


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


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

snake_case__			 =						{
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	Union[str, Any]:





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

						if name != "head.weight":
									a__ :int						  =    "rwkv." + name

						a__ :Optional[Any]						  =    weight
			return state_dict


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Optional[int]	,  a	:   Dict	,  a	:   Optional[Any]=None	,  a	:   Tuple=None	,  a	:   Dict=False	,  a	:   int=None				)    ->	List[Any]:





			"""simple docstring"""
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print("No `--tokenizer_file` provided, we will use the default tokenizer."				)
						a__ :List[Any]						  =    50_277
						a__ :Dict						  =    AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"				)
			else:
						a__ :int						  =    PreTrainedTokenizerFast(tokenizer_file=a				)
						a__ :Any						  =    len(a				)
			tokenizer.save_pretrained(a				)

			# 2. Build the config
			a__ :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:
												a__ :str						  =    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}.'''				)

			a__ :Optional[Any]						  =    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
			a__ :List[Any]						  =    hf_hub_download(a	,  a				)
			a__ :Union[str, Any]						  =    torch.load(a	,  map_location="cpu"				)
			a__ :Optional[Any]						  =    convert_state_dict(a				)

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

			if index is not None:
						a__ :str						  =    os.path.join(a	,  a				)
						# Save the index as well
						with open(a	,  "w"	,  encoding="utf-8"				) as f:
									a__ :List[str]						  =    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."				)
						a__ :Union[str, Any]						  =    list(shards.keys()				)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									a__ :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."				)
						a__ :Union[str, Any]						  =    AutoModelForCausalLM.from_pretrained(a				)
						model.push_to_hub(a	,  max_shard_size="2GB"				)
						tokenizer.push_to_hub(a				)


if __name__ == "__main__":
		snake_case__			 =						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.''',
		)

		snake_case__			 =						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__			 =						{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'albert'




						def __init__(	self						:   Any   ,     __A						:   Optional[int]=30000   ,     __A						:   List[str]=128   ,     __A						:   int=4096   ,     __A						:   Any=12   ,     __A						:   Union[str, Any]=1   ,     __A						:   Optional[int]=64   ,     __A						:   Dict=16384   ,     __A						:   List[str]=1   ,     __A						:   Any="gelu_new"   ,     __A						:   List[Any]=0   ,     __A						:   str=0   ,     __A						:   List[str]=512   ,     __A						:   Optional[Any]=2   ,     __A						:   Tuple=0.02   ,     __A						:   int=1E-12   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]="absolute"   ,     __A						:   Tuple=0   ,     __A						:   str=2   ,     __A						:   Union[str, Any]=3   ,     **__A						:   Union[str, Any]   ,     )      ->str:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     **__A						)

									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    embedding_size
									a__ :Union[str, Any]						  =    hidden_size
									a__ :int						  =    num_hidden_layers
									a__ :List[Any]						  =    num_hidden_groups
									a__ :str						  =    num_attention_heads
									a__ :Optional[Any]						  =    inner_group_num
									a__ :Any						  =    hidden_act
									a__ :Optional[int]						  =    intermediate_size
									a__ :Optional[Any]						  =    hidden_dropout_prob
									a__ :Optional[int]						  =    attention_probs_dropout_prob
									a__ :int						  =    max_position_embeddings
									a__ :List[Any]						  =    type_vocab_size
									a__ :Dict						  =    initializer_range
									a__ :Optional[int]						  =    layer_norm_eps
									a__ :Optional[Any]						  =    classifier_dropout_prob
									a__ :Optional[Any]						  =    position_embedding_type






class 					lowerCAmelCase_ (       _a):




						@property
						def  _snake_case					(	self						:   Optional[int]						)      ->Mapping[str, Mapping[int, str]]:






									"""simple docstring"""






									if self.task == "multiple-choice":
												a__ :List[Any]						  =    {0: "batch", 1: "choice", 2: "sequence"}
									else:
												a__ :Dict						  =    {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]						)


