



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

import sentencepiece as spm

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


UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Dict      =  {"vocab_file": "spiece.model"}

UpperCAmelCase_							:      Tuple      =  {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=False			,	UpperCAmelCase__=True			,	UpperCAmelCase__=False			,	UpperCAmelCase__="<s>"			,	UpperCAmelCase__="</s>"			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__="<sep>"			,	UpperCAmelCase__="<pad>"			,	UpperCAmelCase__="<cls>"			,	UpperCAmelCase__="<mask>"			,	UpperCAmelCase__=["<eop>", "<eod>"]			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							A__							=       AddedToken(UpperCAmelCase__			,	lstrip=UpperCAmelCase__			,	rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) else mask_token

							A__							=       {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=UpperCAmelCase__			,	remove_space=UpperCAmelCase__			,	keep_accents=UpperCAmelCase__			,	bos_token=UpperCAmelCase__			,	eos_token=UpperCAmelCase__			,	unk_token=UpperCAmelCase__			,	sep_token=UpperCAmelCase__			,	pad_token=UpperCAmelCase__			,	cls_token=UpperCAmelCase__			,	mask_token=UpperCAmelCase__			,	additional_special_tokens=UpperCAmelCase__			,	sp_model_kwargs=self.sp_model_kwargs			,	**UpperCAmelCase__			,	)

							A__							=       3

							A__							=       do_lower_case
							A__							=       remove_space
							A__							=       keep_accents
							A__							=       vocab_file

							A__							=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(UpperCAmelCase__		)

							try:
													import jieba
							except ModuleNotFoundError as error:
													raise error.__class__(
													    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
													    "See https://pypi.org/project/jieba/ for installation."		)
							A__							=       jieba
							A__							=       str.maketrans(" \n"			,	"\u2582\u2583"		)



	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def 			__A							(       self		):
							return len(self.sp_model		)



	def 			__A							(       self		):
							A__							=       {self.convert_ids_to_tokens(UpperCAmelCase__		): i for i in range(self.vocab_size		)}
							vocab.update(self.added_tokens_encoder		)
							return vocab



	def __getstate__(       self		):
							A__							=       self.__dict__.copy()
							A__							=       None
							return state



	def __setstate__(       self			,	UpperCAmelCase__		):
							A__							=       d

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

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



	def 			__A							(       self			,	UpperCAmelCase__		):
							if self.remove_space:
													A__							=       " ".join(inputs.strip().split()		)
							else:
													A__							=       inputs
							A__							=       outputs.replace("``"			,	"\""		).replace("''"			,	"\""		)

							if not self.keep_accents:
													A__							=       unicodedata.normalize("NFKD"			,	UpperCAmelCase__		)
													A__							=       "".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase__		)]		)
							if self.do_lower_case:
													A__							=       outputs.lower()

							return outputs



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.preprocess_text(UpperCAmelCase__		)
							A__							=       self.sp_model.encode(UpperCAmelCase__			,	out_type=UpperCAmelCase__		)
							A__							=       []
							for piece in pieces:
													if len(UpperCAmelCase__		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
																			A__							=       self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase__			,	""		)		)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																									if len(cur_pieces[0]		) == 1:
																															A__							=       cur_pieces[1:]
																									else:
																															A__							=       cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]		)
																			new_pieces.extend(UpperCAmelCase__		)
													else:
																			new_pieces.append(UpperCAmelCase__		)

							return new_pieces



	def 			__A							(       self			,	UpperCAmelCase__		):
							return self.sp_model.PieceToId(UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							return self.sp_model.IdToPiece(UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       "".join(UpperCAmelCase__		).replace(UpperCAmelCase__			,	" "		).strip()
							return out_string



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



	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 not None:
													return ([0] * len(UpperCAmelCase__		)) + [1] + ([0] * len(UpperCAmelCase__		)) + [1, 1]
							return ([0] * len(UpperCAmelCase__		)) + [1, 1]



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       [self.sep_token_id]
							A__							=       [2]

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



	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"]		)

							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:
																			A__							=       self.sp_model.serialized_model_proto()
																			fi.write(UpperCAmelCase__		)

							return (out_vocab_file,)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							A__							=       super()._decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)
							A__							=       text.replace(" "			,	""		).replace("\u2582"			,	" "		).replace("\u2583"			,	"\n"		)
							return text










import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


UpperCAmelCase_							:      Dict      =  {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

UpperCAmelCase_							:      Any      =  {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}
def  UpperCamelCase						(       _A			:				Dict    )->       Optional[int]:





						"""simple docstring"""





