


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__    :Optional[int]       =				{
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

lowerCAmelCase__    :List[str]       =				{
    '''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__					(     a__:						Optional[int]			)    ->						Union[str, Any]:


							'''simple docstring'''





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

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

														_UpperCAmelCase											=     weight
							return state_dict

def 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						Optional[Any]     ,    a__:						Optional[int]     ,    a__:						Optional[int]=None     ,    a__:						List[str]=None     ,    a__:						List[str]=False     ,    a__:						int=None			)    ->						Optional[int]:


							'''simple docstring'''





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

							# 2. Build the config
							_UpperCAmelCase											=     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											=     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											=     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											=     hf_hub_download(a__     ,    a__			)
							_UpperCAmelCase											=     torch.load(a__     ,    map_location='cpu'			)
							_UpperCAmelCase											=     convert_state_dict(a__			)

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

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

														del state_dict
														del shards
														gc.collect()

														for shard_file in shard_files:
																					_UpperCAmelCase											=     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											=     AutoModelForCausalLM.from_pretrained(a__			)
														model.push_to_hub(a__     ,    max_shard_size='2GB'			)
														tokenizer.push_to_hub(a__			)


if __name__ == "__main__":
		lowerCAmelCase__    :Tuple       =				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__    :List[str]       =				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,
		)




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase__    :int       =				logging.get_logger(__name__)

def 			lowerCAmelCase__					(     a__:						Dict			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     R'\w+[.]\d+'
							_UpperCAmelCase											=     re.findall(a__     ,    a__			)
							for pat in pats:
														_UpperCAmelCase											=     key.replace(a__     ,    '_'.join(pat.split('.'			)			)			)
							return key

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Dict     ,    a__:						List[Any]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
							if (
							    any('norm' in str_ for str_ in pt_tuple_key			)
							    and (pt_tuple_key[-1] == "bias")
							    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
							    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
							):
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor
							elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('embedding',)
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
														_UpperCAmelCase											=     pt_tensor.transpose(2     ,    3     ,    1     ,    0			)
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight":
														_UpperCAmelCase											=     pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('weight',)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('bias',)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor







def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]     ,    a__:						int=4_2			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     {k: v.numpy() for k, v in pt_state_dict.items()}

							# Step 2: Since the model is stateless, get random Flax params
							_UpperCAmelCase											=     flax_model.init_weights(PRNGKey(a__			)			)

							_UpperCAmelCase											=     flatten_dict(a__			)
							_UpperCAmelCase											=     {}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_UpperCAmelCase											=     rename_key(a__			)
														_UpperCAmelCase											=     tuple(renamed_pt_key.split('.'			)			)

														# Correctly rename weight parameters
														_UpperCAmelCase						,			_UpperCAmelCase											=     rename_key_and_reshape_tensor(a__     ,    a__     ,    a__			)

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																												    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)

        # also add unexpected weight so that warning is thrown
														_UpperCAmelCase											=     jnp.asarray(a__			)

							return unflatten_dict(a__			)
