'''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


_lowercase		:				Dict	       =		{
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

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




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

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

														lowercase_      :    Any       		= weight
							return state_dict




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Optional[Any]=None   ,   UpperCAmelCase__ :      List[str]=None   ,   UpperCAmelCase__ :      str=False   ,   UpperCAmelCase__ :      Optional[Any]=None				)    ->   Any:
							# 1. If possible, build the tokenizer.
							if tokenizer_file is None:
														print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
														lowercase_      :    Dict       		= 50277
														lowercase_      :    Optional[int]       		= AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
							else:
														lowercase_      :    Union[str, Any]       		= PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase__				)
														lowercase_      :    str       		= len(UpperCAmelCase__				)
							tokenizer.save_pretrained(UpperCAmelCase__				)

							# 2. Build the config
							lowercase_      :    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:
																												lowercase_      :    List[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}.'''				)

							lowercase_      :    Dict       		= RwkvConfig(
							    vocab_size=UpperCAmelCase__   ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]   ,   hidden_size=HIDEN_SIZE_MAPPING[size]   ,   )
							config.save_pretrained(UpperCAmelCase__				)

							# 3. Download model file then convert state_dict
							lowercase_      :    Optional[Any]       		= hf_hub_download(UpperCAmelCase__   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= torch.load(UpperCAmelCase__   ,   map_location="""cpu"""				)
							lowercase_      :    Dict       		= convert_state_dict(UpperCAmelCase__				)

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

							if index is not None:
														lowercase_      :    Dict       		= os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__				)
														# Save the index as well
														with open(UpperCAmelCase__   ,   """w"""   ,   encoding="""utf-8"""				) as f:
																					lowercase_      :    Optional[Any]       		= json.dumps(UpperCAmelCase__   ,   indent=2   ,   sort_keys=UpperCAmelCase__				) + """\n"""
																					f.write(UpperCAmelCase__				)

														# 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."""				)
														lowercase_      :    Optional[Any]       		= list(shards.keys()				)

														del state_dict
														del shards
														gc.collect()

														for shard_file in shard_files:
																					lowercase_      :    Optional[Any]       		= torch.load(os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__				)				)
																					torch.save({k: v.cpu().clone() for k, v in state_dict.items()}   ,   os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__				)				)

							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."""				)
														lowercase_      :    Dict       		= AutoModelForCausalLM.from_pretrained(UpperCAmelCase__				)
														model.push_to_hub(UpperCAmelCase__   ,   max_shard_size="""2GB"""				)
														tokenizer.push_to_hub(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				List[str]	       =		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.",
						)

						_lowercase		:				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,
						)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Optional[int]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