						A__							=       list(state_dict.keys()    )
						for name in state_dict_keys:
												A__							=       state_dict.pop(_A    )
												# emb -> embedding
												if name.startswith("emb."    ):
																		A__							=       name.replace("emb."			,							"embeddings."    )
												# ln_0 -> pre_ln (only present at block 0)
												if name.startswith("blocks.0.ln0"    ):
																		A__							=       name.replace("blocks.0.ln0"			,							"blocks.0.pre_ln"    )
												# att -> attention
												A__							=       re.sub(R"blocks\.(\d+)\.att"			,							R"blocks.\1.attention"			,							_A    )
												# ffn -> feed_forward
												A__							=       re.sub(R"blocks\.(\d+)\.ffn"			,							R"blocks.\1.feed_forward"			,							_A    )
												# time_mix_k -> time_mix_key and reshape
												if name.endswith(".time_mix_k"    ):
																		A__							=       name.replace(".time_mix_k"			,							".time_mix_key"    )
												# time_mix_v -> time_mix_value and reshape
												if name.endswith(".time_mix_v"    ):
																		A__							=       name.replace(".time_mix_v"			,							".time_mix_value"    )
												# time_mix_r -> time_mix_key and reshape
												if name.endswith(".time_mix_r"    ):
																		A__							=       name.replace(".time_mix_r"			,							".time_mix_receptance"    )

												if name != "head.weight":
																		A__							=       "rwkv." + name

												A__							=       weight
						return state_dict
def  UpperCamelCase						(       _A			:				str			,							_A			:				List[Any]			,							_A			:				List[Any]			,							_A			:				int=None			,							_A			:				List[str]=None			,							_A			:				Dict=False			,							_A			:				List[Any]=None    )->       str:





						"""simple docstring"""





						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."    )
												A__							=       50277
												A__							=       AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"    )
						else:
												A__							=       PreTrainedTokenizerFast(tokenizer_file=_A    )
												A__							=       len(_A    )
						tokenizer.save_pretrained(_A    )

						# 2. Build the config
						A__							=       list(NUM_HIDDEN_LAYERS_MAPPING.keys()    )
						if size is None:
												# Try to infer size from the checkpoint name
												for candidate in possible_sizes:
																		if candidate in checkpoint_file:
																								A__							=       candidate
																								break
												if size is None:
																		raise ValueError("Could not infer the size, please provide it with the `--size` argument."    )
						if size not in possible_sizes:
												raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}."""    )

						A__							=       RwkvConfig(
						    vocab_size=_A			,							num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,							hidden_size=HIDEN_SIZE_MAPPING[size]			,							)
						config.save_pretrained(_A    )

						# 3. Download model file then convert state_dict
						A__							=       hf_hub_download(_A			,							_A    )
						A__							=       torch.load(_A			,							map_location="cpu"    )
						A__							=       convert_state_dict(_A    )

						# 4. Split in shards and save
						A__						,       A__							=       shard_checkpoint(_A    )
						for shard_file, shard in shards.items():
												torch.save(_A			,							os.path.join(_A			,							_A    )    )

						if index is not None:
												A__							=       os.path.join(_A			,							_A    )
												# Save the index as well
												with open(_A			,							"w"			,							encoding="utf-8"    ) as f:
																		A__							=       json.dumps(_A			,							indent=2			,							sort_keys=_A    ) + "\n"
																		f.write(_A    )

												# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
												print(
												    "Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."    )
												A__							=       list(shards.keys()    )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		A__							=       torch.load(os.path.join(_A			,							_A    )    )
																		torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,							os.path.join(_A			,							_A    )    )

						del state_dict
						gc.collect()

						if push_to_hub:
												if model_name is None:
																		raise ValueError("Please provide a `model_name` to push the model to the Hub."    )
												A__							=       AutoModelForCausalLM.from_pretrained(_A    )
												model.push_to_hub(_A			,							max_shard_size="2GB"    )
												tokenizer.push_to_hub(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      Union[str, Any]      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
						)
						parser.add_argument(
						    "--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
						)
						parser.add_argument(
						    "--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
						)
						parser.add_argument(
						    "--tokenizer_file",
						    default=None,
						    type=str,
						    help="Path to the tokenizer file to use (if not provided, only the model is converted).",
						)
						parser.add_argument(
						    "--size",
						    default=None,
						    type=str,
						    help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
						)
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						    help="Push to the Hub the converted model.",
						)
						parser.add_argument(
						    "--model_name",
						    default=None,
						    type=str,
						    help="Name of the pushed model on the Hub, including the username / organization.",
						)

						UpperCAmelCase_							:      Optional[int]      =  parser.parse_args()
						convert_rmkv_checkpoint_to_hf_format(
						    args.repo_id,
						    args.checkpoint_file,
						    args.output_dir,
						    size=args.size,
						    tokenizer_file=args.tokenizer_file,
						    push_to_hub=args.push_to_hub,
						    model_name=args.model_name,
						)





