




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


lowerCAmelCase							:    List[str]  							=							{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

lowerCAmelCase							:    Optional[Any]  							=							{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}



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

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

						lowerCamelCase       =      weight
			return state_dict



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=None ,				snake_case__=False ,				snake_case__=None       )		->      int:
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print("""No `--tokenizer_file` provided, we will use the default tokenizer."""       )
						lowerCamelCase       =      5_02_77
						lowerCamelCase       =      AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""       )
			else:
						lowerCamelCase       =      PreTrainedTokenizerFast(tokenizer_file=snake_case__       )
						lowerCamelCase       =      len(snake_case__       )
			tokenizer.save_pretrained(snake_case__       )

			# 2. Build the config
			lowerCamelCase       =      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:
												lowerCamelCase       =      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}.'       )

			lowerCamelCase       =      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
			lowerCamelCase       =      hf_hub_download(snake_case__ ,				snake_case__       )
			lowerCamelCase       =      torch.load(snake_case__ ,				map_location="""cpu"""       )
			lowerCamelCase       =      convert_state_dict(snake_case__       )

			# 4. Split in shards and save
			lowerCamelCase   ,     lowerCamelCase       =      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:
						lowerCamelCase       =      os.path.join(snake_case__ ,				snake_case__       )
						# Save the index as well
						with open(snake_case__ ,				"""w""" ,				encoding="""utf-8"""       ) as f:
									lowerCamelCase       =      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."""       )
						lowerCamelCase       =      list(shards.keys()       )

						del state_dict
						del shards
						gc.collect()

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


if __name__ == "__main__":
					lowerCAmelCase							:    int  							=							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.""",
					)

					lowerCAmelCase							:    Optional[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"""




def 					a__				(       snake_case__       )		->      list:
			if n_term == "":
						return []
			lowerCamelCase       =      []
			for temp in range(int(snake_case__       )       ):
						series.append(F'1/{temp + 1}' if series else """1"""       )
			return series


if __name__ == "__main__":
					lowerCAmelCase							:    Optional[int]  							=							input("""Enter the last number (nth term) of the Harmonic Series""")
					print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
					print(harmonic_series(nth_term))



