
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_     (							UpperCAmelCase_							:     str	)						->				Tuple:




	'''simple docstring'''




	_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(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						emb.weight.shape
	_UpperCamelCase   :		str			=						nn.Linear(UpperCAmelCase_							,       UpperCAmelCase_							,       bias=UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						emb.weight.data
	return lin_layer






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Dict=None	)						->				List[str]:




	'''simple docstring'''




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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str = WEIGHTS_NAME	)						->				Dict:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						[]
	_UpperCamelCase   :		Optional[int]			=						0
	os.makedirs(UpperCAmelCase_							,       exist_ok=UpperCAmelCase_	)

	for expert in range(UpperCAmelCase_	):
		_UpperCamelCase   :		int			=						switch_checkpoint_path + F'''-rank-{expert}.pt'''
		if os.path.isfile(UpperCAmelCase_	):
			_UpperCamelCase   :		List[str]			=						torch.load(UpperCAmelCase_	)['model']
			remove_ignore_keys_(UpperCAmelCase_	)
			_UpperCamelCase   :		Optional[Any]			=						rename_fairseq_keys(UpperCAmelCase_							,       UpperCAmelCase_	)
			_UpperCamelCase   :		Union[str, Any]			=						os.path.join(
			    UpperCAmelCase_							,       weights_name.replace('.bin'							,       F'''-{len(UpperCAmelCase_	)+1:05d}-of-???.bin'''	)	)
			torch.save(UpperCAmelCase_							,       UpperCAmelCase_	)
			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(UpperCAmelCase_	)[0]].dtype	)

    # Add the last block
	_UpperCamelCase   :		Dict			=						os.path.join(UpperCAmelCase_							,       weights_name.replace('.bin'							,       F'''-{len(UpperCAmelCase_	)+1:05d}-of-???.bin'''	)	)
	_UpperCamelCase   :		List[str]			=						torch.load(switch_checkpoint_path + '-shared.pt'	)['model']
	remove_ignore_keys_(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						rename_fairseq_keys(UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[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(UpperCAmelCase_	) == 1:
		_UpperCamelCase   :		List[str]			=						os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	)
		torch.save(UpperCAmelCase_							,       UpperCAmelCase_	)
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(UpperCAmelCase_							,       UpperCAmelCase_	)
	# Otherwise, let's build the index
	_UpperCamelCase   :		Union[str, Any]			=						{}
	for idx, shard in enumerate(UpperCAmelCase_	):
		_UpperCamelCase   :		Tuple			=						weights_name.replace('.bin'							,       F'''-{idx+1:05d}-of-{len(UpperCAmelCase_	):05d}.bin'''	)
		_UpperCamelCase   :		Dict			=						os.path.join(UpperCAmelCase_							,       weights_name.replace('.bin'							,       F'''-{idx+1:05d}-of-???.bin'''	)	)
		os.rename(UpperCAmelCase_							,       os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	)	)
		for key in shard:
			_UpperCamelCase   :		Optional[Any]			=						shard_file

    # Add the metadata
	_UpperCamelCase   :		Tuple			=						{'total_size': total_size}
	_UpperCamelCase   :		Dict			=						{'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	)							,       'w'							,       encoding='utf-8'	) as f:
		_UpperCamelCase   :		int			=						json.dumps(UpperCAmelCase_							,       indent=2							,       sort_keys=UpperCAmelCase_	) + '\n'
		f.write(UpperCAmelCase_	)

	return metadata, index


if __name__ == "__main__":
							lowerCAmelCase__								=		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.""",
							)
							lowerCAmelCase__								=		parser.parse_args()
							lowerCAmelCase__			, lowerCAmelCase__								=		shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							lowerCAmelCase__								=		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)
							lowerCAmelCase__								=		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "rwkv"
		a__					       =  {"max_position_embeddings": "context_length"}


		def __init__(				self	,       __snake_case=5_02_77	,       __snake_case=10_24	,       __snake_case=40_96	,       __snake_case=32	,       __snake_case=None	,       __snake_case=None	,       __snake_case=1e-5	,       __snake_case=0	,       __snake_case=0	,       __snake_case=6	,       __snake_case=False	,       __snake_case=True	,       **__snake_case	,       ):
			_UpperCamelCase   :		str			=						vocab_size
			_UpperCamelCase   :		int			=						context_length
			_UpperCamelCase   :		Tuple			=						hidden_size
			_UpperCamelCase   :		Tuple			=						num_hidden_layers
			_UpperCamelCase   :		Dict			=						attention_hidden_size if attention_hidden_size is not None else hidden_size
			_UpperCamelCase   :		Tuple			=						intermediate_size if intermediate_size is not None else 4 * hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						layer_norm_epsilon
			_UpperCamelCase   :		Dict			=						rescale_every
			_UpperCamelCase   :		Optional[Any]			=						use_cache

			_UpperCamelCase   :		str			=						bos_token_id
			_UpperCamelCase   :		Optional[Any]			=						eos_token_id

			super().__init__(
			    tie_word_embeddings=__snake_case	,       bos_token_id=__snake_case	,       eos_token_id=__snake_case	,       **__snake_case)




