




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": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Union[str, Any]:
						_UpperCAmelCase      =			list(state_dict.keys()       )
						for name in state_dict_keys:
												_UpperCAmelCase      =			state_dict.pop(snake_case       )
												# 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"""			,     snake_case       )
												# ffn -> feed_forward
												_UpperCAmelCase      =			re.sub(R"""blocks\.(\d+)\.ffn"""			,     R"""blocks.\1.feed_forward"""			,     snake_case       )
												# 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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=False			,     snake_case=None       )						->				Optional[Any]:
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print("""No `--tokenizer_file` provided, we will use the default tokenizer."""       )
												_UpperCAmelCase      =			5_0_2_7_7
												_UpperCAmelCase      =			AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""       )
						else:
												_UpperCAmelCase      =			PreTrainedTokenizerFast(tokenizer_file=snake_case       )
												_UpperCAmelCase      =			len(snake_case       )
						tokenizer.save_pretrained(snake_case       )

						# 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=snake_case			,     num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,     hidden_size=HIDEN_SIZE_MAPPING[size]			,     )
						config.save_pretrained(snake_case       )

						# 3. Download model file then convert state_dict
						_UpperCAmelCase      =			hf_hub_download(snake_case			,     snake_case       )
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )
						_UpperCAmelCase      =			convert_state_dict(snake_case       )

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

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

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

						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(snake_case       )
												model.push_to_hub(snake_case			,     max_shard_size="""2GB"""       )
												tokenizer.push_to_hub(snake_case       )


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,
				)





def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				bool:
						_UpperCAmelCase      =			len(snake_case       ) + 1
						_UpperCAmelCase      =			len(snake_case       ) + 1

						# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
						# length i of input_string matches with prefix string of length j of
						# given pattern.
						# "dp" stands for dynamic programming.
						_UpperCAmelCase      =			[[0 for i in range(snake_case       )] for j in range(snake_case       )]

						# since string of zero length match pattern of zero length
						_UpperCAmelCase      =			1

						# since pattern of zero length will never match with string of non-zero length
						for i in range(1			,     snake_case       ):
												_UpperCAmelCase      =			0

						# since string of zero length will match with pattern where there
						# is at least one * alternatively
						for j in range(1			,     snake_case       ):
												_UpperCAmelCase      =			dp[0][j - 2] if pattern[j - 1] == """*""" else 0

						# now using bottom-up approach to find for all remaining lengths
						for i in range(1			,     snake_case       ):
												for j in range(1			,     snake_case       ):
																		if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																								_UpperCAmelCase      =			dp[i - 1][j - 1]

																		elif pattern[j - 1] == "*":
																								if dp[i][j - 2] == 1:
																														_UpperCAmelCase      =			1
																								elif pattern[j - 2] in (input_string[i - 1], "."):
																														_UpperCAmelCase      =			dp[i - 1][j]
																								else:
																														_UpperCAmelCase      =			0
																		else:
																								_UpperCAmelCase      =			0

						return bool(dp[-1][-1]       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				a													=			"aab"
				a													=			"c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F'{input_string} matches the given pattern {pattern}')
				else:
								print(F'{input_string} does not match with the given pattern {pattern}')