
"""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': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			list(state_dict.keys()     )
		for name in state_dict_keys:
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				# emb -> embedding
				if name.startswith('''emb.'''     ):
						SCREAMING_SNAKE_CASE_    			=			name.replace('''emb.''',				'''embeddings.'''     )
				# ln_0 -> pre_ln (only present at block 0)
				if name.startswith('''blocks.0.ln0'''     ):
						SCREAMING_SNAKE_CASE_    			=			name.replace('''blocks.0.ln0''',				'''blocks.0.pre_ln'''     )
				# att -> attention
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''blocks\.(\d+)\.att''',				r'''blocks.\1.attention''',				__a     )
				# ffn -> feed_forward
				SCREAMING_SNAKE_CASE_    			=			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'''     ):
						SCREAMING_SNAKE_CASE_    			=			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_    			=			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_    			=			name.replace('''.time_mix_r''',				'''.time_mix_receptance'''     )

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

				SCREAMING_SNAKE_CASE_    			=			weight
		return state_dict



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a=None,				__a=None,				__a=False,				__a=None     ):
		# 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_    			=			50_277
				SCREAMING_SNAKE_CASE_    			=			AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			PreTrainedTokenizerFast(tokenizer_file=__a     )
				SCREAMING_SNAKE_CASE_    			=			len(__a     )
		tokenizer.save_pretrained(__a     )

		# 2. Build the config
		SCREAMING_SNAKE_CASE_    			=			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_    			=			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_    			=			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
		SCREAMING_SNAKE_CASE_    			=			hf_hub_download(__a,				__a     )
		SCREAMING_SNAKE_CASE_    			=			torch.load(__a,				map_location='''cpu'''     )
		SCREAMING_SNAKE_CASE_    			=			convert_state_dict(__a     )

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

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

				del state_dict
				del shards
				gc.collect()

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


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,
						)

"""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
import os

from accelerate.test_utils import execute_subprocess_async



def 							_lowerCamelCase			(			__a=None     ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file''',				default=__a,				help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    ),				)

		if subparsers is not None:
				parser.set_defaults(func=__a     )
		return parser



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE_    			=			script_name
		else:
				SCREAMING_SNAKE_CASE_    			=			F'--config_file={args.config_file} {script_name}'

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			execute_subprocess_async(__a,				env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()