


'''simple docstring'''






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__A	:	List[str]     =     logging.getLogger(__name__)
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		str   		=  'summarization'
							lowercase			:		Tuple   		=  ['loss']
							lowercase			:		Dict   		=  ROUGE_KEYS
							lowercase			:		Optional[Any]   		=  'rouge2'





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:int  ,**_UpperCamelCase		:Any			):
									if hparams.sortish_sampler and hparams.gpus > 1:
											snake_case_		:							Union[str, Any]			       =						False
									elif hparams.max_tokens_per_batch is not None:
											if hparams.gpus > 1:
													raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training"""			)
											if hparams.sortish_sampler:
													raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"""			)

									super().__init__(_UpperCamelCase  ,num_labels=_UpperCamelCase  ,mode=self.mode  ,**_UpperCamelCase			)
									use_task_specific_params(self.model  ,"""summarization"""			)
									save_git_info(self.hparams.output_dir			)
									snake_case_		:							Dict			       =						Path(self.output_dir			) / """metrics.json"""
									snake_case_		:							str			       =						Path(self.output_dir			) / """hparams.pkl"""
									pickle_save(self.hparams  ,self.hparams_save_path			)
									snake_case_		:							Tuple			       =						0
									snake_case_		:							Tuple			       =						defaultdict(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						self.config.model_type
									snake_case_		:							str			       =						self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

									snake_case_		:							dict			       =						{
									    "data_dir": self.hparams.data_dir,
									    "max_source_length": self.hparams.max_source_length,
									    "prefix": self.model.config.prefix or "",
									}
									snake_case_		:							int			       =						{
									    """train""": self.hparams.n_train,
									    """val""": self.hparams.n_val,
									    """test""": self.hparams.n_test,
									}
									snake_case_		:							Optional[Any]			       =						{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

									snake_case_		:							Optional[int]			       =						{
									    """train""": self.hparams.max_target_length,
									    """val""": self.hparams.val_max_target_length,
									    """test""": self.hparams.test_max_target_length,
									}
									assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
									assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
									if self.hparams.freeze_embeds:
											freeze_embeds(self.model			)
									if self.hparams.freeze_encoder:
											freeze_params(self.model.get_encoder()			)
											assert_all_frozen(self.model.get_encoder()			)

									snake_case_		:							Union[str, Any]			       =						get_git_info()["""repo_sha"""]
									snake_case_		:							Optional[int]			       =						hparams.num_workers
									snake_case_		:							List[Any]			       =						None  # default to config
									if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer  ,_UpperCamelCase			):
											snake_case_		:							Any			       =						self.tokenizer.lang_code_to_id[hparams.tgt_lang]
											snake_case_		:							Union[str, Any]			       =						self.decoder_start_token_id
									snake_case_		:							Optional[int]			       =						(
									    SeqaSeqDataset if hasattr(self.tokenizer  ,"""prepare_seq2seq_batch"""			) else LegacySeqaSeqDataset
									)
									snake_case_		:							Tuple			       =						False
									snake_case_		:							List[str]			       =						self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
									if self.hparams.eval_max_gen_length is not None:
											snake_case_		:							Union[str, Any]			       =						self.hparams.eval_max_gen_length
									else:
											snake_case_		:							Any			       =						self.model.config.max_length
									snake_case_		:							int			       =						self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Dict[str, torch.Tensor]			):
									snake_case_		:							List[Any]			       =						{
									    k: self.tokenizer.batch_decode(v.tolist()			) if """mask""" not in k else v.shape for k, v in batch.items()
									}
									save_json(_UpperCamelCase  ,Path(self.output_dir			) / """text_batch.json"""			)
									save_json({k: v.tolist() for k, v in batch.items()}  ,Path(self.output_dir			) / """tok_batch.json"""			)

									snake_case_		:							int			       =						True
									return readable_batch





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:str  ,**_UpperCamelCase		:List[str]			):
									return self.model(_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:List[int]			):
									snake_case_		:							Tuple			       =						self.tokenizer.batch_decode(
									    _UpperCamelCase  ,skip_special_tokens=_UpperCamelCase  ,clean_up_tokenization_spaces=_UpperCamelCase			)
									return lmap(str.strip  ,_UpperCamelCase			)





