
'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]






'''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,
      )




