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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				[
				    "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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				emb.weight.shape
				SCREAMING_SNAKE_CASE__		  =				nn.Linear(__UpperCAmelCase  ,					__UpperCAmelCase  ,					bias=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				emb.weight.data
				return lin_layer




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=None       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				{}
				for old_key in state_dict.keys():
								SCREAMING_SNAKE_CASE__		  =				old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																SCREAMING_SNAKE_CASE__		  =				key.replace("moe_layer.experts.0"  ,					F"""ffn.experts.expert_{expert_idx}"""       )
												else:
																SCREAMING_SNAKE_CASE__		  =				key.replace("moe_layer.experts."  ,					"ffn.experts.expert_"       )
								if "gate" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".moe_layer.gate.wg"  ,					".ffn.router.classifier"       )
								if "fc2" and "experts" not in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".fc2."  ,					".ffn.fc2."       )
								if "fc1" and "experts" not in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".fc1."  ,					".ffn.fc1."       )
								if ".encoder_attn." in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace(".encoder_attn."  ,					".cross_attention."       )
								if "encoder_attn_layer_norm" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("encoder_attn_layer_norm"  ,					"cross_attention_layer_norm"       )
								if "final_layer_norm" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("final_layer_norm"  ,					"ff_layer_norm"       )
								SCREAMING_SNAKE_CASE__		  =				state_dict[old_key]
				return new_dict




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = WEIGHTS_NAME       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				[]
				SCREAMING_SNAKE_CASE__		  =				0
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )

				for expert in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				switch_checkpoint_path + F"""-rank-{expert}.pt"""
								if os.path.isfile(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )["model"]
												remove_ignore_keys_(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				rename_fairseq_keys(__UpperCAmelCase  ,					__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__		  =				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
				SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					weights_name.replace(".bin"  ,					F"""-{len(__UpperCAmelCase       )+1:05d}-of-???.bin"""       )       )
				SCREAMING_SNAKE_CASE__		  =				torch.load(switch_checkpoint_path + "-shared.pt"       )["model"]
				remove_ignore_keys_(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				rename_fairseq_keys(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				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:
								SCREAMING_SNAKE_CASE__		  =				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
				SCREAMING_SNAKE_CASE__		  =				{}
				for idx, shard in enumerate(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				weights_name.replace(".bin"  ,					F"""-{idx+1:05d}-of-{len(__UpperCAmelCase       ):05d}.bin"""       )
								SCREAMING_SNAKE_CASE__		  =				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:
												SCREAMING_SNAKE_CASE__		  =				shard_file

    # Add the metadata
				SCREAMING_SNAKE_CASE__		  =				{"total_size": total_size}
				SCREAMING_SNAKE_CASE__		  =				{"metadata": metadata, "weight_map": weight_map}

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

				return metadata, index


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

						_A   					=    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   					=    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




