


'''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


__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       a				(      __a       ):



					'''simple docstring'''






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

										UpperCamelCase__       :str							 =			weight
					return state_dict


def       a				(      __a			, __a			, __a			, __a=None			, __a=None			, __a=False			, __a=None       ):



					'''simple docstring'''






					if tokenizer_file is None:
										print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''       )
										UpperCamelCase__       :List[str]							 =			50277
										UpperCamelCase__       :str							 =			AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''       )
					else:
										UpperCamelCase__       :str							 =			PreTrainedTokenizerFast(tokenizer_file=__a       )
										UpperCamelCase__       :Optional[Any]							 =			len(__a       )
					tokenizer.save_pretrained(__a       )

					# 2. Build the config
					UpperCamelCase__       :Tuple							 =			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__       :Optional[int]							 =			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__       :List[Any]							 =			hf_hub_download(__a			, __a       )
					UpperCamelCase__       :int							 =			torch.load(__a			, map_location='''cpu'''       )
					UpperCamelCase__       :Union[str, Any]							 =			convert_state_dict(__a       )

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

					if index is not None:
										UpperCamelCase__       :Optional[Any]							 =			os.path.join(__a			, __a       )
										# Save the index as well
										with open(__a			, '''w'''			, encoding='''utf-8'''       ) as f:
															UpperCamelCase__       :Union[str, Any]							 =			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__       :int							 =			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(__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__       :List[str]							 =			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,
		)



'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser


def       a				(      __a=None       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			argparse.ArgumentParser(add_help=__a			, allow_abbrev=__a       )
					# The main config parser
					UpperCamelCase__       :str							 =			config_command_parser(__a       )
					# The subparser to add commands to
					UpperCamelCase__       :Union[str, Any]							 =			config_parser.add_subparsers(title='''subcommands'''			, dest='''subcommand'''       )

					# Then add other parsers with the parent parser
					default_command_parser(__a			, parents=[parent_parser]       )
					update_command_parser(__a			, parents=[parent_parser]       )

					return config_parser


def       a				(      )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			get_config_parser()
					UpperCamelCase__       :List[Any]							 =			config_parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										config_parser.print_help()
										exit(1       )

					# Run
					args.func(__a       )


if __name__ == "__main__":
		main()