



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_							:      List[str]      =  logging.get_logger(__name__)


UpperCAmelCase_							:      Optional[int]      =  {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase_							:      Optional[int]      =  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

UpperCAmelCase_							:      List[str]      =  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}
@lru_cache()
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						A__							=       (
						    list(range(ord("!"    )			,							ord("~"    ) + 1    )    ) + list(range(ord("¡"    )			,							ord("¬"    ) + 1    )    ) + list(range(ord("®"    )			,							ord("ÿ"    ) + 1    )    )
						)
						A__							=       bs[:]
						A__							=       0
						for b in range(2**8    ):
												if b not in bs:
																		bs.append(_A    )
																		cs.append(2**8 + n    )
																		n += 1
						A__							=       [chr(_A    ) for n in cs]
						return dict(zip(_A			,							_A    )    )
def  UpperCamelCase						(       _A			:				List[str]    )->       int:





						"""simple docstring"""





						A__							=       set()
						A__							=       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)    )
												A__							=       char
						return pairs






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   str				=	VOCAB_FILES_NAMES
	lowerCAmelCase	:   Tuple				=	PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase	:   Dict				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase	:   List[str]				=	["""input_ids""", """attention_mask"""]



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__="replace"			,	UpperCAmelCase__="<s>"			,	UpperCAmelCase__="</s>"			,	UpperCAmelCase__="</s>"			,	UpperCAmelCase__="<s>"			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__="<pad>"			,	UpperCAmelCase__="<mask>"			,	UpperCAmelCase__=False			,	**UpperCAmelCase__			,	):
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else bos_token
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else eos_token
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else sep_token
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else cls_token
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else unk_token
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else mask_token

							super().__init__(
							    errors=UpperCAmelCase__			,	bos_token=UpperCAmelCase__			,	eos_token=UpperCAmelCase__			,	unk_token=UpperCAmelCase__			,	sep_token=UpperCAmelCase__			,	cls_token=UpperCAmelCase__			,	pad_token=UpperCAmelCase__			,	mask_token=UpperCAmelCase__			,	add_prefix_space=UpperCAmelCase__			,	**UpperCAmelCase__			,	)

							with open(UpperCAmelCase__			,	encoding="utf-8"		) as vocab_handle:
													A__							=       json.load(UpperCAmelCase__		)
							A__							=       {v: k for k, v in self.encoder.items()}
							A__							=       errors  # how to handle errors in decoding
							A__							=       bytes_to_unicode()
							A__							=       {v: k for k, v in self.byte_encoder.items()}
							with open(UpperCAmelCase__			,	encoding="utf-8"		) as merges_handle:
													A__							=       merges_handle.read().split("\n"		)[1:-1]
							A__							=       [tuple(merge.split()		) for merge in bpe_merges]
							A__							=       dict(zip(UpperCAmelCase__			,	range(len(UpperCAmelCase__		)		)		)		)
							A__							=       {}
							A__							=       add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							A__							=       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)



	@property
	def 			__A							(       self		):
							return len(self.encoder		)



	def 			__A							(       self		):
							return dict(self.encoder			,	**self.added_tokens_encoder		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							if token in self.cache:
													return self.cache[token]
							A__							=       tuple(UpperCAmelCase__		)
							A__							=       get_pairs(UpperCAmelCase__		)

							if not pairs:
													return token

							while True:
													A__							=       min(UpperCAmelCase__			,	key=lambda UpperCAmelCase__		: self.bpe_ranks.get(UpperCAmelCase__			,	float("inf"		)		)		)
													if bigram not in self.bpe_ranks:
																			break
													A__						,       A__							=       bigram
													A__							=       []
													A__							=       0
													while i < len(UpperCAmelCase__		):
																			try:
																									A__							=       word.index(UpperCAmelCase__			,	UpperCAmelCase__		)
																			except ValueError:
																									new_word.extend(word[i:]		)
																									break
																			else:
																									new_word.extend(word[i:j]		)
																									A__							=       j

																			if word[i] == first and i < len(UpperCAmelCase__		) - 1 and word[i + 1] == second:
																									new_word.append(first + second		)
																									i += 2
																			else:
																									new_word.append(word[i]		)
																									i += 1
													A__							=       tuple(UpperCAmelCase__		)
													A__							=       new_word
													if len(UpperCAmelCase__		) == 1:
																			break
													else:
																			A__							=       get_pairs(UpperCAmelCase__		)
							A__							=       " ".join(UpperCAmelCase__		)
							A__							=       word
							return word