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:dict			):
									snake_case_		:							int			       =						self.tokenizer.pad_token_id
									snake_case_       ,      snake_case_		:							Dict			       =						batch["""input_ids"""], batch["""attention_mask"""]
									snake_case_		:							Any			       =						batch["""labels"""]
									if isinstance(self.model  ,_UpperCamelCase			):
											snake_case_		:							str			       =						self.model._shift_right(_UpperCamelCase			)
									else:
											snake_case_		:							List[str]			       =						shift_tokens_right(_UpperCamelCase  ,_UpperCamelCase			)
									if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
											snake_case_		:							Any			       =						decoder_input_ids
											self.save_readable_batch(_UpperCamelCase			)

									snake_case_		:							Optional[int]			       =						self(_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,decoder_input_ids=_UpperCamelCase  ,use_cache=_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						outputs["""logits"""]
									if self.hparams.label_smoothing == 0:
											# Same behavior as modeling_bart.py, besides ignoring pad_token_id
											snake_case_		:							Any			       =						nn.CrossEntropyLoss(ignore_index=_UpperCamelCase			)

											assert lm_logits.shape[-1] == self.vocab_size
											snake_case_		:							List[str]			       =						ce_loss_fct(lm_logits.view(-1  ,lm_logits.shape[-1]			)  ,tgt_ids.view(-1			)			)
									else:
											snake_case_		:							Optional[Any]			       =						nn.functional.log_softmax(_UpperCamelCase  ,dim=-1			)
											snake_case_       ,      snake_case_		:							Optional[Any]			       =						label_smoothed_nll_loss(
											    _UpperCamelCase  ,_UpperCamelCase  ,self.hparams.label_smoothing  ,ignore_index=_UpperCamelCase			)
									return (loss,)





							@property
							def 		a__   (  self		:Union[str, Any]			):
									return self.tokenizer.pad_token_id





							def 		a__   (  self		:int  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						self._step(_UpperCamelCase			)

									snake_case_		:							int			       =						dict(zip(self.loss_names  ,_UpperCamelCase			)			)
									# tokens per batch
									snake_case_		:							List[str]			       =						batch["""input_ids"""].ne(self.pad			).sum() + batch["""labels"""].ne(self.pad			).sum()
									snake_case_		:							List[str]			       =						batch["""input_ids"""].shape[0]
									snake_case_		:							Dict			       =						batch["""input_ids"""].eq(self.pad			).sum()
									snake_case_		:							Any			       =						batch["""input_ids"""].eq(self.pad			).float().mean()
									# TODO(SS): make a wandb summary metric for this
									return {"loss": loss_tensors[0], "log": logs}





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:int			):
									return self._generative_step(_UpperCamelCase			)





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]="val"			):
									self.step_count += 1
									snake_case_		:							Union[str, Any]			       =						{k: torch.stack([x[k] for x in outputs]			).mean() for k in self.loss_names}
									snake_case_		:							Optional[Any]			       =						losses["""loss"""]
									snake_case_		:							str			       =						{
									    k: np.array([x[k] for x in outputs]			).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
									}
									snake_case_		:							Union[str, Any]			       =						(
									    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
									)
									snake_case_		:							torch.FloatTensor			       =						torch.tensor(_UpperCamelCase			).type_as(_UpperCamelCase			)
									generative_metrics.update({k: v.item() for k, v in losses.items()}			)
									losses.update(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
									snake_case_		:							Union[str, Any]			       =						self.step_count
									self.metrics[prefix].append(_UpperCamelCase			)  # callback writes this to self.metrics_save_path
									snake_case_		:							Dict			       =						flatten_list([x["""preds"""] for x in outputs]			)
									return {
									    "log": all_metrics,
									    "preds": preds,
									    F'''{prefix}_loss''': loss,
									    F'''{prefix}_{self.val_metric}''': metric_tensor,
									}





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:int			):
									return calculate_rouge(_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:dict			):
									snake_case_		:							Union[str, Any]			       =						time.time()

									# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
									snake_case_		:							Dict			       =						self.model.generate(
									    batch["""input_ids"""]  ,attention_mask=batch["""attention_mask"""]  ,use_cache=_UpperCamelCase  ,decoder_start_token_id=self.decoder_start_token_id  ,num_beams=self.eval_beams  ,max_length=self.eval_max_length  ,)
									snake_case_		:							Dict			       =						(time.time() - ta) / batch["""input_ids"""].shape[0]
									snake_case_		:							List[str]			       =						self.ids_to_clean_text(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						self.ids_to_clean_text(batch["""labels"""]			)
									snake_case_		:							Optional[Any]			       =						self._step(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						dict(zip(self.loss_names  ,_UpperCamelCase			)			)
									snake_case_		:							Dict			       =						self.calc_generative_metrics(_UpperCamelCase  ,_UpperCamelCase			)
									snake_case_		:							List[str]			       =						np.mean(lmap(_UpperCamelCase  ,_UpperCamelCase			)			)
									base_metrics.update(gen_time=_UpperCamelCase  ,gen_len=_UpperCamelCase  ,preds=_UpperCamelCase  ,target=_UpperCamelCase  ,**_UpperCamelCase			)
									return base_metrics





