


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


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

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




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

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

						UpperCamelCase         =					weight
			return state_dict






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		str=None       ,  SCREAMING_SNAKE_CASE_   :		Any=None       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=False       ,  SCREAMING_SNAKE_CASE_   :		Tuple=None	):





			"""simple docstring"""




			if tokenizer_file is None:
						print("""No `--tokenizer_file` provided, we will use the default tokenizer."""	)
						UpperCamelCase         =					50_277
						UpperCamelCase         =					AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""	)
			else:
						UpperCamelCase         =					PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					len(SCREAMING_SNAKE_CASE_	)
			tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)

			# 2. Build the config
			UpperCamelCase         =					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         =					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         =					RwkvConfig(
			    vocab_size=SCREAMING_SNAKE_CASE_       ,  num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]       ,  hidden_size=HIDEN_SIZE_MAPPING[size]       ,  )
			config.save_pretrained(SCREAMING_SNAKE_CASE_	)

			# 3. Download model file then convert state_dict
			UpperCamelCase         =					hf_hub_download(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					torch.load(SCREAMING_SNAKE_CASE_       ,  map_location="""cpu"""	)
			UpperCamelCase         =					convert_state_dict(SCREAMING_SNAKE_CASE_	)

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

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

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									UpperCamelCase         =					torch.load(os.path.join(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)	)
									torch.save({k: v.cpu().clone() for k, v in state_dict.items()}       ,  os.path.join(SCREAMING_SNAKE_CASE_       ,  SCREAMING_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         =					AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_	)
						model.push_to_hub(SCREAMING_SNAKE_CASE_       ,  max_shard_size="""2GB"""	)
						tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
			__snake_case            =   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.",
			)

			__snake_case            =   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
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__snake_case            =   3






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Generating primitive root of p"""	)
			while True:
						UpperCamelCase         =					random.randrange(3       ,  SCREAMING_SNAKE_CASE_	)
						if pow(SCREAMING_SNAKE_CASE_       ,  2       ,  SCREAMING_SNAKE_CASE_	) == 1:
									continue
						if pow(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) == 1:
									continue
						return g






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Generating prime p..."""	)
			UpperCamelCase         =					rabin_miller.generate_large_prime(SCREAMING_SNAKE_CASE_	)  # select large prime number.
			UpperCamelCase         =					primitive_root(SCREAMING_SNAKE_CASE_	)  # one primitive root on modulo p.
			UpperCamelCase         =					random.randrange(3       ,  SCREAMING_SNAKE_CASE_	)  # private_key -> have to be greater than 2 for safety.
			UpperCamelCase         =					cryptomath.find_mod_inverse(pow(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					(key_size, e_a, e_a, p)
			UpperCamelCase         =					(key_size, d)

			return public_key, private_key






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			if os.path.exists(f'{name}_pubkey.txt'	) or os.path.exists(f'{name}_privkey.txt'	):
						print("""\nWARNING:"""	)
						print(
						    f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
						    """Use a different name or delete these files and re-run this program."""	)
						sys.exit()

			UpperCamelCase  ,  UpperCamelCase         =					generate_key(SCREAMING_SNAKE_CASE_	)
			print(f'\nWriting public key to file {name}_pubkey.txt...'	)
			with open(f'{name}_pubkey.txt'       ,  """w"""	) as fo:
						fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'	)

			print(f'Writing private key to file {name}_privkey.txt...'	)
			with open(f'{name}_privkey.txt'       ,  """w"""	) as fo:
						fo.write(f'{private_key[0]},{private_key[1]}'	)






def 							_lowercase    (		):





			"""simple docstring"""




			print("""Making key files..."""	)
			make_key_files("""elgamal"""       ,  2_048	)
			print("""Key files generation successful"""	)


if __name__ == "__main__":
			main()




