


"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				len(lowerCAmelCase_   )

					for i in range(n - 1   ):
										for j in range(i + 1  ,						lowerCAmelCase_   ):
															if arr[i] > arr[j]:
																				num_inversions += 1

					return num_inversions




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if len(lowerCAmelCase_   ) <= 1:
										return arr, 0
					__SCREAMING_SNAKE_CASE						   =				len(lowerCAmelCase_   ) // 2
					__SCREAMING_SNAKE_CASE						   =				arr[0:mid]
					__SCREAMING_SNAKE_CASE						   =				arr[mid:]

					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				count_inversions_recursive(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				count_inversions_recursive(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				_count_cross_inversions(lowerCAmelCase_  ,						lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				inversion_p + inversions_q + cross_inversions
					return c, num_inversions




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[]
					__SCREAMING_SNAKE_CASE						   =				__SCREAMING_SNAKE_CASE						   =				__SCREAMING_SNAKE_CASE						   =				0
					while i < len(lowerCAmelCase_   ) and j < len(lowerCAmelCase_   ):
										if p[i] > q[j]:
															# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
															# These are all inversions. The claim emerges from the
															# property that P is sorted.
															num_inversion += len(lowerCAmelCase_   ) - i
															r.append(q[j]   )
															j += 1
										else:
															r.append(p[i]   )
															i += 1

					if i < len(lowerCAmelCase_   ):
										r.extend(p[i:]   )
					else:
										r.extend(q[j:]   )

					return r, num_inversion




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[10, 2, 1, 5, 5, 2, 11]

					# this arr has 8 inversions:
					# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

					__SCREAMING_SNAKE_CASE						   =				count_inversions_bf(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				count_inversions_recursive(lowerCAmelCase_   )

					assert num_inversions_bf == num_inversions_recursive == 8

					print("number of inversions = "  ,						lowerCAmelCase_   )

					# testing an array with zero inversion (a sorted arr_1)

					arr_a.sort()
					__SCREAMING_SNAKE_CASE						   =				count_inversions_bf(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				count_inversions_recursive(lowerCAmelCase_   )

					assert num_inversions_bf == num_inversions_recursive == 0
					print("number of inversions = "  ,						lowerCAmelCase_   )

					# an empty list should also have zero inversions
					__SCREAMING_SNAKE_CASE						   =				[]
					__SCREAMING_SNAKE_CASE						   =				count_inversions_bf(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				count_inversions_recursive(lowerCAmelCase_   )

					assert num_inversions_bf == num_inversions_recursive == 0
					print("number of inversions = "  ,						lowerCAmelCase_   )


if __name__ == "__main__":
		main()




'''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)



