
"""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


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

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




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			)			->   Dict:






    """simple docstring"""




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

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

        UpperCAmelCase          =       weight
    return state_dict




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       Optional[Any]			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       List[str]=None			,					lowerCAmelCase					:       int=None			,					lowerCAmelCase					:       Any=False			,					lowerCAmelCase					:       str=None			)			->   Any:






    """simple docstring"""




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

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

    UpperCAmelCase          =       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
    UpperCAmelCase          =       hf_hub_download(lowerCAmelCase			,					lowerCAmelCase			)
    UpperCAmelCase          =       torch.load(lowerCAmelCase			,					map_location='cpu'			)
    UpperCAmelCase          =       convert_state_dict(lowerCAmelCase			)

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

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

        del state_dict
        del shards
        gc.collect()

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    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.''',
   )

   SCREAMING_SNAKE_CASE_            =    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"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



