






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  							=					{
    """configuration_jukebox""": [
        """JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """JukeboxConfig""",
        """JukeboxPriorConfig""",
        """JukeboxVQVAEConfig""",
    ],
    """tokenization_jukebox""": ["""JukeboxTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    """JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """JukeboxModel""",
	    """JukeboxPreTrainedModel""",
	    """JukeboxVQVAE""",
	    """JukeboxPrior""",
	]

if TYPE_CHECKING:
	from .configuration_jukebox import (
	    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    JukeboxConfig,
	    JukeboxPriorConfig,
	    JukeboxVQVAEConfig,
	)
	from .tokenization_jukebox import JukeboxTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_jukebox import (
		    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    JukeboxModel,
		    JukeboxPreTrainedModel,
		    JukeboxPrior,
		    JukeboxVQVAE,
		)

else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''




import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Dict							     =		[
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """decoder.output_projection.weight""",
							    """_float_tensor""",
							    """encoder.embed_positions._float_tensor""",
							    """decoder.embed_positions._float_tensor""",
							]
							for k in ignore_keys:
														state_dict.pop(_lowercase	,				_lowercase		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		emb.weight.shape
							UpperCAmelCase     :		Dict							     =		nn.Linear(_lowercase	,				_lowercase	,				bias=_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		emb.weight.data
							return lin_layer



def       __lowerCamelCase  (					_lowercase	,				_lowercase=None		)      ->			List[Any]:
							UpperCAmelCase     :		Dict							     =		{}
							for old_key in state_dict.keys():
														UpperCAmelCase     :		str							     =		old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												UpperCAmelCase     :		int							     =		key.replace("""moe_layer.experts.0"""	,				F'''ffn.experts.expert_{expert_idx}'''		)
																					else:
																												UpperCAmelCase     :		str							     =		key.replace("""moe_layer.experts."""	,				"""ffn.experts.expert_"""		)
														if "gate" in key:
																					UpperCAmelCase     :		str							     =		key.replace(""".moe_layer.gate.wg"""	,				""".ffn.router.classifier"""		)
														if "fc2" and "experts" not in key:
																					UpperCAmelCase     :		Tuple							     =		key.replace(""".fc2."""	,				""".ffn.fc2."""		)
														if "fc1" and "experts" not in key:
																					UpperCAmelCase     :		List[Any]							     =		key.replace(""".fc1."""	,				""".ffn.fc1."""		)
														if ".encoder_attn." in key:
																					UpperCAmelCase     :		Union[str, Any]							     =		key.replace(""".encoder_attn."""	,				""".cross_attention."""		)
														if "encoder_attn_layer_norm" in key:
																					UpperCAmelCase     :		str							     =		key.replace("""encoder_attn_layer_norm"""	,				"""cross_attention_layer_norm"""		)
														if "final_layer_norm" in key:
																					UpperCAmelCase     :		Optional[int]							     =		key.replace("""final_layer_norm"""	,				"""ff_layer_norm"""		)
														UpperCAmelCase     :		str							     =		state_dict[old_key]
							return new_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase = WEIGHTS_NAME		)      ->			Tuple:
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Dict							     =		0
							os.makedirs(_lowercase	,				exist_ok=_lowercase		)

							for expert in range(_lowercase		):
														UpperCAmelCase     :		str							     =		switch_checkpoint_path + F'''-rank-{expert}.pt'''
														if os.path.isfile(_lowercase		):
																					UpperCAmelCase     :		Optional[Any]							     =		torch.load(_lowercase		)["""model"""]
																					remove_ignore_keys_(_lowercase		)
																					UpperCAmelCase     :		List[str]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
																					UpperCAmelCase     :		Optional[Any]							     =		os.path.join(
																					    _lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
																					torch.save(_lowercase	,				_lowercase		)
																					sharded_state_dicts.append(expert_state.keys()		)
																					total_size += sum([value.numel() for key, value in expert_state.items()]		) * dtype_byte_size(
																					    expert_state[list(_lowercase		)[0]].dtype		)

    # Add the last block
							UpperCAmelCase     :		Optional[Any]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
							UpperCAmelCase     :		Optional[int]							     =		torch.load(switch_checkpoint_path + """-shared.pt"""		)["""model"""]
							remove_ignore_keys_(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
							UpperCAmelCase     :		int							     =		shared_weights["""decoder.embed_tokens.weight"""]
							sharded_state_dicts.append(shared_weights.keys()		)

							# If we only have the shared weights (dummy model/experts saved on the same file)
							if len(_lowercase		) == 1:
														UpperCAmelCase     :		Dict							     =		os.path.join(_lowercase	,				_lowercase		)
														torch.save(_lowercase	,				_lowercase		)
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(_lowercase	,				_lowercase		)
							# Otherwise, let's build the index
							UpperCAmelCase     :		Optional[int]							     =		{}
							for idx, shard in enumerate(_lowercase		):
														UpperCAmelCase     :		List[str]							     =		weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-{len(_lowercase		):05d}.bin'''		)
														UpperCAmelCase     :		List[str]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-???.bin'''		)		)
														os.rename(_lowercase	,				os.path.join(_lowercase	,				_lowercase		)		)
														for key in shard:
																					UpperCAmelCase     :		Tuple							     =		shard_file

    # Add the metadata
							UpperCAmelCase     :		Any							     =		{"""total_size""": total_size}
							UpperCAmelCase     :		Optional[Any]							     =		{"""metadata""": metadata, """weight_map""": weight_map}

							with open(os.path.join(_lowercase	,				_lowercase		)	,				"""w"""	,				encoding="""utf-8"""		) as f:
														UpperCAmelCase     :		str							     =		json.dumps(_lowercase	,				indent=2	,				sort_keys=_lowercase		) + """\n"""
														f.write(_lowercase		)

							return metadata, index


if __name__ == "__main__":
							a   :			List[Any]					  =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--nllb_moe_checkpoint_path""",
							    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
							    type=str,
							    required=False,
							    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
							)
							parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
							    type=str,
							    required=False,
							    help="""Path to the output pytorch model.""",
							)
							a   :			int					  =    parser.parse_args()
							a  ,      a   :			Any					  =    shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							a   :			str					  =    NllbMoeConfig.from_pretrained(
							    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							a   :			Optional[int]					  =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)

