





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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None     ,							UpperCamelCase__=None     ,							UpperCamelCase__=False     ,							UpperCamelCase__=None   ):







							'''simple docstring'''






							if tokenizer_file is None:
														print('No `--tokenizer_file` provided, we will use the default tokenizer.'   )
														snake_case_           =					50277
														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	:     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	:     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,
					)







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       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.inf

							def set_batch_size(UpperCamelCase__   ) -> 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 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = None   ,     **snake_case   ,     ):
											super().__init__(
											    snake_case   ,     split=snake_case   ,     features=snake_case   ,     cache_dir=snake_case   ,     keep_in_memory=snake_case   ,     streaming=snake_case   ,     num_proc=snake_case   ,     **snake_case   ,     )
											snake_case_           =					path_or_paths if isinstance(snake_case   ,     snake_case ) else {self.split: path_or_paths}
											snake_case_           =					_PACKAGED_DATASETS_MODULES['parquet'][1]
											snake_case_           =					Parquet(
											    cache_dir=snake_case   ,     data_files=snake_case   ,     features=snake_case   ,     hash=snake_case   ,     **snake_case   ,     )




				def 					a							(      self ):
											# 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=snake_case   ,     download_mode=snake_case   ,     verification_mode=snake_case   ,     base_path=snake_case   ,     num_proc=self.num_proc   ,     )
																		snake_case_           =					self.builder.as_dataset(
																		    split=self.split   ,     verification_mode=snake_case   ,     in_memory=self.keep_in_memory )
											return dataset







class 				lowercase       :




				def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											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 					a							(      self ):
											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=snake_case   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											else:
																		snake_case_           =					self._write(file_obj=self.path_or_buf   ,     batch_size=snake_case   ,     **self.parquet_writer_kwargs )
											return written




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     **snake_case ):
											snake_case_           =					0
											snake_case_           =					parquet_writer_kwargs.pop('path_or_buf'   ,     snake_case )
											snake_case_           =					self.dataset.features.arrow_schema

											snake_case_           =					pq.ParquetWriter(snake_case   ,     schema=snake_case   ,     **snake_case )

											for offset in logging.tqdm(
											    range(0   ,     len(self.dataset )   ,     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(snake_case   ,     offset + batch_size )   ,     indices=self.dataset._indices if self.dataset._indices is not None else None   ,     )
																		writer.write_table(snake_case )
																		written += batch.nbytes
											writer.close()
											return written




