'''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


lowerCAmelCase_ : Optional[Any]						  =      {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

lowerCAmelCase_ : Optional[Any]						  =      {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}


def __a (	__lowerCamelCase			:						Any				)	->							List[str]:
      '''simple docstring'''


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

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

            lowercase_     =      weight
      return state_dict


def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						List[str]=None ,		__lowerCamelCase			:						List[Any]=None ,		__lowerCamelCase			:						Optional[int]=False ,		__lowerCamelCase			:						Union[str, Any]=None				)	->							List[Any]:
      '''simple docstring'''


      if tokenizer_file is None:
            print("No `--tokenizer_file` provided, we will use the default tokenizer."				)
            lowercase_     =      50_277
            lowercase_     =      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"				)
      else:
            lowercase_     =      PreTrainedTokenizerFast(tokenizer_file=__lowerCamelCase				)
            lowercase_     =      len(__lowerCamelCase				)
      tokenizer.save_pretrained(__lowerCamelCase				)

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

      lowercase_     =      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
      lowercase_     =      hf_hub_download(__lowerCamelCase ,		__lowerCamelCase				)
      lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      lowercase_     =      convert_state_dict(__lowerCamelCase				)

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

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

            del state_dict
            del shards
            gc.collect()

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


if __name__ == "__main__":
       lowerCAmelCase_ : int						  =      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.",
       )

       lowerCAmelCase_ : List[str]						  =      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 json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






