


'''simple docstring'''


lowerCamelCase__    	=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages







'''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,
						)



