

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

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




def 	_a       (				__lowerCAmelCase		:      List[str]     ):

		"""simple docstring"""

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

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

				snake_case__						:       int       						=  weight
		return state_dict




def 	_a       (				__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Union[str, Any]=None   ,							__lowerCAmelCase		:      Optional[Any]=None   ,							__lowerCAmelCase		:      Any=False   ,							__lowerCAmelCase		:      List[str]=None     ):

		"""simple docstring"""

		if tokenizer_file is None:
				print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''     )
				snake_case__						:       Tuple       						=  5_02_77
				snake_case__						:       Dict       						=  AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''     )
		else:
				snake_case__						:       str       						=  PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase     )
				snake_case__						:       Optional[int]       						=  len(__lowerCAmelCase     )
		tokenizer.save_pretrained(__lowerCAmelCase     )

		# 2. Build the config
		snake_case__						:       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:
								snake_case__						:       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}."""     )

		snake_case__						:       List[str]       						=  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
		snake_case__						:       Optional[Any]       						=  hf_hub_download(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Tuple       						=  torch.load(__lowerCAmelCase   ,							map_location='''cpu'''     )
		snake_case__						:       Optional[Any]       						=  convert_state_dict(__lowerCAmelCase     )

		# 4. Split in shards and save
		snake_case__						,       snake_case__						:       Optional[Any]       						=  shard_checkpoint(__lowerCAmelCase     )
		for shard_file, shard in shards.items():
				torch.save(__lowerCAmelCase   ,							os.path.join(__lowerCAmelCase   ,							__lowerCAmelCase     )     )

		if index is not None:
				snake_case__						:       str       						=  os.path.join(__lowerCAmelCase   ,							__lowerCAmelCase     )
				# Save the index as well
				with open(__lowerCAmelCase   ,							'''w'''   ,							encoding='''utf-8'''     ) as f:
						snake_case__						:       Dict       						=  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.'''     )
				snake_case__						:       Optional[Any]       						=  list(shards.keys()     )

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						snake_case__						:       Optional[int]       						=  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.'''     )
				snake_case__						:       List[Any]       						=  AutoModelForCausalLM.from_pretrained(__lowerCAmelCase     )
				model.push_to_hub(__lowerCAmelCase   ,							max_shard_size='''2GB'''     )
				tokenizer.push_to_hub(__lowerCAmelCase     )


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__						:       Optional[Any]										=						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 2022 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
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__						:       List[Any]										=						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""




def 	_a       (				__lowerCAmelCase		:      Any=None     ):

		"""simple docstring"""

		if subparsers is not None:
				snake_case__						:       Tuple       						=  subparsers.add_parser('''tpu-config'''   ,							description=_description     )
		else:
				snake_case__						:       str       						=  argparse.ArgumentParser('''Accelerate tpu-config command'''   ,							description=_description     )
		# Core arguments
		snake_case__						:       Any       						=  parser.add_argument_group(
		    '''Config Arguments'''   ,							'''Arguments that can be configured through `accelerate config`.'''     )
		config_args.add_argument(
		    '''--config_file'''   ,							type=__lowerCAmelCase   ,							default=__lowerCAmelCase   ,							help='''Path to the config file to use for accelerate.'''   ,							)
		config_args.add_argument(
		    '''--tpu_name'''   ,							default=__lowerCAmelCase   ,							help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''   ,							)
		config_args.add_argument(
		    '''--tpu_zone'''   ,							default=__lowerCAmelCase   ,							help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''   ,							)
		snake_case__						:       Optional[Any]       						=  parser.add_argument_group('''TPU Arguments'''   ,							'''Arguments for options ran inside the TPU.'''     )
		pod_args.add_argument(
		    '''--use_alpha'''   ,							action='''store_true'''   ,							help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''   ,							)
		pod_args.add_argument(
		    '''--command_file'''   ,							default=__lowerCAmelCase   ,							help='''The path to the file containing the commands to run on the pod on startup.'''   ,							)
		pod_args.add_argument(
		    '''--command'''   ,							action='''append'''   ,							nargs='''+'''   ,							help='''A command to run on the pod. Can be passed multiple times.'''   ,							)
		pod_args.add_argument(
		    '''--install_accelerate'''   ,							action='''store_true'''   ,							help='''Whether to install accelerate on the pod. Defaults to False.'''   ,							)
		pod_args.add_argument(
		    '''--accelerate_version'''   ,							default='''latest'''   ,							help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''   ,							)
		pod_args.add_argument(
		    '''--debug'''   ,							action='''store_true'''   ,							help='''If set, will print the command that would be run instead of running it.'''     )

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




def 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(__lowerCAmelCase     ):
				snake_case__						:       Tuple       						=  load_config_from_file(args.config_file     )
				if not args.command_file and defaults.command_file is not None and not args.command:
						snake_case__						:       Optional[int]       						=  defaults.command_file
				if not args.command and defaults.commands is not None:
						snake_case__						:       Union[str, Any]       						=  defaults.commands
				if not args.tpu_name:
						snake_case__						:       Union[str, Any]       						=  defaults.tpu_name
				if not args.tpu_zone:
						snake_case__						:       Optional[Any]       						=  defaults.tpu_zone
		if args.accelerate_version == "dev":
				snake_case__						:       Tuple       						=  '''git+https://github.com/huggingface/accelerate.git'''
		elif args.accelerate_version == "latest":
				snake_case__						:       Union[str, Any]       						=  '''accelerate -U'''
		elif isinstance(parse(args.accelerate_version     )   ,							__lowerCAmelCase     ):
				snake_case__						:       Any       						=  F"""accelerate=={args.accelerate_version}"""

		if not args.command_file and not args.command:
				raise ValueError('''You must specify either a command file or a command to run on the pod.'''     )

		if args.command_file:
				with open(args.command_file   ,							'''r'''     ) as f:
						snake_case__						:       str       						=  [f.read().splitlines()]

    # To turn list of lists into list of strings
		if isinstance(args.command[0]   ,							__lowerCAmelCase     ):
				snake_case__						:       List[Any]       						=  [line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		snake_case__						:       Optional[int]       						=  ['''cd /usr/share''']
		if args.install_accelerate:
				new_cmd += [F"""pip install {args.accelerate_version}"""]
		new_cmd += args.command
		snake_case__						:       Tuple       						=  '''; '''.join(__lowerCAmelCase     )

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		snake_case__						:       str       						=  ['''gcloud''']
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(F"""Running {' '.join(__lowerCAmelCase     )}"""     )
				return
		subprocess.run(__lowerCAmelCase     )
		print('''Successfully setup pod.'''     )





def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Dict       						=  tpu_command_parser()
		snake_case__						:       Dict       						=  parser.parse_args()

		tpu_command_launcher(__lowerCAmelCase     )



