




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

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





def 						snake_case_							(_a	:							Optional[Any]     ):
    UpperCAmelCase			       =		list(state_dict.keys()     )
    for name in state_dict_keys:
        UpperCAmelCase			       =		state_dict.pop(_a     )
        # 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'''    ,   _a     )
        # ffn -> feed_forward
        UpperCAmelCase			       =		re.sub(R'''blocks\.(\d+)\.ffn'''    ,   R'''blocks.\1.feed_forward'''    ,   _a     )
        # 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 						snake_case_							(_a	:							List[str]    ,   _a	:							int    ,   _a	:							Optional[Any]    ,   _a	:							Dict=None    ,   _a	:							List[str]=None    ,   _a	:							Union[str, Any]=False    ,   _a	:							Tuple=None     ):
    # 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=_a     )
        UpperCAmelCase			       =		len(_a     )
    tokenizer.save_pretrained(_a     )

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

    # 3. Download model file then convert state_dict
    UpperCAmelCase			       =		hf_hub_download(_a    ,   _a     )
    UpperCAmelCase			       =		torch.load(_a    ,   map_location='''cpu'''     )
    UpperCAmelCase			       =		convert_state_dict(_a     )

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

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

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

    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(_a     )
        model.push_to_hub(_a    ,   max_shard_size='''2GB'''     )
        tokenizer.push_to_hub(_a     )


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,
   )










'''simple docstring'''







def 						snake_case_							(_a	:							str    ,   _a	:							str     ):
    UpperCAmelCase			       =		len(_a     ) + 1
    UpperCAmelCase			       =		len(_a     ) + 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(_a     )] for j in range(_a     )]

    # 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    ,   _a     ):
        UpperCAmelCase			       =		0

    # since string of zero length will match with pattern where there
    # is at least one * alternatively
    for j in range(1    ,   _a     ):
        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    ,   _a     ):
        for j in range(1    ,   _a     ):
            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}""")