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]			):
									return self._generative_step(_UpperCamelCase			)





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Tuple			):
									return self.validation_epoch_end(_UpperCamelCase  ,prefix="""test"""			)





							def 		a__   (  self		:int  ,_UpperCamelCase		:Tuple			):
									snake_case_		:							Dict			       =						self.n_obs[type_path]
									snake_case_		:							int			       =						self.target_lens[type_path]
									snake_case_		:							int			       =						self.dataset_class(
									    self.tokenizer  ,type_path=_UpperCamelCase  ,n_obs=_UpperCamelCase  ,max_target_length=_UpperCamelCase  ,**self.dataset_kwargs  ,)
									return dataset





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:int  ,_UpperCamelCase		:bool = False			):
									snake_case_		:							int			       =						self.get_dataset(_UpperCamelCase			)

									if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
											snake_case_		:							int			       =						dataset.make_sortish_sampler(_UpperCamelCase  ,distributed=self.hparams.gpus > 1			)
											return DataLoader(
											    _UpperCamelCase  ,batch_size=_UpperCamelCase  ,collate_fn=dataset.collate_fn  ,shuffle=_UpperCamelCase  ,num_workers=self.num_workers  ,sampler=_UpperCamelCase  ,)

									elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
											snake_case_		:							Any			       =						dataset.make_dynamic_sampler(
											    self.hparams.max_tokens_per_batch  ,distributed=self.hparams.gpus > 1			)
											return DataLoader(
											    _UpperCamelCase  ,batch_sampler=_UpperCamelCase  ,collate_fn=dataset.collate_fn  ,num_workers=self.num_workers  ,)
									else:
											return DataLoader(
											    _UpperCamelCase  ,batch_size=_UpperCamelCase  ,collate_fn=dataset.collate_fn  ,shuffle=_UpperCamelCase  ,num_workers=self.num_workers  ,sampler=_UpperCamelCase  ,)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Dict			       =						self.get_dataloader("""train"""  ,batch_size=self.hparams.train_batch_size  ,shuffle=_UpperCamelCase			)
									return dataloader





							def 		a__   (  self		:str			):
									return self.get_dataloader("""val"""  ,batch_size=self.hparams.eval_batch_size			)





							def 		a__   (  self		:Union[str, Any]			):
									return self.get_dataloader("""test"""  ,batch_size=self.hparams.eval_batch_size			)





