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(UpperCAmelCase__     :							Optional[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(_UpperCamelCase      , _UpperCamelCase  )





def 	__lowerCamelCase(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 	__lowerCamelCase(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							Dict=None  ):
					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 	__lowerCamelCase(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							str = WEIGHTS_NAME  ):
					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__":
							_lowerCamelCase					:   Optional[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.''',
							)
							_lowerCamelCase					:   Dict							=		parser.parse_args()
							_lowerCamelCase					:   Union[str, Any]							=		shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_28,
							    args.dtype,
							)

							_lowerCamelCase					:   Tuple							=		NllbMoeConfig.from_pretrained(
							    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							_lowerCamelCase					:   List[Any]							=		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('''Done''')
							model.save_pretrained(args.pytorch_dump_folder_path)





import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase					:   Dict							=		logging.get_logger(__name__)

_lowerCamelCase					:   List[Any]							=		'''▁'''

_lowerCamelCase					:   Optional[int]							=		{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowerCamelCase					:   str							=		{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       collections.OrderedDict()
					with open(UpperCAmelCase__      , "r"      , encoding="utf-8"  ) as reader:
										SCREAMING_SNAKE_CASE    			=       reader.readlines()
					for index, token in enumerate(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       token.rstrip("\n"  )
										SCREAMING_SNAKE_CASE    			=       index
					return vocab


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							VOCAB_FILES_NAMES
							lowercase__						:       Optional[Any]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       Union[str, Any]								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__						:       Any								=							["""input_ids""", """attention_mask"""]




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					str="[SEP]"	,							_UpperCamelCase   :					Dict="[SEP]"	,							_UpperCamelCase   :					Tuple="[UNK]"	,							_UpperCamelCase   :					Dict="[PAD]"	,							_UpperCamelCase   :					Any="[CLS]"	,							_UpperCamelCase   :					Optional[Any]="[MASK]"	,							_UpperCamelCase   :					Optional[Dict[str, Any]] = None	,							**_UpperCamelCase   :					Dict	,							)					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=_UpperCamelCase	,							eos_token=_UpperCamelCase	,							sep_token=_UpperCamelCase	,							unk_token=_UpperCamelCase	,							pad_token=_UpperCamelCase	,							cls_token=_UpperCamelCase	,							mask_token=_UpperCamelCase	,							sp_model_kwargs=self.sp_model_kwargs	,							**_UpperCamelCase	,							)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(str(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												SCREAMING_SNAKE_CASE    			=       {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

												for i in range(10      ):
																	SCREAMING_SNAKE_CASE    			=       F"[unused{i}]"
																	SCREAMING_SNAKE_CASE    			=       5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												SCREAMING_SNAKE_CASE    			=       12
												SCREAMING_SNAKE_CASE    			=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(_UpperCamelCase      )




							def __getstate__(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.__dict__.copy()
												SCREAMING_SNAKE_CASE    			=       None
												return state




							def __setstate__(						self   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"      )
																	raise

												# for backward compatibility
												if not hasattr(self	,							"sp_model_kwargs"      ):
																	SCREAMING_SNAKE_CASE    			=       {}

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
												self.sp_model.Load(self.vocab_file      )




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None	,							_UpperCamelCase   :					bool = False      )					->				List[int]:



												'''simple docstring'''

												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 ([0] * len(_UpperCamelCase      )) + [1]
												return ([0] * len(_UpperCamelCase      )) + [1] + ([0] * len(_UpperCamelCase      )) + [1]




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]

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




							@property
							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												return len(self.sp_model      ) + self.fairseq_offset




							def 	__snake_case(						self   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {self.convert_ids_to_tokens(_UpperCamelCase      ): i for i in range(self.vocab_size      )}
												vocab.update(self.added_tokens_encoder      )
												return vocab




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					str      )					->				str:



												'''simple docstring'''

												return self.sp_model.encode(_UpperCamelCase	,							out_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												SCREAMING_SNAKE_CASE    			=       self.sp_model.PieceToId(_UpperCamelCase      )

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str      )					->				int:



												'''simple docstring'''

												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset      )




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "".join(_UpperCamelCase      ).replace(_UpperCamelCase	,							" "      ).strip()
												return out_string




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

												if not os.path.isdir(_UpperCamelCase      ):
																	logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
																	return
												SCREAMING_SNAKE_CASE    			=       os.path.join(
												    _UpperCamelCase	,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

												if os.path.abspath(self.vocab_file      ) != os.path.abspath(_UpperCamelCase      ) and os.path.isfile(self.vocab_file      ):
																	copyfile(self.vocab_file	,							_UpperCamelCase      )
												elif not os.path.isfile(self.vocab_file      ):
																	with open(_UpperCamelCase	,							"wb"      ) as fi:
																						SCREAMING_SNAKE_CASE    			=       self.sp_model.serialized_model_proto()
																						fi.write(_UpperCamelCase      )

												return (out_vocab_file,)





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None      )					->				List[int]:



												'''simple docstring'''

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




