

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""": 1_2,
    """430M""": 2_4,
    """1B5""": 2_4,
    """3B""": 3_2,
    """7B""": 3_2,
    """14B""": 4_0,
}

lowerCAmelCase__								=		{
    """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_     (							UpperCAmelCase_							:     Union[str, Any]	)						->				Optional[int]:




	'''simple docstring'''




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

		_UpperCamelCase   :		Any			=						weight
	return state_dict






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Union[str, Any]=None							,       UpperCAmelCase_							:     Tuple=None							,       UpperCAmelCase_							:     List[str]=False							,       UpperCAmelCase_							:     Any=None	)						->				List[Any]:




	'''simple docstring'''




	if tokenizer_file is None:
		print('No `--tokenizer_file` provided, we will use the default tokenizer.'	)
		_UpperCamelCase   :		Tuple			=						5_0_2_7_7
		_UpperCamelCase   :		List[str]			=						AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'	)
	else:
		_UpperCamelCase   :		Union[str, Any]			=						PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						len(UpperCAmelCase_	)
	tokenizer.save_pretrained(UpperCAmelCase_	)

	# 2. Build the config
	_UpperCamelCase   :		Union[str, 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:
				_UpperCamelCase   :		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}.'''	)

	_UpperCamelCase   :		int			=						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
	_UpperCamelCase   :		str			=						hf_hub_download(UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		Dict			=						convert_state_dict(UpperCAmelCase_	)

	# 4. Split in shards and save
	_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						shard_checkpoint(UpperCAmelCase_	)
	for shard_file, shard in shards.items():
		torch.save(UpperCAmelCase_							,       os.path.join(UpperCAmelCase_							,       UpperCAmelCase_	)	)

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

		del state_dict
		del shards
		gc.collect()

		for shard_file in shard_files:
			_UpperCamelCase   :		List[str]			=						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.'	)
		_UpperCamelCase   :		int			=						AutoModelForCausalLM.from_pretrained(UpperCAmelCase_	)
		model.push_to_hub(UpperCAmelCase_							,       max_shard_size='2GB'	)
		tokenizer.push_to_hub(UpperCAmelCase_	)


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,
							)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

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




