






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

A:      Dict  				=				{
    "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 	_UpperCAmelCase    (  a		:	Optional[Any]							)			->			Tuple:





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

												if name != "head.weight":
																		lowercase_      :   Dict        =			'rwkv.' + name

												lowercase_      :   Any        =			weight
						return state_dict






def 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Any						,			a		:	List[str]						,			a		:	Dict=None						,			a		:	Tuple=None						,			a		:	int=False						,			a		:	Any=None							)			->			Dict:





						"""simple docstring"""
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print('No `--tokenizer_file` provided, we will use the default tokenizer.'							)
												lowercase_      :   int        =			5_0_2_7_7
												lowercase_      :   Union[str, Any]        =			AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'							)
						else:
												lowercase_      :   Dict        =			PreTrainedTokenizerFast(tokenizer_file=a							)
												lowercase_      :   Tuple        =			len(a							)
						tokenizer.save_pretrained(a							)

						# 2. Build the config
						lowercase_      :   Dict        =			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:
																								lowercase_      :   Union[str, 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}."							)

						lowercase_      :   List[Any]        =			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
						lowercase_      :   Union[str, Any]        =			hf_hub_download(a						,			a							)
						lowercase_      :   Optional[int]        =			torch.load(a						,			map_location='cpu'							)
						lowercase_      :   Tuple        =			convert_state_dict(a							)

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

						if index is not None:
												lowercase_      :   List[Any]        =			os.path.join(a						,			a							)
												# Save the index as well
												with open(a						,			'w'						,			encoding='utf-8'							) as f:
																		lowercase_      :   int        =			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.'							)
												lowercase_      :   List[Any]        =			list(shards.keys()							)

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		lowercase_      :   Any        =			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.'							)
												lowercase_      :   List[str]        =			AutoModelForCausalLM.from_pretrained(a							)
												model.push_to_hub(a						,			max_shard_size='2GB'							)
												tokenizer.push_to_hub(a							)


if __name__ == "__main__":
			A:      Tuple  				=				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:      Union[str, Any]  				=				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'''

from typing import TYPE_CHECKING

from ..utils import _LazyModule


A:      int  				=				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

						A:      Dict  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
