


'''simple docstring'''


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=0  ):







				"""simple docstring"""





				if name is None:
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   None
				else:
								_SCREAMING_SNAKE_CASE				: int  	=   """.""" * max(0							,   spaces - 2  ) + """# {:""" + str(50 - spaces  ) + """s}"""
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   fmt.format(SCREAMING_SNAKE_CASE__  )

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[str]  	=   param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				_SCREAMING_SNAKE_CASE				: Tuple  	=   (num_heads, num_splits, hidden_size) + input_shape[1:]
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   param.view(*SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   param.transpose(0							,   1  ).contiguous()
				_SCREAMING_SNAKE_CASE				: List[str]  	=   param.view(*SCREAMING_SNAKE_CASE__  )
				return param







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   config.n_positions
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   config.n_layer
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   config.vocab_size
				_SCREAMING_SNAKE_CASE				: Tuple  	=   config.n_head
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   config.n_embd // config.n_head

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
				_SCREAMING_SNAKE_CASE				: Tuple  	=   {
				    """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				: Dict  	=   """model.language_model.encoder.layers."""
				for i in range(SCREAMING_SNAKE_CASE__  ):
								_SCREAMING_SNAKE_CASE				: Tuple  	=   torch.tril(torch.ones((n_positions, n_positions)							,   dtype=torch.bool  )  )
								_SCREAMING_SNAKE_CASE				: Any  	=   causal_mask.view(1							,   1							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								_SCREAMING_SNAKE_CASE				: str  	=   causal_mask
								_SCREAMING_SNAKE_CASE				: Dict  	=   torch.tensor(-1E4							,   dtype=torch.bfloataa  )

								_SCREAMING_SNAKE_CASE				: str  	=   sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

								_SCREAMING_SNAKE_CASE				: Dict  	=   sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
								_SCREAMING_SNAKE_CASE				: Dict  	=   fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__							,   3							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   vala.transpose(0							,   1  ).contiguous()

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

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

				return sd_hf







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   6_4000
				_SCREAMING_SNAKE_CASE				: str  	=   config_megatron["""encoder_seq_length"""]
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   config_megatron["""hidden_size"""]
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   config_megatron["""num_layers"""]
				_SCREAMING_SNAKE_CASE				: str  	=   config_megatron["""num_attention_heads"""]
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   config_megatron["""ffn_hidden_size"""]
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   """gelu"""
				_SCREAMING_SNAKE_CASE				: List[str]  	=   0.1
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   0.1
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   0.1
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   config_megatron["""layernorm_epsilon"""]  # 1e-5
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   config_megatron["""init_method_std"""]  # 0.02
				_SCREAMING_SNAKE_CASE				: Tuple  	=   config_megatron["""apply_query_key_layer_scaling"""]  # True
				_SCREAMING_SNAKE_CASE				: Tuple  	=   True
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   True

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

				return config_hf







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				print(SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Any  	=   args.checkpoint_path
				_SCREAMING_SNAKE_CASE				: Tuple  	=   args.save_path
				if isfile(SCREAMING_SNAKE_CASE__  ):
								raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""  )

				# Load the model.
				_SCREAMING_SNAKE_CASE				: Dict  	=   torch.load(SCREAMING_SNAKE_CASE__							,   map_location="""cpu"""  )

				# Load the config.
				_SCREAMING_SNAKE_CASE				: Tuple  	=   checkpoint["""hyper_parameters"""]["""cfg"""]
				_SCREAMING_SNAKE_CASE				: str  	=   GPTaConfig()
				_SCREAMING_SNAKE_CASE				: str  	=   copy_config(config_hf=SCREAMING_SNAKE_CASE__							,   config_megatron=SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Any  	=   ["""GPT2LMHeadModel"""]

				_SCREAMING_SNAKE_CASE				: List[str]  	=   checkpoint["""state_dict"""]

				# Convert.
				print("""Converting"""  )
				_SCREAMING_SNAKE_CASE				: int  	=   convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

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

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   """GPTSw3Tokenizer"""

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

				# Store the state_dict to file.
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   os.path.join(SCREAMING_SNAKE_CASE__							,   """pytorch_model.bin"""  )
				print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""  )
				torch.save(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )


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_	:     Dict							     =	parser.parse_args()
				main(_args)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_	:     Any							     =	{
    'configuration_musicgen': [
        'MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MusicgenConfig',
        'MusicgenDecoderConfig',
    ],
    'processing_musicgen': ['MusicgenProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MusicgenForConditionalGeneration',
				    'MusicgenForCausalLM',
				    'MusicgenModel',
				    'MusicgenPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_musicgen import (
				    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MusicgenConfig,
				    MusicgenDecoderConfig,
				)
				from .processing_musicgen import MusicgenProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_musicgen import (
								    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MusicgenForCausalLM,
								    MusicgenForConditionalGeneration,
								    MusicgenModel,
								    MusicgenPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     Tuple							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




