




'''simple docstring'''





import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





def      __a     (       _UpperCamelCase:   Features   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				np.inf

     def set_batch_size(_UpperCamelCase:   FeatureType   ) -> None:
          nonlocal batch_size
          if isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
               _snake_case							=				min(_UpperCamelCase ,						config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
          elif isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
               _snake_case							=				min(_UpperCamelCase ,						config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
          elif isinstance(_UpperCamelCase ,						_UpperCamelCase   ) and feature.dtype == "binary":
               _snake_case							=				min(_UpperCamelCase ,						config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

     _visit(_UpperCamelCase ,						_UpperCamelCase   )

     return None if batch_size is np.inf else batch_size

class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      Union[str, Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE ,split=_SCREAMING_SNAKE_CASE ,features=_SCREAMING_SNAKE_CASE ,cache_dir=_SCREAMING_SNAKE_CASE ,keep_in_memory=_SCREAMING_SNAKE_CASE ,streaming=_SCREAMING_SNAKE_CASE ,num_proc=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
          _snake_case							=				path_or_paths if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
          _snake_case							=				_PACKAGED_DATASETS_MODULES["parquet"][1]
          _snake_case							=				Parquet(
              cache_dir=_SCREAMING_SNAKE_CASE ,data_files=_SCREAMING_SNAKE_CASE ,features=_SCREAMING_SNAKE_CASE ,hash=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)






     def     _lowercase     (			self   )							->      Optional[Any]:
          # Build iterable dataset
          if self.streaming:
               _snake_case							=				self.builder.as_streaming_dataset(split=self.split   )
          # Build regular (map-style) dataset
          else:
               _snake_case							=				None
               _snake_case							=				None
               _snake_case							=				None
               _snake_case							=				None

               self.builder.download_and_prepare(
                   download_config=_SCREAMING_SNAKE_CASE ,download_mode=_SCREAMING_SNAKE_CASE ,verification_mode=_SCREAMING_SNAKE_CASE ,base_path=_SCREAMING_SNAKE_CASE ,num_proc=self.num_proc ,)
               _snake_case							=				self.builder.as_dataset(
                   split=self.split ,verification_mode=_SCREAMING_SNAKE_CASE ,in_memory=self.keep_in_memory   )
          return dataset







class        _a    :






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      Any:
          _snake_case							=				dataset
          _snake_case							=				path_or_buf
          _snake_case							=				batch_size or get_writer_batch_size(dataset.features   )
          _snake_case							=				parquet_writer_kwargs






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

          if isinstance(self.path_or_buf ,(str, bytes, os.PathLike)   ):
               with open(self.path_or_buf ,"wb+"   ) as buffer:
                    _snake_case							=				self._write(file_obj=_SCREAMING_SNAKE_CASE ,batch_size=_SCREAMING_SNAKE_CASE ,**self.parquet_writer_kwargs   )
          else:
               _snake_case							=				self._write(file_obj=self.path_or_buf ,batch_size=_SCREAMING_SNAKE_CASE ,**self.parquet_writer_kwargs   )
          return written






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      int:
          _snake_case							=				0
          _snake_case							=				parquet_writer_kwargs.pop("path_or_buf" ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.dataset.features.arrow_schema

          _snake_case							=				pq.ParquetWriter(_SCREAMING_SNAKE_CASE ,schema=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

          for offset in logging.tqdm(
              range(0 ,len(self.dataset   ) ,_SCREAMING_SNAKE_CASE   ) ,unit="ba" ,disable=not logging.is_progress_bar_enabled() ,desc="Creating parquet from Arrow format" ,):
               _snake_case							=				query_table(
                   table=self.dataset._data ,key=slice(_SCREAMING_SNAKE_CASE ,offset + batch_size   ) ,indices=self.dataset._indices if self.dataset._indices is not None else None ,)
               writer.write_table(_SCREAMING_SNAKE_CASE   )
               written += batch.nbytes
          writer.close()
          return written








'''simple docstring'''





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_						:      int       =						{
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

UpperCamelCase_						:      str       =						{
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}





def      __a     (       _UpperCamelCase:   str   )						->			Any:

     """simple docstring"""




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

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

          _snake_case							=				weight
     return state_dict





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   str=None ,						_UpperCamelCase:   Optional[Any]=None ,						_UpperCamelCase:   List[str]=False ,						_UpperCamelCase:   Dict=None   )						->			Dict:

     """simple docstring"""




     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
          _snake_case							=				50_277
          _snake_case							=				AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
     else:
          _snake_case							=				PreTrainedTokenizerFast(tokenizer_file=_UpperCamelCase   )
          _snake_case							=				len(_UpperCamelCase   )
     tokenizer.save_pretrained(_UpperCamelCase   )

     # 2. Build the config
     _snake_case							=				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:
                    _snake_case							=				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}."""   )

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

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

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

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

          # 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."   )
          _snake_case							=				list(shards.keys()   )

          del state_dict
          del shards
          gc.collect()

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

     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."   )
          _snake_case							=				AutoModelForCausalLM.from_pretrained(_UpperCamelCase   )
          model.push_to_hub(_UpperCamelCase ,						max_shard_size="2GB"   )
          tokenizer.push_to_hub(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      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_						:      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,
   )


