


'''simple docstring'''


class 		lowerCAmelCase__	:




			def __init__(						self     :   Tuple		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					name
					_UpperCAmelCase						:				Dict	    =					val




			def __str__(						self     :   str       )      ->Optional[int]:



					'''simple docstring'''







					return F"""{self.__class__.__name__}({self.name}, {self.val})"""






			def __lt__(						self     :   int		,     lowerCamelCase__     :   List[Any]       )      ->Any:



					'''simple docstring'''







					return self.val < other.val






class 		lowerCAmelCase__	:




			def __init__(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Any       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					{}
					_UpperCAmelCase						:				Optional[int]	    =					{}
					_UpperCAmelCase						:				Dict	    =					self.build_heap(lowerCamelCase__       )




			def __getitem__(						self     :   List[str]		,     lowerCamelCase__     :   Tuple       )      ->Dict:



					'''simple docstring'''







					return self.get_value(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   str       )      ->Optional[Any]:



					'''simple docstring'''







					return (idx - 1) // 2




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   List[Any]       )      ->Optional[Any]:



					'''simple docstring'''







					return idx * 2 + 1




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   Tuple       )      ->List[str]:



					'''simple docstring'''







					return idx * 2 + 2




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Any       )      ->List[str]:



					'''simple docstring'''







					return self.heap_dict[key]




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[Any]       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					len(lowerCamelCase__       ) - 1
					_UpperCAmelCase						:				List[str]	    =					self.get_parent_idx(lowerCamelCase__       )

					for idx, i in enumerate(lowerCamelCase__       ):
							_UpperCAmelCase						:				int	    =					idx
							_UpperCAmelCase						:				Tuple	    =					i.val

					for i in range(lowerCamelCase__		,     -1		,     -1       ):
							self.sift_down(lowerCamelCase__		,     lowerCamelCase__       )
					return array




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   List[str]       )      ->Dict:



					'''simple docstring'''







					while True:
							_UpperCAmelCase						:				int	    =					self.get_left_child_idx(lowerCamelCase__       )  # noqa: E741
							_UpperCAmelCase						:				Tuple	    =					self.get_right_child_idx(lowerCamelCase__       )

							_UpperCAmelCase						:				int	    =					idx
							if l < len(lowerCamelCase__       ) and array[l] < array[idx]:
									_UpperCAmelCase						:				List[str]	    =					l
							if r < len(lowerCamelCase__       ) and array[r] < array[smallest]:
									_UpperCAmelCase						:				int	    =					r

							if smallest != idx:
									_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					array[smallest], array[idx]
									(
									    (
									    _UpperCAmelCase
									)    ,			(
									    _UpperCAmelCase
									)    ,			
									)						:				Tuple	    =					(
									    self.idx_of_element[array[smallest]],
									    self.idx_of_element[array[idx]],
									)
									_UpperCAmelCase						:				Optional[int]	    =					smallest
							else:
									break




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.get_parent_idx(lowerCamelCase__       )
					while p >= 0 and self.heap[p] > self.heap[idx]:
							_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.heap[idx], self.heap[p]
							_UpperCAmelCase    ,			_UpperCAmelCase						:				int	    =					(
							    self.idx_of_element[self.heap[idx]],
							    self.idx_of_element[self.heap[p]],
							)
							_UpperCAmelCase						:				List[Any]	    =					p
							_UpperCAmelCase						:				Union[str, Any]	    =					self.get_parent_idx(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Optional[int]:



					'''simple docstring'''







					return self.heap[0]




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.heap[-1], self.heap[0]
					_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					(
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

					_UpperCAmelCase						:				List[str]	    =					self.heap.pop()
					del self.idx_of_element[x]
					self.sift_down(0		,     self.heap       )
					return x




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Dict       )      ->Optional[Any]:



					'''simple docstring'''







					self.heap.append(lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					len(self.heap       ) - 1
					_UpperCAmelCase						:				List[str]	    =					node.val
					self.sift_up(len(self.heap       ) - 1       )




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Union[str, Any]:



					'''simple docstring'''







					return len(self.heap       ) == 0






			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Union[str, Any]       )      ->List[Any]:



					'''simple docstring'''







					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					_UpperCAmelCase						:				str	    =					new_value
					_UpperCAmelCase						:				str	    =					new_value
					self.sift_up(self.idx_of_element[node]       )




lowerCamelCase__					=   Node('R', -1)
lowerCamelCase__					=   Node('B', 6)
lowerCamelCase__					=   Node('A', 3)
lowerCamelCase__					=   Node('X', 1)
lowerCamelCase__					=   Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCamelCase__					=   MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
						print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()







'''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       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[str]	    =					[
		    "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(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[Any]	    =					emb.weight.shape
		_UpperCAmelCase						:				str	    =					nn.Linear(__lowerCAmelCase					,       __lowerCAmelCase					,       bias=__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[int]	    =					emb.weight.data
		return lin_layer






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=None      ):
		_UpperCAmelCase						:				int	    =					{}
		for old_key in state_dict.keys():
				_UpperCAmelCase						:				Tuple	    =					old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								_UpperCAmelCase						:				Optional[int]	    =					key.replace("moe_layer.experts.0"					,       F"""ffn.experts.expert_{expert_idx}"""      )
						else:
								_UpperCAmelCase						:				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						:				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						:				List[Any]	    =					key.replace(".encoder_attn."					,       ".cross_attention."      )
				if "encoder_attn_layer_norm" in key:
						_UpperCAmelCase						:				Any	    =					key.replace("encoder_attn_layer_norm"					,       "cross_attention_layer_norm"      )
				if "final_layer_norm" in key:
						_UpperCAmelCase						:				int	    =					key.replace("final_layer_norm"					,       "ff_layer_norm"      )
				_UpperCAmelCase						:				Tuple	    =					state_dict[old_key]
		return new_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = WEIGHTS_NAME      ):
		_UpperCAmelCase						:				Optional[int]	    =					[]
		_UpperCAmelCase						:				Optional[Any]	    =					0
		os.makedirs(__lowerCAmelCase					,       exist_ok=__lowerCAmelCase      )

		for expert in range(__lowerCAmelCase      ):
				_UpperCAmelCase						:				Tuple	    =					switch_checkpoint_path + F"""-rank-{expert}.pt"""
				if os.path.isfile(__lowerCAmelCase      ):
						_UpperCAmelCase						:				Tuple	    =					torch.load(__lowerCAmelCase      )["model"]
						remove_ignore_keys_(__lowerCAmelCase      )
						_UpperCAmelCase						:				Dict	    =					rename_fairseq_keys(__lowerCAmelCase					,       __lowerCAmelCase      )
						_UpperCAmelCase						:				List[str]	    =					os.path.join(
						    __lowerCAmelCase					,       weights_name.replace(".bin"					,       F"""-{len(__lowerCAmelCase      )+1:05d}-of-???.bin"""      )      )
						torch.save(__lowerCAmelCase					,       __lowerCAmelCase      )
						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(__lowerCAmelCase      )[0]].dtype      )

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

    # Add the metadata
		_UpperCAmelCase						:				Any	    =					{"total_size": total_size}
		_UpperCAmelCase						:				List[str]	    =					{"metadata": metadata, "weight_map": weight_map}

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

		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,
						    128,
						    args.dtype,
						)

						lowerCamelCase__					=   NllbMoeConfig.from_pretrained(
						    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
						)
						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)



