UpperCAmelCase_   :   List[Any]     	=       config_megatron["encoder_seq_length"]
   UpperCAmelCase_   :   Dict     	=       config_megatron["hidden_size"]
   UpperCAmelCase_   :   List[Any]     	=       config_megatron["num_layers"]
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["num_attention_heads"]
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["ffn_hidden_size"]
   UpperCAmelCase_   :   List[str]     	=       "gelu"
   UpperCAmelCase_   :   Tuple     	=       0.1
   UpperCAmelCase_   :   Any     	=       0.1
   UpperCAmelCase_   :   Dict     	=       0.1
   UpperCAmelCase_   :   Optional[Any]     	=       config_megatron["layernorm_epsilon"]  # 1e-5
   UpperCAmelCase_   :   Dict     	=       config_megatron["init_method_std"]  # 0.02
   UpperCAmelCase_   :   Optional[int]     	=       config_megatron["apply_query_key_layer_scaling"]  # True
   UpperCAmelCase_   :   List[Any]     	=       True
   UpperCAmelCase_   :   str     	=       True

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

   return config_hf







def       __a	(						__lowerCamelCase    ):
   print(__lowerCamelCase    )

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

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

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

   UpperCAmelCase_   :   int     	=       checkpoint["state_dict"]

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

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

   UpperCAmelCase_   :   Tuple     	=       "GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
    _a									=				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')
    _a									=				parser.parse_args()
    main(_args)









import argparse

import torch

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


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





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					_snake_case						=					WavLMForXVector.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['connector.weight']
					_snake_case						=					downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_snake_case						=					downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										_snake_case						=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					_snake_case						=					downstream_dict['objective.W']
					return model





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

					_snake_case						=					hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'    ):
										_snake_case						=					convert_classification(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForAudioFrameClassification'    ):
										_snake_case						=					convert_diarization(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					elif arch.endswith('ForXVector'    ):
										_snake_case						=					convert_xvector(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__    )
					else:
										raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''    )

					if hf_config.use_weighted_layer_sum:
										_snake_case						=					checkpoint['Featurizer']['weights']

					hf_feature_extractor.save_pretrained(UpperCamelCase__    )
					hf_model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
					UpperCAmelCase_   				=  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.""")
					UpperCAmelCase_   				=  parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