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       []
							for token in re.findall(self.pat			,	UpperCAmelCase__		):
													A__							=       "".join(
													    self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase__		).split(" "		)		)
							return bpe_tokens



	def 			__A							(       self			,	UpperCAmelCase__		):
							return self.encoder.get(UpperCAmelCase__			,	self.encoder.get(self.unk_token		)		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							return self.decoder.get(UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       "".join(UpperCAmelCase__		)
							A__							=       bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"			,	errors=self.errors		)
							return text



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							if not os.path.isdir(UpperCAmelCase__		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
													return
							A__							=       os.path.join(
							    UpperCAmelCase__			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
							A__							=       os.path.join(
							    UpperCAmelCase__			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

							with open(UpperCAmelCase__			,	"w"			,	encoding="utf-8"		) as f:
													f.write(json.dumps(self.encoder			,	indent=2			,	sort_keys=UpperCAmelCase__			,	ensure_ascii=UpperCAmelCase__		) + "\n"		)

							A__							=       0
							with open(UpperCAmelCase__			,	"w"			,	encoding="utf-8"		) as writer:
													writer.write("#version: 0.2\n"		)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,	key=lambda UpperCAmelCase__		: kv[1]		):
																			if index != token_index:
																									logger.warning(
																									    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																									    " Please check that the tokenizer is not corrupted!"		)
																									A__							=       token_index
																			writer.write(" ".join(UpperCAmelCase__		) + "\n"		)
																			index += 1

							return vocab_file, merge_file



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A__							=       [self.cls_token_id]
							A__							=       [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False		):
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=UpperCAmelCase__			,	token_ids_a=UpperCAmelCase__			,	already_has_special_tokens=UpperCAmelCase__		)

							if token_ids_a is None:
													return [1] + ([0] * len(UpperCAmelCase__		)) + [1]
							return [1] + ([0] * len(UpperCAmelCase__		)) + [1, 1] + ([0] * len(UpperCAmelCase__		)) + [1]



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       [self.sep_token_id]
							A__							=       [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep		) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=False			,	**UpperCAmelCase__		):
							A__							=       kwargs.pop("add_prefix_space"			,	self.add_prefix_space		)
							if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase__		) > 0 and not text[0].isspace()):
													A__							=       " " + text
							return (text, kwargs)










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						(       _A			:				Tuple    )->       Dict:





						"""simple docstring"""





						A__							=       [
						    "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(_A			,							_A    )
def  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__						,       A__							=       emb.weight.shape
						A__							=       nn.Linear(_A			,							_A			,							bias=_A    )
						A__							=       emb.weight.data
						return lin_layer
def  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[Any]=None    )->       str:





						"""simple docstring"""





						A__							=       {}
						for old_key in state_dict.keys():
												A__							=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								A__							=       key.replace("moe_layer.experts.0"			,							f"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								A__							=       key.replace("moe_layer.experts."			,							"ffn.experts.expert_"    )
												if "gate" in key:
																		A__							=       key.replace(".moe_layer.gate.wg"			,							".ffn.router.classifier"    )
												if "fc2" and "experts" not in key:
																		A__							=       key.replace(".fc2."			,							".ffn.fc2."    )
												if "fc1" and "experts" not in key:
																		A__							=       key.replace(".fc1."			,							".ffn.fc1."    )
												if ".encoder_attn." in key:
																		A__							=       key.replace(".encoder_attn."			,							".cross_attention."    )
												if "encoder_attn_layer_norm" in key:
																		A__							=       key.replace("encoder_attn_layer_norm"			,							"cross_attention_layer_norm"    )
												if "final_layer_norm" in key:
																		A__							=       key.replace("final_layer_norm"			,							"ff_layer_norm"    )
												A__							=       state_dict[old_key]
						return new_dict
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Tuple			,							_A			:				int			,							_A			:				str			,							_A			:				str = WEIGHTS_NAME    )->       List[str]:





						"""simple docstring"""





						A__							=       []
						A__							=       0
						os.makedirs(_A			,							exist_ok=_A    )

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

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

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

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

						return metadata, index


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  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_							:      Union[str, Any]      =  parser.parse_args()
						UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCAmelCase_							:      Any      =  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_							:      Tuple      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





