
'''simple docstring'''




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
      import jieba

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


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)

lowercase			:				Union[str, Any]    		=			{'vocab_file': 'vocab.txt'}

lowercase			:				List[Any]    		=			{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

lowercase			:				Optional[int]    		=			{
    'openbmb/cpm-ant-10b': 10_24,
}







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Any					=						collections.OrderedDict()
    with open(snake_case__		,				'''r'''		,				encoding='''utf-8'''						) as reader:
        A       :   Dict					=						reader.readlines()
    for index, token in enumerate(snake_case__						):
        A       :   str					=						token.rstrip('''\n'''						)
        A       :   Tuple					=						index
    return vocab





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE=200	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						vocab
         A       :   Union[str, Any]					=						unk_token
         A       :   Optional[Any]					=						max_input_chars_per_word





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   Optional[Any]					=						list(SCREAMING_SNAKE_CASE	)
         if len(SCREAMING_SNAKE_CASE	) > self.max_input_chars_per_word:
             return [self.unk_token]

         A       :   Union[str, Any]					=						0
         A       :   Optional[Any]					=						[]
         while start < len(SCREAMING_SNAKE_CASE	):
             A       :   Optional[Any]					=						len(SCREAMING_SNAKE_CASE	)
             A       :   int					=						None
             while start < end:
                 A       :   Dict					=						''''''.join(chars[start:end]	)
                 if substr in self.vocab:
                     A       :   List[Any]					=						substr
                     break
                 end -= 1
             if cur_substr is None:
                 sub_tokens.append(self.unk_token	)
                 start += 1
             else:
                 sub_tokens.append(SCREAMING_SNAKE_CASE	)
                 A       :   List[Any]					=						end

         return sub_tokens





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		['''input_ids''', '''attention_mask''']
     __magic_name__						=		False





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE="<d>"						,			SCREAMING_SNAKE_CASE="</d>"						,			SCREAMING_SNAKE_CASE="<s>"						,			SCREAMING_SNAKE_CASE="</s>"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="</n>"						,			SCREAMING_SNAKE_CASE="</_>"						,			SCREAMING_SNAKE_CASE="left"						,			**SCREAMING_SNAKE_CASE						,			) ->				Union[str, Any]:






         """simple docstring"""






         requires_backends(self						,			['''jieba''']	)
         super().__init__(
             bod_token=SCREAMING_SNAKE_CASE						,			eod_token=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			line_token=SCREAMING_SNAKE_CASE						,			space_token=SCREAMING_SNAKE_CASE						,			padding_side=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)
         A       :   Dict					=						bod_token
         A       :   List[Any]					=						eod_token
         A       :   Optional[Any]					=						load_vocab(SCREAMING_SNAKE_CASE	)
         A       :   int					=						self.encoder[space_token]
         A       :   str					=						self.encoder[line_token]

         del self.encoder[space_token]
         del self.encoder[line_token]

         A       :   Optional[Any]					=						collections.OrderedDict(sorted(self.encoder.items()						,			key=lambda SCREAMING_SNAKE_CASE	: x[1]	)	)
         A       :   Any					=						{v: k for k, v in self.encoder.items()}

         A       :   Optional[Any]					=						WordpieceTokenizer(vocab=self.encoder						,			unk_token=self.unk_token	)





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         return self.encoder[self.bod_token]





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return self.encoder[self.eod_token]





     @property
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         return self.encoder["\n"]





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return len(self.encoder	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return dict(self.encoder						,			**self.added_tokens_encoder	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						[]
         for x in jieba.cut(SCREAMING_SNAKE_CASE						,			cut_all=SCREAMING_SNAKE_CASE	):
             output_tokens.extend(self.wordpiece_tokenizer.tokenize(SCREAMING_SNAKE_CASE	)	)
         return output_tokens





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   Optional[Any]					=						[i for i in token_ids if i >= 0]
         A       :   Dict					=						[
             x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
         ]
         return super()._decode(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         return token in self.encoder





