



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


_lowercase		:				Dict      		=logging.get_logger(__name__)

_lowercase		:				List[str]      		="▁"

_lowercase		:				int      		={"vocab_file": "prophetnet.tokenizer"}

_lowercase		:				List[str]      		={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

_lowercase		:				Tuple      		={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

_lowercase		:				Optional[int]      		={
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}

def      lowerCAmelCase_					(  _lowercase    :       Optional[int])							->	int:







			"""simple docstring"""
			a__       :						Optional[Any]							=  collections.OrderedDict()
			with open(_lowercase						,       """r"""						,       encoding="""utf-8""") as reader:
						a__       :						Any							=  reader.readlines()
			for index, token in enumerate(_lowercase):
						a__       :						Dict							=  token.rstrip("""\n""")
						a__       :						str							=  index
			return vocab





class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Dict      =					VOCAB_FILES_NAMES
						__lowerCAmelCase					:List[str]      =					PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase					:List[str]      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase					:Dict      =					["input_ids", "attention_mask"]

						def __init__(       self ,       __lowercase ,       __lowercase="[SEP]" ,       __lowercase="[SEP]" ,       __lowercase="[SEP]" ,       __lowercase="[UNK]" ,       __lowercase="[PAD]" ,       __lowercase="[CLS]" ,       __lowercase="[MASK]" ,       __lowercase = None ,       **__lowercase ,       ) ->				None:
									"""simple docstring"""




									a__       :						int							=  {} if sp_model_kwargs is None else sp_model_kwargs

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

									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

									a__       :						List[str]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(str(__lowercase  )  )
									a__       :						Optional[int]							=  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
									a__       :						List[str]							=  {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

									for i in range(1_0  ):
												a__       :						str							=  F'''[unused{i}]'''
												a__       :						List[str]							=  5 + i

									# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
									a__       :						Any							=  1_2
									a__       :						Optional[Any]							=  {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(__lowercase  )

						def __getstate__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.__dict__.copy()
									a__       :						Optional[int]							=  None
									return state

						def __setstate__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Dict							=  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"""  ):
												a__       :						Tuple							=  {}

									a__       :						List[Any]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(self.vocab_file  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None ,       __lowercase = False  ) ->				List[int]:
									"""simple docstring"""




									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowercase ,       token_ids_a=__lowercase ,       already_has_special_tokens=__lowercase  )

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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[int]:
									"""simple docstring"""




									a__       :						int							=  [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 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									return len(self.sp_model  ) + self.fairseq_offset

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Tuple							=  {self.convert_ids_to_tokens(__lowercase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
									a__       :						List[str]							=  self.sp_model.PieceToId(__lowercase  )

									# 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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Optional[Any]							=  """""".join(__lowercase  ).replace(__lowercase ,       """ """  ).strip()
									return out_string

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				Tuple[str]:
									"""simple docstring"""




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

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowercase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file ,       __lowercase  )
									elif not os.path.isfile(self.vocab_file  ):
												with open(__lowercase ,       """wb"""  ) as fi:
															a__       :						List[Any]							=  self.sp_model.serialized_model_proto()
															fi.write(__lowercase  )

									return (out_vocab_file,)

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = None  ) ->				List[int]:
									"""simple docstring"""




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









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    :       List[str])							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						List[str]							=  [
			    """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    :       List[Any])							->	Optional[Any]:







			"""simple docstring"""
			a__	,						a__       :						Union[str, Any]							=  emb.weight.shape
			a__       :						str							=  nn.Linear(_lowercase						,       _lowercase						,       bias=_lowercase)
			a__       :						Any							=  emb.weight.data
			return lin_layer

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       int=None)							->	List[Any]:







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

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       Optional[int]						,       _lowercase    :       Any						,       _lowercase    :       Dict						,       _lowercase    :       str = WEIGHTS_NAME)							->	Tuple:







			"""simple docstring"""
			a__       :						Tuple							=  []
			a__       :						Optional[Any]							=  0
			os.makedirs(_lowercase						,       exist_ok=_lowercase)

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

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

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

			return metadata, index


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

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




