





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

UpperCamelCase_									={
    '169M': 768,
    '430M': 1_024,
    '1B5': 2_048,
    '3B': 2_560,
    '7B': 4_096,
    '14B': 5_120,
}





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

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

												_UpperCamelCase      : Optional[int]         =   weight
						return state_dict





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=False	,						_lowercase=None       ):
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
												_UpperCamelCase      : List[str]         =   5_0277
												_UpperCamelCase      : Optional[Any]         =   AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
						else:
												_UpperCamelCase      : Union[str, Any]         =   PreTrainedTokenizerFast(tokenizer_file=snake_case_       )
												_UpperCamelCase      : int         =   len(snake_case_       )
						tokenizer.save_pretrained(snake_case_       )

						# 2. Build the config
						_UpperCamelCase      : List[str]         =   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:
																								_UpperCamelCase      : Optional[int]         =   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}."""       )

						_UpperCamelCase      : Optional[int]         =   RwkvConfig(
						    vocab_size=snake_case_	,						num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]	,						hidden_size=HIDEN_SIZE_MAPPING[size]	,						)
						config.save_pretrained(snake_case_       )

						# 3. Download model file then convert state_dict
						_UpperCamelCase      : str         =   hf_hub_download(snake_case_	,						snake_case_       )
						_UpperCamelCase      : List[Any]         =   torch.load(snake_case_	,						map_location='''cpu'''       )
						_UpperCamelCase      : Optional[int]         =   convert_state_dict(snake_case_       )

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

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

												# 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.'''       )
												_UpperCamelCase      : Union[str, Any]         =   list(shards.keys()       )

												del state_dict
												del shards
												gc.collect()

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

						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.'''       )
												_UpperCamelCase      : List[str]         =   AutoModelForCausalLM.from_pretrained(snake_case_       )
												model.push_to_hub(snake_case_	,						max_shard_size='''2GB'''       )
												tokenizer.push_to_hub(snake_case_       )


if __name__ == "__main__":
						UpperCamelCase_									=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_									=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 __future__ import annotations

import math





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):

						if depth < 0:
												raise ValueError('''Depth cannot be less than 0'''       )

						if len(_lowercase       ) == 0:
												raise ValueError('''Scores cannot be empty'''       )

						if depth == height:
												return scores[node_index]

						if is_max:
												return max(
												    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)

						return min(
						    minimax(depth + 1	,						node_index * 2	,						_lowercase	,						_lowercase	,						_lowercase       )	,						minimax(depth + 1	,						node_index * 2 + 1	,						_lowercase	,						_lowercase	,						_lowercase       )	,						)





def 	a_    (							):
						_UpperCamelCase      : Dict         =   [90, 23, 6, 33, 21, 65, 123, 3_4423]
						_UpperCamelCase      : int         =   math.log(len(_lowercase       )	,						2       )
						print('''Optimal value : '''	,						end=''''''       )
						print(minimax(0	,						0	,						_lowercase	,						_lowercase	,						_lowercase       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()



