



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

UpperCAmelCase_							:      Any      =  {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}
def  UpperCamelCase						(       _A			:				Dict    )->       Optional[int]:





						"""simple docstring"""





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

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

												A__							=       weight
						return state_dict
def  UpperCamelCase						(       _A			:				str			,							_A			:				List[Any]			,							_A			:				List[Any]			,							_A			:				int=None			,							_A			:				List[str]=None			,							_A			:				Dict=False			,							_A			:				List[Any]=None    )->       str:





						"""simple docstring"""





						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."    )
												A__							=       50277
												A__							=       AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"    )
						else:
												A__							=       PreTrainedTokenizerFast(tokenizer_file=_A    )
												A__							=       len(_A    )
						tokenizer.save_pretrained(_A    )

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

						A__							=       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
						A__							=       hf_hub_download(_A			,							_A    )
						A__							=       torch.load(_A			,							map_location="cpu"    )
						A__							=       convert_state_dict(_A    )

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

						if index is not None:
												A__							=       os.path.join(_A			,							_A    )
												# Save the index as well
												with open(_A			,							"w"			,							encoding="utf-8"    ) as f:
																		A__							=       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."    )
												A__							=       list(shards.keys()    )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		A__							=       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."    )
												A__							=       AutoModelForCausalLM.from_pretrained(_A    )
												model.push_to_hub(_A			,							max_shard_size="2GB"    )
												tokenizer.push_to_hub(_A    )


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_							:      Optional[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 math
from collections.abc import Iterator
from itertools import takewhile
def  UpperCamelCase						(       _A			:				int    )->       bool:





						"""simple docstring"""





						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5			,							int(math.sqrt(_A    ) + 1    )			,							6    ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def  UpperCamelCase						(       )->       Iterator[int]:





						"""simple docstring"""





						A__							=       2
						while True:
												if is_prime(_A    ):
																		yield num
												num += 1
def  UpperCamelCase						(       _A			:				int = 2000000    )->       int:





						"""simple docstring"""





						return sum(takewhile(lambda _A    : x < n			,							prime_generator()    )    )


if __name__ == "__main__":
						print(F'''{solution() = }''')





