
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					   =       {
    '169M': 1_2,
    '430M': 2_4,
    '1B5': 2_4,
    '3B': 3_2,
    '7B': 3_2,
    '14B': 4_0,
}

__lowerCAmelCase					   =       {
    '169M': 7_6_8,
    '430M': 1_0_2_4,
    '1B5': 2_0_4_8,
    '3B': 2_5_6_0,
    '7B': 4_0_9_6,
    '14B': 5_1_2_0,
}


def    a      ( a	)     ->str:




			'''simple docstring'''


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

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

						SCREAMING_SNAKE_CASE     						= weight
			return state_dict


def    a      ( a		,						a		,						a		,						a=None		,						a=None		,						a=False		,						a=None	)     ->Union[str, Any]:




			'''simple docstring'''


			if tokenizer_file is None:
						print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''	)
						SCREAMING_SNAKE_CASE     						= 5_0277
						SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''	)
			else:
						SCREAMING_SNAKE_CASE     						= PreTrainedTokenizerFast(tokenizer_file=a	)
						SCREAMING_SNAKE_CASE     						= len(a	)
			tokenizer.save_pretrained(a	)

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

			SCREAMING_SNAKE_CASE     						= 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
			SCREAMING_SNAKE_CASE     						= hf_hub_download(a		,						a	)
			SCREAMING_SNAKE_CASE     						= torch.load(a		,						map_location='''cpu'''	)
			SCREAMING_SNAKE_CASE     						= convert_state_dict(a	)

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

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

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									SCREAMING_SNAKE_CASE     						= 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.'''	)
						SCREAMING_SNAKE_CASE     						= AutoModelForCausalLM.from_pretrained(a	)
						model.push_to_hub(a		,						max_shard_size='''2GB'''	)
						tokenizer.push_to_hub(a	)


if __name__ == "__main__":
							__lowerCAmelCase					   =       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					   =       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,
							)

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()