

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			list(state_dict.keys()      )
				for name in state_dict_keys:
								A__        =			state_dict.pop(SCREAMING_SNAKE_CASE_      )
								# 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"	,       SCREAMING_SNAKE_CASE_      )
								# ffn -> feed_forward
								A__        =			re.sub(R"blocks\.(\d+)\.ffn"	,       R"blocks.\1.feed_forward"	,       SCREAMING_SNAKE_CASE_      )
								# 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__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							Tuple	,       SCREAMING_SNAKE_CASE_:							List[Any]=None	,       SCREAMING_SNAKE_CASE_:							str=None	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]=False	,       SCREAMING_SNAKE_CASE_:							str=None      )			->      Optional[Any]:




				'''simple docstring'''





				if tokenizer_file is None:
								print("No `--tokenizer_file` provided, we will use the default tokenizer."      )
								A__        =			5_0_2_7_7
								A__        =			AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"      )
				else:
								A__        =			PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE_      )
								A__        =			len(SCREAMING_SNAKE_CASE_      )
				tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )

				# 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=SCREAMING_SNAKE_CASE_	,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]	,       hidden_size=HIDEN_SIZE_MAPPING[size]	,       )
				config.save_pretrained(SCREAMING_SNAKE_CASE_      )

				# 3. Download model file then convert state_dict
				A__        =			hf_hub_download(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				A__        =			torch.load(SCREAMING_SNAKE_CASE_	,       map_location="cpu"      )
				A__        =			convert_state_dict(SCREAMING_SNAKE_CASE_      )

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

				if index is not None:
								A__        =			os.path.join(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								# Save the index as well
								with open(SCREAMING_SNAKE_CASE_	,       "w"	,       encoding="utf-8"      ) as f:
												A__        =			json.dumps(SCREAMING_SNAKE_CASE_	,       indent=2	,       sort_keys=SCREAMING_SNAKE_CASE_      ) + "\n"
												f.write(SCREAMING_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."      )
								A__        =			list(shards.keys()      )

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												A__        =			torch.load(os.path.join(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
												torch.save({k: v.cpu().clone() for k, v in state_dict.items()}	,       os.path.join(SCREAMING_SNAKE_CASE_	,       SCREAMING_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."      )
								A__        =			AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_      )
								model.push_to_hub(SCREAMING_SNAKE_CASE_	,       max_shard_size="2GB"      )
								tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_      )


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.abc import Iterator
from typing import Any

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






							A__        =			data
							A__        =			None

class 					a__						:
			"""simple docstring"""







			def __init__(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			None
							A__        =			None


			def __iter__(    self       )  ->						Iterator[Any]:
							'''simple docstring'''






							A__        =			self.head
							while self.head:
											yield node.data
											A__        =			node.next
											if node == self.head:
															break


			def __len__(    self       )  ->						int:
							'''simple docstring'''






							return sum(1 for _ in self       )


			def __repr__(    self       )  ->						List[Any]:
							'''simple docstring'''






							return "->".join(str(lowercase       ) for item in iter(self       )       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							self.insert_nth(len(self       )      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							self.insert_nth(0      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						None:
							'''simple docstring'''






							if index < 0 or index > len(self       ):
											raise IndexError("list index out of range."       )
							A__        =			Node(lowercase       )
							if self.head is None:
											A__        =			new_node  # first node points itself
											A__        =			A__        =			new_node
							elif index == 0:  # insert at head
											A__        =			self.head
											A__        =			A__        =			new_node
							else:
											A__        =			self.head
											for _ in range(index - 1       ):
															A__        =			temp.next
											A__        =			temp.next
											A__        =			new_node
											if index == len(self       ) - 1:  # insert at tail
															A__        =			new_node


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							return self.delete_nth(0       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							return self.delete_nth(len(self       ) - 1       )


			def      UpperCamelCase		(    self      ,      lowercase = 0       )  ->						Any:
							'''simple docstring'''






							if not 0 <= index < len(self       ):
											raise IndexError("list index out of range."       )
							A__        =			self.head
							if self.head == self.tail:  # just one node
											A__        =			A__        =			None
							elif index == 0:  # delete head node
											A__        =			self.tail.next.next
											A__        =			self.head.next
							else:
											A__        =			self.head
											for _ in range(index - 1       ):
															A__        =			temp.next
											A__        =			temp.next
											A__        =			temp.next.next
											if index == len(self       ) - 1:  # delete at tail
															A__        =			temp
							return delete_node.data


			def      UpperCamelCase		(    self       )  ->						bool:
							'''simple docstring'''






							return len(self       ) == 0



def 					lowerCAmelCase__							(	)			->      None:




				'''simple docstring'''





				A__        =			CircularLinkedList()
				assert len(SCREAMING_SNAKE_CASE_      ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(SCREAMING_SNAKE_CASE_      ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1      )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0      )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5      ):
								assert len(SCREAMING_SNAKE_CASE_      ) == i
								circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_	,       i + 1      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )

				circular_linked_list.insert_tail(6      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       7      )      )
				circular_linked_list.insert_head(0      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(0	,       7      )      )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )
				assert circular_linked_list.delete_nth(2      ) == 3

				circular_linked_list.insert_nth(2	,       3      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()

