
"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   params

											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   np.array([len(SCREAMING_SNAKE_CASE__    ) for t in data]    )

											self.check()
											self.remove_long_sequences()
											self.remove_empty_sequences()
											self.remove_unknown_sequences()
											self.check()
											self.print_statistics()



				def __getitem__(self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return (self.token_ids[index], self.lengths[index])



				def __len__(self    )					->		int:



											"""simple docstring"""




											return len(self.lengths    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											assert len(self.token_ids    ) == len(self.lengths    )
											assert all(self.lengths[i] == len(self.token_ids[i]    ) for i in range(len(self.lengths    )    )    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.params.max_model_input_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.lengths > max_len
											logger.info(F'''Splitting {sum(SCREAMING_SNAKE_CASE__    )} too long sequences.'''    )

											def divide_chunks(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		return [l[i : i + n] for i in range(0   ,       len(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )]

											SCREAMING_SNAKE_CASE__						:       Tuple           =   []
											SCREAMING_SNAKE_CASE__						:       Any           =   []
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
											else:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

											for seq_, len_ in zip(self.token_ids   ,       self.lengths    ):
																		assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																		if len_ <= max_len:
																									new_tok_ids.append(seq_    )
																									new_lengths.append(len_    )
																		else:
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
																									for sub_s in divide_chunks(seq_   ,       max_len - 2    ):
																																if sub_s[0] != cls_id:
																																							SCREAMING_SNAKE_CASE__						:       int           =   np.insert(SCREAMING_SNAKE_CASE__   ,       0   ,       SCREAMING_SNAKE_CASE__    )
																																if sub_s[-1] != sep_id:
																																							SCREAMING_SNAKE_CASE__						:       str           =   np.insert(SCREAMING_SNAKE_CASE__   ,       len(SCREAMING_SNAKE_CASE__    )   ,       SCREAMING_SNAKE_CASE__    )
																																assert len(SCREAMING_SNAKE_CASE__    ) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(SCREAMING_SNAKE_CASE__    )

																									new_tok_ids.extend(SCREAMING_SNAKE_CASE__    )
																									new_lengths.extend([len(SCREAMING_SNAKE_CASE__    ) for l in sub_seqs]    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   np.array(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   np.array(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.lengths > 11
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.token_ids[indices]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.lengths[indices]
											SCREAMING_SNAKE_CASE__						:       str           =   len(self    )
											logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											if "unk_token" not in self.params.special_tok_ids:
																		return
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.params.special_tok_ids["""unk_token"""]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   len(self    )
											SCREAMING_SNAKE_CASE__						:       str           =   np.array([np.count_nonzero(a == unk_token_id    ) for a in self.token_ids]    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   (unk_occs / self.lengths) < 0.5
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.token_ids[indices]
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.lengths[indices]
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self    )
											logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											if not self.params.is_master:
																		return
											logger.info(F'''{len(self    )} sequences'''    )
											# data_len = sum(self.lengths)
											# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
											# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

											# unk_idx = self.params.special_tok_ids['unk_token']
											# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
											# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [t[0] for t in batch]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [t[1] for t in batch]
											assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

											# Max for paddings
											SCREAMING_SNAKE_CASE__						:       int           =   max(SCREAMING_SNAKE_CASE__    )

											# Pad token ids
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.params.special_tok_ids["""pad_token"""]
											else:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.params.special_tok_ids["""unk_token"""]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [list(t.astype(SCREAMING_SNAKE_CASE__    )    ) + [pad_idx] * (max_seq_len_ - len(SCREAMING_SNAKE_CASE__    )) for t in token_ids]
											assert len(tk_    ) == len(SCREAMING_SNAKE_CASE__    )
											assert all(len(SCREAMING_SNAKE_CASE__    ) == max_seq_len_ for t in tk_    )

											SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor(tk_    )  # (bs, max_seq_len_)
											SCREAMING_SNAKE_CASE__						:       Any           =   torch.tensor(SCREAMING_SNAKE_CASE__    )  # (bs)
											return tk_t, lg_t









'''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  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Dict							     =		[
							    """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(_lowercase	,				_lowercase		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		emb.weight.shape
							UpperCAmelCase     :		Dict							     =		nn.Linear(_lowercase	,				_lowercase	,				bias=_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		emb.weight.data
							return lin_layer



def       __lowerCamelCase  (					_lowercase	,				_lowercase=None		)      ->			List[Any]:
							UpperCAmelCase     :		Dict							     =		{}
							for old_key in state_dict.keys():
														UpperCAmelCase     :		str							     =		old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												UpperCAmelCase     :		int							     =		key.replace("""moe_layer.experts.0"""	,				F'''ffn.experts.expert_{expert_idx}'''		)
																					else:
																												UpperCAmelCase     :		str							     =		key.replace("""moe_layer.experts."""	,				"""ffn.experts.expert_"""		)
														if "gate" in key:
																					UpperCAmelCase     :		str							     =		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     :		Union[str, Any]							     =		key.replace(""".encoder_attn."""	,				""".cross_attention."""		)
														if "encoder_attn_layer_norm" in key:
																					UpperCAmelCase     :		str							     =		key.replace("""encoder_attn_layer_norm"""	,				"""cross_attention_layer_norm"""		)
														if "final_layer_norm" in key:
																					UpperCAmelCase     :		Optional[int]							     =		key.replace("""final_layer_norm"""	,				"""ff_layer_norm"""		)
														UpperCAmelCase     :		str							     =		state_dict[old_key]
							return new_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase = WEIGHTS_NAME		)      ->			Tuple:
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Dict							     =		0
							os.makedirs(_lowercase	,				exist_ok=_lowercase		)

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

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

    # Add the metadata
							UpperCAmelCase     :		Any							     =		{"""total_size""": total_size}
							UpperCAmelCase     :		Optional[Any]							     =		{"""metadata""": metadata, """weight_map""": weight_map}

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

							return metadata, index


if __name__ == "__main__":
							a   :			List[Any]					  =    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   :			int					  =    parser.parse_args()
							a  ,      a   :			Any					  =    shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							a   :			str					  =    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   :			Optional[int]					  =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)

