



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

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

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

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

										SCREAMING_SNAKE_CASE							:     Optional[int]						=				weight
					return state_dict

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=None      ,	__lowerCAmelCase=None      ,	__lowerCAmelCase=False      ,	__lowerCAmelCase=None   )     ->     Optional[Any]:
					# 1. If possible, build the tokenizer.
					if tokenizer_file is None:
										print('No `--tokenizer_file` provided, we will use the default tokenizer.'   )
										SCREAMING_SNAKE_CASE							:     Dict						=				5_0277
										SCREAMING_SNAKE_CASE							:     Dict						=				AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'   )
					else:
										SCREAMING_SNAKE_CASE							:     Any						=				PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     List[Any]						=				len(__lowerCAmelCase   )
					tokenizer.save_pretrained(__lowerCAmelCase   )

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

					SCREAMING_SNAKE_CASE							:     Optional[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
					SCREAMING_SNAKE_CASE							:     int						=				hf_hub_download(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Dict						=				torch.load(__lowerCAmelCase      ,	map_location='cpu'   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				convert_state_dict(__lowerCAmelCase   )

					# 4. Split in shards and save
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				shard_checkpoint(__lowerCAmelCase   )
					for shard_file, shard in shards.items():
										torch.save(__lowerCAmelCase      ,	os.path.join(__lowerCAmelCase      ,	__lowerCAmelCase   )   )

					if index is not None:
										SCREAMING_SNAKE_CASE							:     int						=				os.path.join(__lowerCAmelCase      ,	__lowerCAmelCase   )
										# Save the index as well
										with open(__lowerCAmelCase      ,	'w'      ,	encoding='utf-8'   ) as f:
															SCREAMING_SNAKE_CASE							:     Tuple						=				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.'   )
										SCREAMING_SNAKE_CASE							:     int						=				list(shards.keys()   )

										del state_dict
										del shards
										gc.collect()

										for shard_file in shard_files:
															SCREAMING_SNAKE_CASE							:     List[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.'   )
										SCREAMING_SNAKE_CASE							:     int						=				AutoModelForCausalLM.from_pretrained(__lowerCAmelCase   )
										model.push_to_hub(__lowerCAmelCase      ,	max_shard_size='2GB'   )
										tokenizer.push_to_hub(__lowerCAmelCase   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[Any]		      =    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    :		Any		      =    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,
			)




def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data