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         return "".join(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         return self.encoder.get(SCREAMING_SNAKE_CASE						,			self.encoder.get(self.unk_token	)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         return self.decoder.get(SCREAMING_SNAKE_CASE						,			self.unk_token	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if os.path.isdir(SCREAMING_SNAKE_CASE	):
             A       :   int					=						os.path.join(
                 SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
         else:
             A       :   List[Any]					=						(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         A       :   Optional[Any]					=						0
         if " " in self.encoder:
             A       :   List[str]					=						self.encoder[''' ''']
             del self.encoder[" "]
         if "\n" in self.encoder:
             A       :   Any					=						self.encoder['''\n''']
             del self.encoder["\n"]
         A       :   Dict					=						collections.OrderedDict(sorted(self.encoder.items()						,			key=lambda SCREAMING_SNAKE_CASE	: x[1]	)	)
         with open(SCREAMING_SNAKE_CASE						,			'''w'''						,			encoding='''utf-8'''	) as writer:
             for token, token_index in self.encoder.items():
                 if index != token_index:
                     logger.warning(
                         F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
                         ''' Please check that the vocabulary is not corrupted!'''	)
                     A       :   List[str]					=						token_index
                 writer.write(token + '''\n'''	)
                 index += 1
         return (vocab_file,)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         if token_ids_a is None:
             return [self.bos_token_id] + token_ids_a
         return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






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

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	))
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	))






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

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







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						list(state_dict.keys()						)
    for name in state_dict_keys:
        A       :   str					=						state_dict.pop(snake_case__						)
        # emb -> embedding
        if name.startswith('''emb.'''						):
            A       :   Optional[Any]					=						name.replace('''emb.'''		,				'''embeddings.'''						)
        # ln_0 -> pre_ln (only present at block 0)
        if name.startswith('''blocks.0.ln0'''						):
            A       :   Union[str, Any]					=						name.replace('''blocks.0.ln0'''		,				'''blocks.0.pre_ln'''						)
        # att -> attention
        A       :   int					=						re.sub(R'''blocks\.(\d+)\.att'''		,				R'''blocks.\1.attention'''		,				snake_case__						)
        # ffn -> feed_forward
        A       :   List[Any]					=						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'''						):
            A       :   List[str]					=						name.replace('''.time_mix_k'''		,				'''.time_mix_key'''						)
        # time_mix_v -> time_mix_value and reshape
        if name.endswith('''.time_mix_v'''						):
            A       :   Union[str, Any]					=						name.replace('''.time_mix_v'''		,				'''.time_mix_value'''						)
        # time_mix_r -> time_mix_key and reshape
        if name.endswith('''.time_mix_r'''						):
            A       :   Union[str, Any]					=						name.replace('''.time_mix_r'''		,				'''.time_mix_receptance'''						)

        if name != "head.weight":
            A       :   List[Any]					=						'''rwkv.''' + name

        A       :   Dict					=						weight
    return state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=False		,				snake_case__=None						):




    '''simple docstring'''



    if tokenizer_file is None:
        print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''						)
        A       :   int					=						5_0277
        A       :   Optional[int]					=						AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''						)
    else:
        A       :   str					=						PreTrainedTokenizerFast(tokenizer_file=snake_case__						)
        A       :   Any					=						len(snake_case__						)
    tokenizer.save_pretrained(snake_case__						)

    # 2. Build the config
    A       :   List[str]					=						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:
                A       :   List[str]					=						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}.'						)

    A       :   Any					=						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
    A       :   Union[str, Any]					=						hf_hub_download(snake_case__		,				snake_case__						)
    A       :   Tuple					=						torch.load(snake_case__		,				map_location='''cpu'''						)
    A       :   List[Any]					=						convert_state_dict(snake_case__						)

    # 4. Split in shards and save
    A,  A       :   List[str]					=						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:
        A       :   Dict					=						os.path.join(snake_case__		,				snake_case__						)
        # Save the index as well
        with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as f:
            A       :   List[Any]					=						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.'''						)
        A       :   List[Any]					=						list(shards.keys()						)

        del state_dict
        del shards
        gc.collect()

        for shard_file in shard_files:
            A       :   Union[str, Any]					=						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.'''						)
        A       :   int					=						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			:				List[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.',
      )

      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,
      )




