


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

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

				if name != "head.weight":
						__a           =			'''rwkv.''' + name

				__a           =			weight
		return state_dict

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

		# 2. Build the config
		__a           =			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:
								__a           =			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}.'''			)

		__a           =			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
		__a           =			hf_hub_download(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			torch.load(lowerCAmelCase__    ,					map_location='''cpu'''			)
		__a           =			convert_state_dict(lowerCAmelCase__			)

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

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

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						__a           =			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.'''			)
				__a           =			AutoModelForCausalLM.from_pretrained(lowerCAmelCase__			)
				model.push_to_hub(lowerCAmelCase__    ,					max_shard_size='''2GB'''			)
				tokenizer.push_to_hub(lowerCAmelCase__			)


if __name__ == "__main__":
				lowercase_			     =  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_			     =  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"""




import math

def        lowercase							(		lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     int			) ->      int:
		__a           =			len(lowerCAmelCase__			)
		__a           =			int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
		__a           =			0
		while arr[min(lowerCAmelCase__    ,					lowerCAmelCase__			) - 1] < x:
				__a           =			step
				step += int(math.floor(math.sqrt(lowerCAmelCase__			)			)			)
				if prev >= n:
						return -1

		while arr[prev] < x:
				__a           =			prev + 1
				if prev == min(lowerCAmelCase__    ,					lowerCAmelCase__			):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
				lowercase_			     =  input("Enter numbers separated by a comma:\n").strip()
				lowercase_			     =  [int(item) for item in user_input.split(",")]
				lowercase_			     =  int(input("Enter the number to be searched:\n"))
				lowercase_			     =  jump_search(arr, x)
				if res == -1:
								print("Number not found!")
				else:
								print(F'''Number {x} is at index {res}''')






