


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						int:
						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(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					emb.weight.shape
						SCREAMING_SNAKE_CASE													=					nn.Linear(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			bias=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					emb.weight.data
						return lin_layer


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any]=None  )   ->						Tuple:
						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     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				str = WEIGHTS_NAME  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					[]
						SCREAMING_SNAKE_CASE													=					0
						os.makedirs(SCREAMING_SNAKE_CASE_ ,			exist_ok=SCREAMING_SNAKE_CASE_  )

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

    # Add the last block
						SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			weights_name.replace('.bin' ,			F'-{len(SCREAMING_SNAKE_CASE_  )+1:05d}-of-???.bin'  )  )
						SCREAMING_SNAKE_CASE													=					torch.load(switch_checkpoint_path + '-shared.pt'  )['model']
						remove_ignore_keys_(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					rename_fairseq_keys(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						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(SCREAMING_SNAKE_CASE_  ) == 1:
												SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												torch.save(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return {weights_name: sharded_state_dicts[0]}, None
						else:
												torch.save(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						# Otherwise, let's build the index
						SCREAMING_SNAKE_CASE													=					{}
						for idx, shard in enumerate(SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					weights_name.replace('.bin' ,			F'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_  ):05d}.bin'  )
												SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			weights_name.replace('.bin' ,			F'-{idx+1:05d}-of-???.bin'  )  )
												os.rename(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )  )
												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(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) ,			'w' ,			encoding='utf-8'  ) as f:
												SCREAMING_SNAKE_CASE													=					json.dumps(SCREAMING_SNAKE_CASE_ ,			indent=2 ,			sort_keys=SCREAMING_SNAKE_CASE_  ) + '\n'
												f.write(SCREAMING_SNAKE_CASE_  )

						return metadata, index


if __name__ == "__main__":
	__UpperCamelCase						=      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.''',
	)
	__UpperCamelCase						=      parser.parse_args()
	__UpperCamelCase,__UpperCamelCase						=      shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

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






"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