							@staticmethod
							def 		a__   (  _UpperCamelCase		:Any  ,_UpperCamelCase		:Union[str, Any]			):
									BaseTransformer.add_model_specific_args(_UpperCamelCase  ,_UpperCamelCase			)
									add_generic_args(_UpperCamelCase  ,_UpperCamelCase			)
									parser.add_argument(
									    """--max_source_length"""  ,default=1_0_2_4  ,type=_UpperCamelCase  ,help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )  ,)
									parser.add_argument(
									    """--max_target_length"""  ,default=5_6  ,type=_UpperCamelCase  ,help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )  ,)
									parser.add_argument(
									    """--val_max_target_length"""  ,default=1_4_2  ,type=_UpperCamelCase  ,help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )  ,)
									parser.add_argument(
									    """--test_max_target_length"""  ,default=1_4_2  ,type=_UpperCamelCase  ,help=(
									        """The maximum total input sequence length after tokenization. Sequences longer """
									        """than this will be truncated, sequences shorter will be padded."""
									    )  ,)
									parser.add_argument("""--freeze_encoder"""  ,action="""store_true"""			)
									parser.add_argument("""--freeze_embeds"""  ,action="""store_true"""			)
									parser.add_argument("""--sortish_sampler"""  ,action="""store_true"""  ,default=_UpperCamelCase			)
									parser.add_argument("""--overwrite_output_dir"""  ,action="""store_true"""  ,default=_UpperCamelCase			)
									parser.add_argument("""--max_tokens_per_batch"""  ,type=_UpperCamelCase  ,default=_UpperCamelCase			)
									parser.add_argument("""--logger_name"""  ,type=_UpperCamelCase  ,choices=["""default""", """wandb""", """wandb_shared"""]  ,default="""default"""			)
									parser.add_argument("""--n_train"""  ,type=_UpperCamelCase  ,default=-1  ,required=_UpperCamelCase  ,help="""# examples. -1 means use all."""			)
									parser.add_argument("""--n_val"""  ,type=_UpperCamelCase  ,default=5_0_0  ,required=_UpperCamelCase  ,help="""# examples. -1 means use all."""			)
									parser.add_argument("""--n_test"""  ,type=_UpperCamelCase  ,default=-1  ,required=_UpperCamelCase  ,help="""# examples. -1 means use all."""			)
									parser.add_argument(
									    """--task"""  ,type=_UpperCamelCase  ,default="""summarization"""  ,required=_UpperCamelCase  ,help="""# examples. -1 means use all."""			)
									parser.add_argument("""--label_smoothing"""  ,type=_UpperCamelCase  ,default=0.0  ,required=_UpperCamelCase			)
									parser.add_argument("""--src_lang"""  ,type=_UpperCamelCase  ,default=""""""  ,required=_UpperCamelCase			)
									parser.add_argument("""--tgt_lang"""  ,type=_UpperCamelCase  ,default=""""""  ,required=_UpperCamelCase			)
									parser.add_argument("""--eval_beams"""  ,type=_UpperCamelCase  ,default=_UpperCamelCase  ,required=_UpperCamelCase			)
									parser.add_argument(
									    """--val_metric"""  ,type=_UpperCamelCase  ,default=_UpperCamelCase  ,required=_UpperCamelCase  ,choices=["""bleu""", """rouge2""", """loss""", None]			)
									parser.add_argument("""--eval_max_gen_length"""  ,type=_UpperCamelCase  ,default=_UpperCamelCase  ,help="""never generate more than n tokens"""			)
									parser.add_argument("""--save_top_k"""  ,type=_UpperCamelCase  ,default=1  ,required=_UpperCamelCase  ,help="""How many checkpoints to save"""			)
									parser.add_argument(
									    """--early_stopping_patience"""  ,type=_UpperCamelCase  ,default=-1  ,required=_UpperCamelCase  ,help=(
									        """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
									        """ val_check_interval will effect it."""
									    )  ,)
									return parser





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Optional[Any]   		=  'translation'
							lowercase			:		List[Any]   		=  ['loss']
							lowercase			:		Optional[int]   		=  ['bleu']
							lowercase			:		List[Any]   		=  'bleu'





							def __init__(  self		:Optional[int]  ,_UpperCamelCase		:str  ,**_UpperCamelCase		:int			):
									super().__init__(_UpperCamelCase  ,**_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						hparams.src_lang
									snake_case_		:							str			       =						hparams.tgt_lang





							def 		a__   (  self		:Any  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Union[str, Any]			):
									return calculate_bleu(_UpperCamelCase  ,_UpperCamelCase			)





def      UpperCAmelCase      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :Optional[Any]=None ):







		'''simple docstring'''
		Path(args.output_dir ).mkdir(exist_ok=lowerCamelCase_ )
		check_output_dir(lowerCamelCase_				,      expected_items=3 )

		if model is None:
				if "summarization" in args.task:
						snake_case_		:							SummarizationModule			       =						SummarizationModule(lowerCamelCase_ )
				else:
						snake_case_		:							SummarizationModule			       =						TranslationModule(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						Path(args.data_dir ).name
		if (
		    args.logger_name == "default"
		    or args.fast_dev_run
		    or str(args.output_dir ).startswith("""/tmp""" )
		    or str(args.output_dir ).startswith("""/var""" )
		):
				snake_case_		:							List[str]			       =						True  # don't pollute wandb logs unnecessarily
		elif args.logger_name == "wandb":
				from pytorch_lightning.loggers import WandbLogger

				snake_case_		:							Tuple			       =						os.environ.get("""WANDB_PROJECT"""				,      lowerCamelCase_ )
				snake_case_		:							List[Any]			       =						WandbLogger(name=model.output_dir.name				,      project=lowerCamelCase_ )

		elif args.logger_name == "wandb_shared":
				from pytorch_lightning.loggers import WandbLogger

