


'''simple docstring'''


from __future__ import annotations

import math






class 	lowerCAmelCase__			:




			def __init__(		self  :     Dict	,  lowerCamelCase__  :     int )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      size
					# approximate the overall size of segment tree with given value
					_UpperCAmelCase		:       int								=      [0 for i in range(0	,  4 * size )]
					# create array to store lazy update
					_UpperCAmelCase		:       Optional[Any]								=      [0 for i in range(0	,  4 * size )]
					_UpperCAmelCase		:       Union[str, Any]								=      [0 for i in range(0	,  4 * size )]  # flag for lazy update




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     int )   ->int:



					'''simple docstring'''







					return idx * 2




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     int )   ->int:



					'''simple docstring'''







					return idx * 2 + 1




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     list[int] )   ->None:



					'''simple docstring'''







					if left_element == right_element:
							_UpperCAmelCase		:       List[Any]								=      a[left_element - 1]
					else:
							_UpperCAmelCase		:       Optional[Any]								=      (left_element + right_element) // 2
							self.build(self.left(lowerCamelCase__ )	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
							self.build(self.right(lowerCamelCase__ )	,  mid + 1	,  lowerCamelCase__	,  lowerCamelCase__ )
							_UpperCAmelCase		:       int								=      max(
							    self.segment_tree[self.left(lowerCamelCase__ )]	,  self.segment_tree[self.right(lowerCamelCase__ )] )




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int )   ->bool:



					'''simple docstring'''







					if self.flag[idx] is True:
							_UpperCAmelCase		:       Tuple								=      self.lazy[idx]
							_UpperCAmelCase		:       Any								=      False
							if left_element != right_element:
									_UpperCAmelCase		:       List[str]								=      self.lazy[idx]
									_UpperCAmelCase		:       List[str]								=      self.lazy[idx]
									_UpperCAmelCase		:       List[Any]								=      True
									_UpperCAmelCase		:       Any								=      True

					if right_element < a or left_element > b:
							return True
					if left_element >= a and right_element <= b:
							_UpperCAmelCase		:       List[str]								=      val
							if left_element != right_element:
									_UpperCAmelCase		:       Tuple								=      val
									_UpperCAmelCase		:       Union[str, Any]								=      val
									_UpperCAmelCase		:       Optional[int]								=      True
									_UpperCAmelCase		:       str								=      True
							return True
					_UpperCAmelCase		:       Union[str, Any]								=      (left_element + right_element) // 2
					self.update(self.left(lowerCamelCase__ )	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					self.update(self.right(lowerCamelCase__ )	,  mid + 1	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      max(
					    self.segment_tree[self.left(lowerCamelCase__ )]	,  self.segment_tree[self.right(lowerCamelCase__ )] )
					return True




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int )   ->int | float:



					'''simple docstring'''







					if self.flag[idx] is True:
							_UpperCAmelCase		:       Any								=      self.lazy[idx]
							_UpperCAmelCase		:       str								=      False
							if left_element != right_element:
									_UpperCAmelCase		:       Optional[int]								=      self.lazy[idx]
									_UpperCAmelCase		:       Optional[Any]								=      self.lazy[idx]
									_UpperCAmelCase		:       List[Any]								=      True
									_UpperCAmelCase		:       Tuple								=      True
					if right_element < a or left_element > b:
							return -math.inf
					if left_element >= a and right_element <= b:
							return self.segment_tree[idx]
					_UpperCAmelCase		:       Optional[int]								=      (left_element + right_element) // 2
					_UpperCAmelCase		:       str								=      self.query(self.left(lowerCamelCase__ )	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      self.query(self.right(lowerCamelCase__ )	,  mid + 1	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					return max(lowerCamelCase__	,  lowerCamelCase__ )






			def __str__(		self  :     Optional[Any] )   ->str:



					'''simple docstring'''







					return str([self.query(1	,  1	,  self.size	,  lowerCamelCase__	,  lowerCamelCase__ ) for i in range(1	,  self.size + 1 )] )


if __name__ == "__main__":
						lowerCamelCase__    	=     [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
						lowerCamelCase__    	=     15
						lowerCamelCase__    	=     SegmentTree(size)
						segt.build(1, 1, size, A)
						print(segt.query(1, 1, size, 4, 6))
						print(segt.query(1, 1, size, 7, 11))
						print(segt.query(1, 1, size, 7, 12))
						segt.update(1, 1, size, 1, 3, 111)
						print(segt.query(1, 1, size, 1, 15))
						segt.update(1, 1, size, 7, 8, 235)
						print(segt)







'''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


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

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






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[str]								=      list(state_dict.keys()   )
		for name in state_dict_keys:
				_UpperCAmelCase		:       Optional[int]								=      state_dict.pop(__lowerCAmelCase   )
				# emb -> embedding
				if name.startswith("emb."   ):
						_UpperCAmelCase		:       Tuple								=      name.replace("emb."      , "embeddings."   )
				# ln_0 -> pre_ln (only present at block 0)
				if name.startswith("blocks.0.ln0"   ):
						_UpperCAmelCase		:       Optional[int]								=      name.replace("blocks.0.ln0"      , "blocks.0.pre_ln"   )
				# att -> attention
				_UpperCAmelCase		:       Union[str, Any]								=      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		:       int								=      name.replace(".time_mix_k"      , ".time_mix_key"   )
				# time_mix_v -> time_mix_value and reshape
				if name.endswith(".time_mix_v"   ):
						_UpperCAmelCase		:       Union[str, Any]								=      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		:       List[str]								=      "rwkv." + name

				_UpperCAmelCase		:       Optional[Any]								=      weight
		return state_dict






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=None      , __lowerCAmelCase=None      , __lowerCAmelCase=False      , __lowerCAmelCase=None   ):
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
				_UpperCAmelCase		:       str								=      50_277
				_UpperCAmelCase		:       Optional[Any]								=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
		else:
				_UpperCAmelCase		:       Tuple								=      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase   )
				_UpperCAmelCase		:       List[Any]								=      len(__lowerCAmelCase   )
		tokenizer.save_pretrained(__lowerCAmelCase   )

		# 2. Build the config
		_UpperCAmelCase		:       Optional[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		:       Optional[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		:       Any								=      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		:       str								=      hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[int]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )
		_UpperCAmelCase		:       Any								=      convert_state_dict(__lowerCAmelCase   )

		# 4. Split in shards and save
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[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		:       int								=      os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )
				# Save the index as well
				with open(__lowerCAmelCase      , "w"      , encoding="utf-8"   ) as f:
						_UpperCAmelCase		:       int								=      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		:       Union[str, Any]								=      list(shards.keys()   )

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						_UpperCAmelCase		:       Union[str, Any]								=      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__":
						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,
						)



