




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Dict   )						->							List[str]:
    if "model" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''model.''' ,  ''''''   )
    if "norm1" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''norm1''' ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''norm2''' ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''norm''' ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        UpperCAmelCase_											=  orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        UpperCAmelCase_											=  orig_key.replace(f'transformer_{layer_num}' ,  f'encoder.layer.{layer_num}'   )
    if "mha.attn" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''mha.attn''' ,  '''attention.self'''   )
    if "mha" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''mha''' ,  '''attention'''   )
    if "W_q" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''W_q''' ,  '''self.query'''   )
    if "W_k" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''W_k''' ,  '''self.key'''   )
    if "W_v" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''W_v''' ,  '''self.value'''   )
    if "ff.0" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''ff.0''' ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''ff.2''' ,  '''output.dense'''   )
    if "ff" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''ff''' ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''mlm.mlm_class''' ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''mlm''' ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        UpperCAmelCase_											=  orig_key.replace('''backbone.backbone.encoders''' ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        UpperCAmelCase_											=  '''mra.''' + orig_key

    return orig_key




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int ,  __UpperCamelCase    :		Tuple   )						->							List[Any]:
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_											=  orig_state_dict.pop(__UpperCamelCase   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            UpperCAmelCase_											=  val

    UpperCAmelCase_											=  orig_state_dict['''cls.predictions.decoder.bias''']
    UpperCAmelCase_											=  torch.arange(__UpperCamelCase   ).expand((1, -1)   ) + 2

    return orig_state_dict




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Any ,  __UpperCamelCase    :		Optional[Any] ,  __UpperCamelCase    :		Union[str, Any]   )						->							Tuple:
    UpperCAmelCase_											=  torch.load(__UpperCamelCase ,  map_location='''cpu'''   )['''model_state_dict''']
    UpperCAmelCase_											=  MraConfig.from_json_file(__UpperCamelCase   )
    UpperCAmelCase_											=  MraForMaskedLM(__UpperCamelCase   )

    UpperCAmelCase_											=  convert_checkpoint_helper(config.max_position_embeddings ,  __UpperCamelCase   )

    print(model.load_state_dict(__UpperCamelCase   )   )
    model.eval()
    model.save_pretrained(__UpperCamelCase   )

    print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
 _lowerCamelCase    				=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The json file for Mra model config.',
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 _lowerCamelCase    				=		parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Any     	=						'vit-hybrid'
     def __init__(			self     :    str      ,					__snake_case     :    str=None      ,					__snake_case     :    List[Any]=7_68      ,					__snake_case     :    List[Any]=12      ,					__snake_case     :    Dict=12      ,					__snake_case     :    Optional[int]=30_72      ,					__snake_case     :    Optional[int]="gelu"      ,					__snake_case     :    Union[str, Any]=0.0      ,					__snake_case     :    Any=0.0      ,					__snake_case     :    List[Any]=0.02      ,					__snake_case     :    Union[str, Any]=1E-12      ,					__snake_case     :    Optional[int]=2_24      ,					__snake_case     :    Optional[int]=1      ,					__snake_case     :    Tuple=3      ,					__snake_case     :    Union[str, Any]=[1, 10_24, 24, 24]      ,					__snake_case     :    List[Any]=True      ,					**__snake_case     :    Tuple      ,					):
         super().__init__(**__snake_case       )

         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''       )
             UpperCAmelCase_											=  {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }

         if isinstance(__snake_case      ,					__snake_case       ):
             if "model_type" in backbone_config:
                 UpperCAmelCase_											=  CONFIG_MAPPING[backbone_config['''model_type''']]
             else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''       )
                 UpperCAmelCase_											=  BitConfig
             UpperCAmelCase_											=  backbone_config_class(**__snake_case       )

         UpperCAmelCase_											=  backbone_featmap_shape
         UpperCAmelCase_											=  backbone_config
         UpperCAmelCase_											=  hidden_size
         UpperCAmelCase_											=  num_hidden_layers
         UpperCAmelCase_											=  num_attention_heads
         UpperCAmelCase_											=  intermediate_size
         UpperCAmelCase_											=  hidden_act
         UpperCAmelCase_											=  hidden_dropout_prob
         UpperCAmelCase_											=  attention_probs_dropout_prob
         UpperCAmelCase_											=  initializer_range
         UpperCAmelCase_											=  layer_norm_eps
         UpperCAmelCase_											=  image_size
         UpperCAmelCase_											=  patch_size
         UpperCAmelCase_											=  num_channels
         UpperCAmelCase_											=  qkv_bias




     def 						lowerCamelCase_     (			self     :    Any       ):
         UpperCAmelCase_											=  copy.deepcopy(self.__dict__       )
         UpperCAmelCase_											=  self.backbone_config.to_dict()
         UpperCAmelCase_											=  self.__class__.model_type
         return output