				snake_case_		:							str			       =						WandbLogger(name=model.output_dir.name				,      project=F'''hf_{dataset}''' )

		if args.early_stopping_patience >= 0:
				snake_case_		:							Optional[Any]			       =						get_early_stopping_callback(model.val_metric				,      args.early_stopping_patience )
		else:
				snake_case_		:							Optional[Any]			       =						False

		snake_case_		:							List[Any]			       =						args.val_metric == """loss"""
		snake_case_		:							pl.Trainer			       =						generic_train(
		    lowerCamelCase_				,      lowerCamelCase_				,      logging_callback=SeqaSeqLoggingCallback()				,      checkpoint_callback=get_checkpoint_callback(
		        args.output_dir				,      model.val_metric				,      args.save_top_k				,      lowerCamelCase_ )				,      early_stopping_callback=lowerCamelCase_				,      logger=lowerCamelCase_				,      )
		pickle_save(model.hparams				,      model.output_dir / """hparams.pkl""" )
		if not args.do_predict:
				return model

		snake_case_		:							List[str]			       =						""""""
		snake_case_		:							Tuple			       =						sorted(glob.glob(os.path.join(args.output_dir				,      """*.ckpt""" )				,      recursive=lowerCamelCase_ ) )
		if checkpoints:
				snake_case_		:							Optional[int]			       =						checkpoints[-1]
				snake_case_		:							str			       =						checkpoints[-1]
		trainer.logger.log_hyperparams(model.hparams )

		# test() without a model tests using the best checkpoint automatically
		trainer.test()
		return model


if __name__ == "__main__":
							__A	:	Dict     =     argparse.ArgumentParser()
							__A	:	str     =     pl.Trainer.add_argparse_args(parser)
							__A	:	Optional[int]     =     SummarizationModule.add_model_specific_args(parser, os.getcwd())

							__A	:	Optional[Any]     =     parser.parse_args()

							main(args)



'''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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[
		    """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      UpperCAmelCase      (		lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		snake_case_       ,      snake_case_		:							Dict			       =						emb.weight.shape
		snake_case_		:							str			       =						nn.Linear(lowerCamelCase_				,      lowerCamelCase_				,      bias=lowerCamelCase_ )
		snake_case_		:							str			       =						emb.weight.data
		return lin_layer





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]=None ):







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





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :str = WEIGHTS_NAME ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						[]
		snake_case_		:							Dict			       =						0
		os.makedirs(lowerCamelCase_				,      exist_ok=lowerCamelCase_ )

		for expert in range(lowerCamelCase_ ):
				snake_case_		:							Optional[Any]			       =						switch_checkpoint_path + F'''-rank-{expert}.pt'''
				if os.path.isfile(lowerCamelCase_ ):
						snake_case_		:							List[Any]			       =						torch.load(lowerCamelCase_ )["""model"""]
						remove_ignore_keys_(lowerCamelCase_ )
						snake_case_		:							List[str]			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
						snake_case_		:							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
		snake_case_		:							Tuple			       =						os.path.join(lowerCamelCase_				,      weights_name.replace(""".bin"""				,      F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
		snake_case_		:							Tuple			       =						torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
		remove_ignore_keys_(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						rename_fairseq_keys(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							List[str]			       =						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:
				snake_case_		:							Union[str, Any]			       =						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
		snake_case_		:							str			       =						{}
		for idx, shard in enumerate(lowerCamelCase_ ):
				snake_case_		:							List[str]			       =						weights_name.replace(""".bin"""				,      F'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )
				snake_case_		:							Optional[int]			       =						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:
						snake_case_		:							Optional[int]			       =						shard_file

    # Add the metadata
		snake_case_		:							Any			       =						{"""total_size""": total_size}
		snake_case_		:							int			       =						{"""metadata""": metadata, """weight_map""": weight_map}

		with open(os.path.join(lowerCamelCase_				,      lowerCamelCase_ )				,      """w"""				,      encoding="""utf-8""" ) as f:
				snake_case_		:							List[str]			       =						json.dumps(lowerCamelCase_				,      indent=2				,      sort_keys=lowerCamelCase_ ) + """\n"""
				f.write(lowerCamelCase_ )

		return metadata, index


if __name__ == "__main__":
							__A	:	int     =     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	:	List[str]     =     parser.parse_args()
							__A,   __A	:	List[Any]     =     shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							__A	:	Tuple     =     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)
							__A	:	List[str]     =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)