






"""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''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}

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

						if name != "head.weight":
									UpperCAmelCase_     : Union[str, Any]			     =					"rwkv." + name

						UpperCAmelCase_     : Optional[int]			     =					weight
			return state_dict

def 				snake_case      (   A__	,A__	,A__	,A__=None	,A__=None	,A__=False	,A__=None						):
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print("No `--tokenizer_file` provided, we will use the default tokenizer."						)
						UpperCAmelCase_     : List[str]			     =					5_02_77
						UpperCAmelCase_     : Tuple			     =					AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"						)
			else:
						UpperCAmelCase_     : Optional[Any]			     =					PreTrainedTokenizerFast(tokenizer_file=A__						)
						UpperCAmelCase_     : List[Any]			     =					len(A__						)
			tokenizer.save_pretrained(A__						)

			# 2. Build the config
			UpperCAmelCase_     : Optional[Any]			     =					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_     : Tuple			     =					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
			UpperCAmelCase_     : Optional[int]			     =					hf_hub_download(A__	,A__						)
			UpperCAmelCase_     : Dict			     =					torch.load(A__	,map_location="cpu"						)
			UpperCAmelCase_     : int			     =					convert_state_dict(A__						)

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

			if index is not None:
						UpperCAmelCase_     : int			     =					os.path.join(A__	,A__						)
						# Save the index as well
						with open(A__	,"w"	,encoding="utf-8"						) as f:
									UpperCAmelCase_     : int			     =					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."						)
						UpperCAmelCase_     : Any			     =					list(shards.keys()						)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									UpperCAmelCase_     : Dict			     =					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."						)
						UpperCAmelCase_     : Union[str, Any]			     =					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,
			)











"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



