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

__lowercase		:		Dict														=							{
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}





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

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

          __snake_case				=							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):
     # 1. If possible, build the tokenizer.
     if tokenizer_file is None:
          print('''No `--tokenizer_file` provided, we will use the default tokenizer.''')
          __snake_case				=							5_02_77
          __snake_case				=							AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''')
     else:
          __snake_case				=							PreTrainedTokenizerFast(tokenizer_file=snake_case)
          __snake_case				=							len(snake_case)
     tokenizer.save_pretrained(snake_case)

     # 2. Build the config
     __snake_case				=							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:
                    __snake_case				=							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}.")

     __snake_case				=							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
     __snake_case				=							hf_hub_download(snake_case,      snake_case)
     __snake_case				=							torch.load(snake_case,      map_location='''cpu''')
     __snake_case				=							convert_state_dict(snake_case)

     # 4. Split in shards and save
     __snake_case		,	__snake_case				=							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:
          __snake_case				=							os.path.join(snake_case,      snake_case)
          # Save the index as well
          with open(snake_case,      '''w''',      encoding='''utf-8''') as f:
               __snake_case				=							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.''')
          __snake_case				=							list(shards.keys())

          del state_dict
          del shards
          gc.collect()

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


if __name__ == "__main__":
       __lowercase		:		Tuple														=							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		:		Union[str, Any]														=							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 os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase		:		List[str]														=							logging.get_logger(__name__)

__lowercase		:		Union[str, Any]														=							{
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

__lowercase		:		Optional[int]														=							{
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

__lowercase		:		Dict														=							{
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							set()
     __snake_case				=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char))
          __snake_case				=							char

     __snake_case				=							set(snake_case)
     return pairs




class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Union[str, Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					Any							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Optional[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    def __init__(		self							:     Union[str, Any]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     str="<s>"	,      A_							:     List[str]="</s>"	,      A_							:     List[Any]="</s>"	,      A_							:     List[str]="<s>"	,      A_							:     Optional[int]="<unk>"	,      A_							:     List[str]="<pad>"	,      A_							:     Any="<mask>"	,      **A_							:     int	,      )    ->       Tuple:
         super().__init__(
             bos_token=A_	,      eos_token=A_	,      unk_token=A_	,      sep_token=A_	,      cls_token=A_	,      pad_token=A_	,      mask_token=A_	,      **A_	,      )

         __snake_case				=							vocab_file
         __snake_case				=							merges_file

         __snake_case				=							{}
         __snake_case				=							0
         __snake_case				=							1
         __snake_case				=							2
         __snake_case				=							3

         self.add_from_file(A_    )

         __snake_case				=							{v: k for k, v in self.encoder.items()}

         with open(A_	,      encoding='''utf-8'''    ) as merges_handle:
              __snake_case				=							merges_handle.read().split('''\n'''    )[:-1]
         __snake_case				=							[tuple(merge.split()[:-1]    ) for merge in merges]
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							{}
    def  lowercase					(		self							:     str	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __snake_case				=							[self.cls_token_id]
         __snake_case				=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def  lowercase					(		self							:     Dict	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_	,      token_ids_a=A_	,      already_has_special_tokens=A_    )

         if token_ids_a is None:
              return [1] + ([0] * len(A_    )) + [1]
         return [1] + ([0] * len(A_    )) + [1, 1] + ([0] * len(A_    )) + [1]
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep    ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         return len(self.encoder    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         return dict(self.encoder	,      **self.added_tokens_encoder    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]    )    ->       Optional[int]:
         if token in self.cache:
              return self.cache[token]
         __snake_case				=							tuple(A_    )
         __snake_case				=							tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
         __snake_case				=							get_pairs(A_    )

         if not pairs:
              return token

         while True:
              __snake_case				=							min(A_	,      key=lambda A_    : self.bpe_ranks.get(A_	,      float('''inf'''    )    )    )
              if bigram not in self.bpe_ranks:
                   break
              __snake_case		,	__snake_case				=							bigram
              __snake_case				=							[]
              __snake_case				=							0
              while i < len(A_    ):
                   try:
                        __snake_case				=							word.index(A_	,      A_    )
                   except ValueError:
                        new_word.extend(word[i:]    )
                        break
                   else:
                        new_word.extend(word[i:j]    )
                        __snake_case				=							j

                   if word[i] == first and i < len(A_    ) - 1 and word[i + 1] == second:
                        new_word.append(first + second    )
                        i += 2
                   else:
                        new_word.append(word[i]    )
                        i += 1
              __snake_case				=							tuple(A_    )
              __snake_case				=							new_word
              if len(A_    ) == 1:
                   break
              else:
                   __snake_case				=							get_pairs(A_    )
         __snake_case				=							'''@@ '''.join(A_    )
         __snake_case				=							word[:-4]
         __snake_case				=							word
         return word
    def  lowercase					(		self							:     int	,      A_							:     Tuple    )    ->       int:
         __snake_case				=							[]

         __snake_case				=							re.findall(R'''\S+\n?'''	,      A_    )

         for token in words:
              split_tokens.extend(list(self.bpe(A_    ).split(''' '''    )    )    )
         return split_tokens
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Tuple    )    ->       int:
         return self.encoder.get(A_	,      self.encoder.get(self.unk_token    )    )
    def  lowercase					(		self							:     int	,      A_							:     Dict    )    ->       Optional[Any]:
         return self.decoder.get(A_	,      self.unk_token    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     int    )    ->       Tuple:
         __snake_case				=							''' '''.join(A_    ).replace('''@@ '''	,      ''''''    ).strip()
         return out_string
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
              return
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(
             A_	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

         if os.path.abspath(self.vocab_file    ) != os.path.abspath(A_    ):
              copyfile(self.vocab_file	,      A_    )

         if os.path.abspath(self.merges_file    ) != os.path.abspath(A_    ):
              copyfile(self.merges_file	,      A_    )

         return out_vocab_file, out_merge_file







    def  lowercase					(		self							:     Optional[int]	,      A_							:     Union[str, Any]    )    ->       Dict:
         if isinstance(A_	,      A_    ):
              try:
                   with open(A_	,      '''r'''	,      encoding='''utf-8'''    ) as fd:
                        self.add_from_file(A_    )
              except FileNotFoundError as fnfe:
                   raise fnfe
              except UnicodeError:
                   raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset"    )
              return

         __snake_case				=							f.readlines()
         for lineTmp in lines:
              __snake_case				=							lineTmp.strip()
              __snake_case				=							line.rfind(''' '''    )
              if idx == -1:
                   raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\''''    )
              __snake_case				=							line[:idx]
              __snake_case				=							len(self.encoder    )