
"""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


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

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




def      __lowerCamelCase	(		UpperCamelCase__   ):


	"""simple docstring"""






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

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

		_UpperCAmelCase			=    weight
	return state_dict




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__=None					,  UpperCamelCase__=None					,  UpperCamelCase__=False					,  UpperCamelCase__=None   ):


	"""simple docstring"""






	if tokenizer_file is None:
		print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
		_UpperCAmelCase			=    5_0277
		_UpperCAmelCase			=    AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
	else:
		_UpperCAmelCase			=    PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__   )
		_UpperCAmelCase			=    len(UpperCamelCase__   )
	tokenizer.save_pretrained(UpperCamelCase__   )

	# 2. Build the config
	_UpperCAmelCase			=    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			=    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			=    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			=    hf_hub_download(UpperCamelCase__					,  UpperCamelCase__   )
	_UpperCAmelCase			=    torch.load(UpperCamelCase__					,  map_location="cpu"   )
	_UpperCAmelCase			=    convert_state_dict(UpperCamelCase__   )

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

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

		del state_dict
		del shards
		gc.collect()

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


if __name__ == "__main__":
			__magic_name__									=					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.''',
			)

			__magic_name__									=					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"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : list				)->		list:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCAmelCase__						:    List[Any]							=							True
          for i in range(0			,					len(snake_case				) - 1			,					2				):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    int							=							False

          for i in range(1			,					len(snake_case				) - 1			,					2				):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    str							=							False
     return input_list


if __name__ == "__main__":
 print("""Enter list to be sorted""")
 _lowerCAmelCase	:	List[str]				=      [int(x) for x in input().split()]
 # inputing elements of the list in one line
 _lowerCAmelCase	:	Optional[int]				=      odd_even_sort(input_list)
 print("""The sorted list is""")
 print(sorted_list)


