

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''': 7_68,
    '''430M''': 10_24,
    '''1B5''': 20_48,
    '''3B''': 25_60,
    '''7B''': 40_96,
    '''14B''': 51_20,
}


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

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

												snake_case_   =	weight
						return state_dict


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=False  ,		SCREAMING_SNAKE_CASE__=None       ):
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
												snake_case_   =	50277
												snake_case_   =	AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
						else:
												snake_case_   =	PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__       )
												snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__       )

						# 2. Build the config
						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:
																								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}.'''       )

						snake_case_   =	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
						snake_case_   =	hf_hub_download(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						snake_case_   =	torch.load(SCREAMING_SNAKE_CASE__  ,		map_location='''cpu'''       )
						snake_case_   =	convert_state_dict(SCREAMING_SNAKE_CASE__       )

						# 4. Split in shards and save
						snake_case_,    snake_case_   =	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:
												snake_case_   =	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:
																		snake_case_   =	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.'''       )
												snake_case_   =	list(shards.keys()       )

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		snake_case_   =	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.'''       )
												snake_case_   =	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"vit_msn"







			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[int]=7_6_8							,   _UpperCamelCase :				Optional[Any]=1_2							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				str=3_0_7_2							,   _UpperCamelCase :				Tuple="gelu"							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-06							,   _UpperCamelCase :				Any=2_2_4							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				Any=3							,   _UpperCamelCase :				str=True							,   **_UpperCamelCase :				Any							,   )      ->int:
									super().__init__(**_UpperCamelCase							)

									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias