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

lowercase  :					Tuple													=       {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    int:






							'''simple docstring'''





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

														if name != "head.weight":
																					__UpperCamelCase	:   Optional[Any]			   =					"rwkv." + name

														__UpperCamelCase	:   int			   =					weight
							return state_dict




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[Any]						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			Any=None						,  _lowerCamelCase :			Dict=None						,  _lowerCamelCase :			List[str]=False						,  _lowerCamelCase :			List[str]=None)					->    Union[str, Any]:






							'''simple docstring'''





							if tokenizer_file is None:
														print("No `--tokenizer_file` provided, we will use the default tokenizer.")
														__UpperCamelCase	:   Optional[Any]			   =					50_277
														__UpperCamelCase	:   List[Any]			   =					AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
							else:
														__UpperCamelCase	:   List[str]			   =					PreTrainedTokenizerFast(tokenizer_file=_lowerCamelCase)
														__UpperCamelCase	:   Optional[Any]			   =					len(_lowerCamelCase)
							tokenizer.save_pretrained(_lowerCamelCase)

							# 2. Build the config
							__UpperCamelCase	:   int			   =					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	:   Union[str, Any]			   =					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	:   int			   =					RwkvConfig(
							    vocab_size=_lowerCamelCase						,  num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]						,  hidden_size=HIDEN_SIZE_MAPPING[size]						,  )
							config.save_pretrained(_lowerCamelCase)

							# 3. Download model file then convert state_dict
							__UpperCamelCase	:   Union[str, Any]			   =					hf_hub_download(_lowerCamelCase						,  _lowerCamelCase)
							__UpperCamelCase	:   Tuple			   =					torch.load(_lowerCamelCase						,  map_location="cpu")
							__UpperCamelCase	:   Optional[int]			   =					convert_state_dict(_lowerCamelCase)

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

							if index is not None:
														__UpperCamelCase	:   Union[str, Any]			   =					os.path.join(_lowerCamelCase						,  _lowerCamelCase)
														# Save the index as well
														with open(_lowerCamelCase						,  "w"						,  encoding="utf-8") as f:
																					__UpperCamelCase	:   Union[str, Any]			   =					json.dumps(_lowerCamelCase						,  indent=2						,  sort_keys=_lowerCamelCase) + "\n"
																					f.write(_lowerCamelCase)

														# 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[str]			   =					list(shards.keys())

														del state_dict
														del shards
														gc.collect()

														for shard_file in shard_files:
																					__UpperCamelCase	:   int			   =					torch.load(os.path.join(_lowerCamelCase						,  _lowerCamelCase))
																					torch.save({k: v.cpu().clone() for k, v in state_dict.items()}						,  os.path.join(_lowerCamelCase						,  _lowerCamelCase))

							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	:   Optional[Any]			   =					AutoModelForCausalLM.from_pretrained(_lowerCamelCase)
														model.push_to_hub(_lowerCamelCase						,  max_shard_size="2GB")
														tokenizer.push_to_hub(_lowerCamelCase)


if __name__ == "__main__":
		lowercase  :					Dict													=       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  :					List[str]													=       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,
		)

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]						)		-> None:
								__UpperCamelCase	:   dict[str, TrieNode]			   =					{}  # Mapping from char to TrieNode
								__UpperCamelCase	:   List[str]			   =					False







	def    _lowerCamelCase				(					self		:Any     ,   a		:list[str]						)		-> None:
								for word in words:
															self.insert(a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str						)		-> None:
								__UpperCamelCase	:   Dict			   =					self
								for char in word:
															if char not in curr.nodes:
																						__UpperCamelCase	:   List[Any]			   =					TrieNode()
															__UpperCamelCase	:   List[Any]			   =					curr.nodes[char]
								__UpperCamelCase	:   Union[str, Any]			   =					True







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> bool:
								__UpperCamelCase	:   Union[str, Any]			   =					self
								for char in word:
															if char not in curr.nodes:
																						return False
															__UpperCamelCase	:   Union[str, Any]			   =					curr.nodes[char]
								return curr.is_leaf







	def    _lowerCamelCase				(					self		:Any     ,   a		:str						)		-> None:

								def _delete(a		:TrieNode     ,   a		:str     ,   a		:int						) -> bool:
															if index == len(a						):
																						# If word does not exist
																						if not curr.is_leaf:
																													return False
																						__UpperCamelCase	:   str			   =					False
																						return len(curr.nodes						) == 0
															__UpperCamelCase	:   List[Any]			   =					word[index]
															__UpperCamelCase	:   Optional[int]			   =					curr.nodes.get(a						)
															# If char not in current trie node
															if not char_node:
																						return False
															# Flag to check if node can be deleted
															__UpperCamelCase	:   int			   =					_delete(a     ,   a     ,   index + 1						)
															if delete_curr:
																						del curr.nodes[char]
																						return len(curr.nodes						) == 0
															return delete_curr

								_delete(self     ,   a     ,   0						)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			TrieNode						,  _lowerCamelCase :			str)					->    None:






							'''simple docstring'''





							if node.is_leaf:
														print(_lowerCamelCase						,  end=" ")

							for key, value in node.nodes.items():
														print_words(_lowerCamelCase						,  word + key)




def 	_SCREAMING_SNAKE_CASE					(		)					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					"banana bananas bandana band apple all beast".split()
							__UpperCamelCase	:   Union[str, Any]			   =					TrieNode()
							root.insert_many(_lowerCamelCase)
							# print_words(root, "")
							assert all(root.find(_lowerCamelCase) for word in words)
							assert root.find("banana")
							assert not root.find("bandanas")
							assert not root.find("apps")
							assert root.find("apple")
							assert root.find("all")
							root.delete("all")
							assert not root.find("all")
							root.delete("banana")
							assert not root.find("banana")
							assert root.find("bananas")
							return True




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			bool)					->    None:






							'''simple docstring'''





							print(str(_lowerCamelCase)						,  "works!" if passes else "doesn't work :(")




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							assert test_trie()




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							print_results("Testing trie functionality"						,  test_trie())


if __name__ == "__main__":
		main()