



'''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


a     :    int      						=					{
    """169M""": 1_2,
    """430M""": 2_4,
    """1B5""": 2_4,
    """3B""": 3_2,
    """7B""": 3_2,
    """14B""": 4_0,
}

a     :    List[str]      						=					{
    """169M""": 7_6_8,
    """430M""": 1_0_2_4,
    """1B5""": 2_0_4_8,
    """3B""": 2_5_6_0,
    """7B""": 4_0_9_6,
    """14B""": 5_1_2_0,
}



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

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

														UpperCAmelCase  :	Union[str, Any]							  =	weight
							return state_dict



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=False			,       _lowercase=None	)   ->    Dict:
							# 1. If possible, build the tokenizer.
							if tokenizer_file is None:
														print("""No `--tokenizer_file` provided, we will use the default tokenizer."""	)
														UpperCAmelCase  :	Dict							  =	5_0_2_7_7
														UpperCAmelCase  :	str							  =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""	)
							else:
														UpperCAmelCase  :	List[Any]							  =	PreTrainedTokenizerFast(tokenizer_file=_lowercase	)
														UpperCAmelCase  :	int							  =	len(_lowercase	)
							tokenizer.save_pretrained(_lowercase	)

							# 2. Build the config
							UpperCAmelCase  :	Dict							  =	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  :	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  :	Any							  =	RwkvConfig(
							    vocab_size=_lowercase			,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,       hidden_size=HIDEN_SIZE_MAPPING[size]			,       )
							config.save_pretrained(_lowercase	)

							# 3. Download model file then convert state_dict
							UpperCAmelCase  :	Optional[int]							  =	hf_hub_download(_lowercase			,       _lowercase	)
							UpperCAmelCase  :	int							  =	torch.load(_lowercase			,       map_location="""cpu"""	)
							UpperCAmelCase  :	List[str]							  =	convert_state_dict(_lowercase	)

							# 4. Split in shards and save
							UpperCAmelCase  ,  UpperCAmelCase  :	Union[str, Any]							  =	shard_checkpoint(_lowercase	)
							for shard_file, shard in shards.items():
														torch.save(_lowercase			,       os.path.join(_lowercase			,       _lowercase	)	)

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

														# 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  :	Optional[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(_lowercase			,       _lowercase	)	)
																					torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,       os.path.join(_lowercase			,       _lowercase	)	)

							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  :	Any							  =	AutoModelForCausalLM.from_pretrained(_lowercase	)
														model.push_to_hub(_lowercase			,       max_shard_size="""2GB"""	)
														tokenizer.push_to_hub(_lowercase	)


if __name__ == "__main__":
							a     :    Any      						=					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     :    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'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

