



"""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


A		=						{
    '169M': 1_2,
    '430M': 2_4,
    '1B5': 2_4,
    '3B': 3_2,
    '7B': 3_2,
    '14B': 4_0,
}

A		=						{
    '169M': 7_6_8,
    '430M': 1_0_2_4,
    '1B5': 2_0_4_8,
    '3B': 2_5_6_0,
    '7B': 4_0_9_6,
    '14B': 5_1_2_0,
}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






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

												if name != "head.weight":
																		snake_case   :			str						    = "rwkv." + name

												snake_case   :			Union[str, Any]						    = weight
						return state_dict

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Optional[int]=None     ,		lowerCamelCase_:							str=None     ,		lowerCamelCase_:							Optional[Any]=False     ,		lowerCamelCase_:							Optional[Any]=None ):
						"""simple docstring"""






						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer." )
												snake_case   :			Tuple						    = 5_0_2_7_7
												snake_case   :			List[Any]						    = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b" )
						else:
												snake_case   :			Any						    = PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_ )
												snake_case   :			Tuple						    = len(lowerCamelCase_ )
						tokenizer.save_pretrained(lowerCamelCase_ )

						# 2. Build the config
						snake_case   :			Union[str, Any]						    = 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   :			Optional[Any]						    = 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   :			Tuple						    = RwkvConfig(
						    vocab_size=lowerCamelCase_     ,		num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,		hidden_size=HIDEN_SIZE_MAPPING[size]     ,		)
						config.save_pretrained(lowerCamelCase_ )

						# 3. Download model file then convert state_dict
						snake_case   :			int						    = hf_hub_download(lowerCamelCase_     ,		lowerCamelCase_ )
						snake_case   :			Optional[int]						    = torch.load(lowerCamelCase_     ,		map_location="cpu" )
						snake_case   :			List[Any]						    = convert_state_dict(lowerCamelCase_ )

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

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

												# 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   :			Tuple						    = list(shards.keys() )

												del state_dict
												del shards
												gc.collect()

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

						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   :			Union[str, Any]						    = AutoModelForCausalLM.from_pretrained(lowerCamelCase_ )
												model.push_to_hub(lowerCamelCase_     ,		max_shard_size="2GB" )
												tokenizer.push_to_hub(lowerCamelCase_ )


if __name__ == "__main__":
			A		=						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.',
			)

			A		=						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,
			)








"""simple docstring"""







import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


A		=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
			from .safilesystem import SaFileSystem  # noqa: F401

A		=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
			if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
						warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
			fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						if "://" in dataset_path:
												snake_case   :			List[str]						    = dataset_path.split("://" )[1]
						return dataset_path

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							fsspec.AbstractFileSystem ):
						"""simple docstring"""






						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							fsspec.AbstractFileSystem     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			int						    = not is_remote_filesystem(lowerCamelCase_ )
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(lowerCamelCase_ )     ,		fs._strip_protocol(lowerCamelCase_ ) )
						else:
												fs.mv(lowerCamelCase_     ,		lowerCamelCase_     ,		recursive=lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						if hasattr(fsspec.asyn     ,		"reset_lock" ):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												snake_case   :			Union[str, Any]						    = None
												snake_case   :			Optional[int]						    = None
												snake_case   :			List[str]						    = threading.Lock()



