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

__a: List[Any]          =   {
    """169M""": 7_68,
    """430M""": 10_24,
    """1B5""": 20_48,
    """3B""": 25_60,
    """7B""": 40_96,
    """14B""": 51_20,
}
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Tuple  =					list(state_dict.keys()     )
 for name in state_dict_keys:
  lowercase__				:    Optional[int]  =					state_dict.pop(UpperCAmelCase     )
  # emb -> embedding
  if name.startswith('''emb.'''     ):
   lowercase__				:    Dict  =					name.replace('''emb.'''		,     '''embeddings.'''     )
  # ln_0 -> pre_ln (only present at block 0)
  if name.startswith('''blocks.0.ln0'''     ):
   lowercase__				:    Optional[int]  =					name.replace('''blocks.0.ln0'''		,     '''blocks.0.pre_ln'''     )
  # att -> attention
  lowercase__				:    Tuple  =					re.sub(r'''blocks\.(\d+)\.att'''		,     r'''blocks.\1.attention'''		,     UpperCAmelCase     )
  # ffn -> feed_forward
  lowercase__				:    Optional[int]  =					re.sub(r'''blocks\.(\d+)\.ffn'''		,     r'''blocks.\1.feed_forward'''		,     UpperCAmelCase     )
  # time_mix_k -> time_mix_key and reshape
  if name.endswith('''.time_mix_k'''     ):
   lowercase__				:    int  =					name.replace('''.time_mix_k'''		,     '''.time_mix_key'''     )
  # time_mix_v -> time_mix_value and reshape
  if name.endswith('''.time_mix_v'''     ):
   lowercase__				:    int  =					name.replace('''.time_mix_v'''		,     '''.time_mix_value'''     )
  # time_mix_r -> time_mix_key and reshape
  if name.endswith('''.time_mix_r'''     ):
   lowercase__				:    Any  =					name.replace('''.time_mix_r'''		,     '''.time_mix_receptance'''     )

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

  lowercase__				:    List[Any]  =					weight
 return state_dict
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=False		,     UpperCAmelCase=None     ):
 # 1. If possible, build the tokenizer.
 if tokenizer_file is None:
  print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''     )
  lowercase__				:    str  =					5_0277
  lowercase__				:    List[str]  =					AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''     )
 else:
  lowercase__				:    Optional[int]  =					PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					len(UpperCAmelCase     )
 tokenizer.save_pretrained(UpperCAmelCase     )

 # 2. Build the config
 lowercase__				:    Optional[Any]  =					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__				:    Optional[int]  =					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__				:    List[Any]  =					RwkvConfig(
     vocab_size=UpperCAmelCase		,     num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]		,     hidden_size=HIDEN_SIZE_MAPPING[size]		,     )
 config.save_pretrained(UpperCAmelCase     )

 # 3. Download model file then convert state_dict
 lowercase__				:    int  =					hf_hub_download(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    int  =					torch.load(UpperCAmelCase		,     map_location='''cpu'''     )
 lowercase__				:    Optional[int]  =					convert_state_dict(UpperCAmelCase     )

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

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

  # 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__				:    Optional[Any]  =					list(shards.keys()     )

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   lowercase__				:    Optional[int]  =					torch.load(os.path.join(UpperCAmelCase		,     UpperCAmelCase     )     )
   torch.save({k: v.cpu().clone() for k, v in state_dict.items()}		,     os.path.join(UpperCAmelCase		,     UpperCAmelCase     )     )

 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__				:    Tuple  =					AutoModelForCausalLM.from_pretrained(UpperCAmelCase     )
  model.push_to_hub(UpperCAmelCase		,     max_shard_size='''2GB'''     )
  tokenizer.push_to_hub(UpperCAmelCase     )


if __name__ == "__main__":
  __a: str          =   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: 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 os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  __a: Tuple          =   None


__a: Tuple          =   logging.get_logger(__name__)

__a: Optional[Any]          =   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a: Optional[Any]          =   {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__a: Tuple          =   {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE							=    TaTokenizer

       SCREAMING_SNAKE_CASE							=    []

       def __init__(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase=100	,   __lowerCAmelCase=None	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
         lowercase__				:    Union[str, Any]  =					[F"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
        elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         lowercase__				:    Dict  =					len(set(filter(lambda __lowerCAmelCase : bool('''extra_id_''' in str(__lowerCAmelCase ) )	,   __lowerCAmelCase ) ) )
         if extra_tokens != extra_ids:
          raise ValueError(
              F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
              ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
              ''' tokens''' )

        super().__init__(
            __lowerCAmelCase	,   tokenizer_file=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   extra_ids=__lowerCAmelCase	,   additional_special_tokens=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

        lowercase__				:    Union[str, Any]  =					vocab_file
        lowercase__				:    Optional[int]  =					False if not self.vocab_file else True
        lowercase__				:    Any  =					extra_ids

       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Union[str, Any]:
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowercase__				:    Any  =					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
          return init_max_model_length
         elif init_max_model_length is None:
          warnings.warn(
              '''This tokenizer was incorrectly instantiated with a model max length of'''
              F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
              ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
              ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
              F""" {pretrained_model_name_or_path} automatically truncating your input to"""
              F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
              F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
              ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
              ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''	,   __lowerCAmelCase	,   )

        return max_model_length

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Tuple[str]:
        if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )

        if not os.path.isdir(__lowerCAmelCase ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
         return
        lowercase__				:    List[Any]  =					os.path.join(
            __lowerCAmelCase	,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ):
         copyfile(self.vocab_file	,   __lowerCAmelCase )
         logger.info(F"""Copy vocab file to {out_vocab_file}""" )

        return (out_vocab_file,)

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Any  =					token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
        else:
         lowercase__				:    Dict  =					token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Optional[int]  =					[self.eos_token_id]

        if token_ids_a is None:
         return len(token_ids_a + eos ) * [0]
        return len(token_ids_a + eos + token_ids_a + eos ) * [0]

       def _lowerCAmelCase(						self )  ->  List[Any]:
        return list(
            set(filter(lambda __lowerCAmelCase : bool(re.search(r'''<extra_id_\d+>'''	,   __lowerCAmelCase ) ) is not None	,   self.additional_special_tokens ) ) )


       def _lowerCAmelCase(						self )  ->  Tuple:
        return [self.convert_tokens_to_ids(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]

