
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


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

_A  =	{
    "169M": 7_68,
    "430M": 10_24,
    "1B5": 20_48,
    "3B": 25_60,
    "7B": 40_96,
    "14B": 51_20,
}






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


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

						if name != "head.weight":
									lowerCAmelCase_         =      "rwkv." + name

						lowerCAmelCase_         =      weight
			return state_dict






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




			"""simple docstring"""


			if tokenizer_file is None:
						print("No `--tokenizer_file` provided, we will use the default tokenizer."							)
						lowerCAmelCase_         =      50277
						lowerCAmelCase_         =      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"							)
			else:
						lowerCAmelCase_         =      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase							)
						lowerCAmelCase_         =      len(__lowerCAmelCase							)
			tokenizer.save_pretrained(__lowerCAmelCase							)

			# 2. Build the config
			lowerCAmelCase_         =      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:
												lowerCAmelCase_         =      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}."""							)

			lowerCAmelCase_         =      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
			lowerCAmelCase_         =      hf_hub_download(__lowerCAmelCase       ,				__lowerCAmelCase							)
			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			lowerCAmelCase_         =      convert_state_dict(__lowerCAmelCase							)

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

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

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									lowerCAmelCase_         =      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."							)
						lowerCAmelCase_         =      AutoModelForCausalLM.from_pretrained(__lowerCAmelCase							)
						model.push_to_hub(__lowerCAmelCase       ,				max_shard_size="2GB"							)
						tokenizer.push_to_hub(__lowerCAmelCase							)


if __name__ == "__main__":
			_A  =	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.",
			)

			_A  =	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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

			_A  =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



