



'''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


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

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




def lowerCAmelCase						(__A):



					"""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 lowerCAmelCase						(__A						,			__A						,			__A						,			__A=None						,			__A=None						,			__A=False						,			__A=None):



					"""simple docstring"""



					if tokenizer_file is None:
										print('''No `--tokenizer_file` provided, we will use the default tokenizer.''')
										_a											=						50_277
										_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__":
		lowercase_	 =						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.",
		)

		lowercase_	 =						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 lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



