



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
      import jieba

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


UpperCAmelCase_							:      Optional[int]      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Union[str, Any]      =  {"vocab_file": "vocab.txt"}

UpperCAmelCase_							:      int      =  {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

UpperCAmelCase_							:      Dict      =  {
    "openbmb/cpm-ant-10b": 1_024,
}
def  UpperCamelCase						(       _A			:				Union[str, Any]    )->       str:





      """simple docstring"""





      A__							=       collections.OrderedDict()
      with open(__A			,							"r"			,							encoding="utf-8"    ) as reader:
            A__							=       reader.readlines()
      for index, token in enumerate(__A    ):
            A__							=       token.rstrip("\n"    )
            A__							=       index
      return vocab






class      UpperCamelCase    (						_UpperCAmelCase     ):



 def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__=200		):
       A__							=       vocab
       A__							=       unk_token
       A__							=       max_input_chars_per_word



 def 			__A							(       self			,	UpperCAmelCase__		):
       A__							=       list(_snake_case		)
       if len(_snake_case		) > self.max_input_chars_per_word:
             return [self.unk_token]

       A__							=       0
       A__							=       []
       while start < len(_snake_case		):
             A__							=       len(_snake_case		)
             A__							=       None
             while start < end:
                   A__							=       "".join(chars[start:end]		)
                   if substr in self.vocab:
                         A__							=       substr
                         break
                   end -= 1
             if cur_substr is None:
                   sub_tokens.append(self.unk_token		)
                   start += 1
             else:
                   sub_tokens.append(_snake_case		)
                   A__							=       end

       return sub_tokens






class      UpperCamelCase    (						_UpperCAmelCase     ):
 lowerCAmelCase	:   Dict				=	VOCAB_FILES_NAMES
 lowerCAmelCase	:   Optional[Any]				=	PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase	:   List[str]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase	:   str				=	['''input_ids''', '''attention_mask''']
 lowerCAmelCase	:   List[Any]				=	False



 def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__="<d>"			,	UpperCAmelCase__="</d>"			,	UpperCAmelCase__="<s>"			,	UpperCAmelCase__="</s>"			,	UpperCAmelCase__="<pad>"			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__="</n>"			,	UpperCAmelCase__="</_>"			,	UpperCAmelCase__="left"			,	**UpperCAmelCase__			,	):
       requires_backends(self			,	["jieba"]		)
       super().__init__(
           bod_token=_snake_case			,	eod_token=_snake_case			,	bos_token=_snake_case			,	eos_token=_snake_case			,	pad_token=_snake_case			,	unk_token=_snake_case			,	line_token=_snake_case			,	space_token=_snake_case			,	padding_side=_snake_case			,	**_snake_case			,	)
       A__							=       bod_token
       A__							=       eod_token
       A__							=       load_vocab(_snake_case		)
       A__							=       self.encoder[space_token]
       A__							=       self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       A__							=       collections.OrderedDict(sorted(self.encoder.items()			,	key=lambda UpperCAmelCase__		: x[1]		)		)
       A__							=       {v: k for k, v in self.encoder.items()}

       A__							=       WordpieceTokenizer(vocab=self.encoder			,	unk_token=self.unk_token		)



 @property
 def 			__A							(       self		):
       return self.encoder[self.bod_token]



 @property
 def 			__A							(       self		):
       return self.encoder[self.eod_token]



 @property
 def 			__A							(       self		):
       return self.encoder["\n"]



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



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



 def 			__A							(       self			,	UpperCAmelCase__		):
       A__							=       []
       for x in jieba.cut(_snake_case			,	cut_all=_snake_case		):
             output_tokens.extend(self.wordpiece_tokenizer.tokenize(_snake_case		)		)
       return output_tokens



 def 			__A							(       self			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
       A__							=       [i for i in token_ids if i >= 0]
       A__							=       [
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(_snake_case			,	**_snake_case		)



 def 			__A							(       self			,	UpperCAmelCase__		):
       return token in self.encoder



 def 			__A							(       self			,	UpperCAmelCase__		):
       return "".join(_snake_case		)



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



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



 def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):


       if os.path.isdir(_snake_case		):
             A__							=       os.path.join(
                 _snake_case			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
       else:
             A__							=       (filename_prefix + "-" if filename_prefix else "") + save_directory
       A__							=       0
       if " " in self.encoder:
             A__							=       self.encoder[" "]
             del self.encoder[" "]
       if "\n" in self.encoder:
             A__							=       self.encoder["\n"]
             del self.encoder["\n"]
       A__							=       collections.OrderedDict(sorted(self.encoder.items()			,	key=lambda UpperCAmelCase__		: x[1]		)		)
       with open(_snake_case			,	"w"			,	encoding="utf-8"		) as writer:
             for token, token_index in self.encoder.items():
                   if index != token_index:
                         logger.warning(
                             F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                             " Please check that the vocabulary is not corrupted!"		)
                         A__							=       token_index
                   writer.write(token + "\n"		)
                   index += 1
       return (vocab_file,)



 def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):


       if token_ids_a is None:
             return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



 def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = False		):


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

       if token_ids_a is not None:
             return [1] + ([0] * len(_snake_case		)) + [1] + ([0] * len(_snake_case		))
       return [1] + ([0] * len(_snake_case		))










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,
						)





