import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def     A__	(       __A						):







    '''simple docstring'''
    if "model" in orig_key:
        _lowerCamelCase  :		Optional[int]			    =    orig_key.replace("""model.""" ,			""""""						)
    if "norm1" in orig_key:
        _lowerCamelCase  :		List[Any]			    =    orig_key.replace("""norm1""" ,			"""attention.output.LayerNorm"""						)
    if "norm2" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""norm2""" ,			"""output.LayerNorm"""						)
    if "norm" in orig_key:
        _lowerCamelCase  :		Optional[Any]			    =    orig_key.replace("""norm""" ,			"""LayerNorm"""						)
    if "transformer" in orig_key:
        _lowerCamelCase  :		Optional[int]			    =    orig_key.split("""."""						)[0].split("""_"""						)[-1]
        _lowerCamelCase  :		Any			    =    orig_key.replace(F"""transformer_{layer_num}""" ,			F"""encoder.layer.{layer_num}"""						)
    if "mha.attn" in orig_key:
        _lowerCamelCase  :		int			    =    orig_key.replace("""mha.attn""" ,			"""attention.self"""						)
    if "mha" in orig_key:
        _lowerCamelCase  :		Dict			    =    orig_key.replace("""mha""" ,			"""attention"""						)
    if "W_q" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""W_q""" ,			"""self.query"""						)
    if "W_k" in orig_key:
        _lowerCamelCase  :		Union[str, Any]			    =    orig_key.replace("""W_k""" ,			"""self.key"""						)
    if "W_v" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""W_v""" ,			"""self.value"""						)
    if "ff1" in orig_key:
        _lowerCamelCase  :		Tuple			    =    orig_key.replace("""ff1""" ,			"""intermediate.dense"""						)
    if "ff2" in orig_key:
        _lowerCamelCase  :		List[str]			    =    orig_key.replace("""ff2""" ,			"""output.dense"""						)
    if "ff" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""ff""" ,			"""output.dense"""						)
    if "mlm_class" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""mlm.mlm_class""" ,			"""cls.predictions.decoder"""						)
    if "mlm" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""mlm""" ,			"""cls.predictions.transform"""						)
    if "cls" not in orig_key:
        _lowerCamelCase  :		Dict			    =    """nystromformer.""" + orig_key

    return orig_key







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    for key in orig_state_dict.copy().keys():
        _lowerCamelCase  :		Optional[int]			    =    orig_state_dict.pop(__A						)

        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            _lowerCamelCase  :		Optional[Any]			    =    val

    _lowerCamelCase  :		Any			    =    orig_state_dict["""cls.predictions.decoder.bias"""]
    _lowerCamelCase  :		Any			    =    (
        torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
    )

    return orig_state_dict







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    torch.load(__A ,			map_location="""cpu"""						)["""model_state_dict"""]
    _lowerCamelCase  :		List[Any]			    =    NystromformerConfig.from_json_file(__A						)
    _lowerCamelCase  :		List[str]			    =    NystromformerForMaskedLM(__A						)

    _lowerCamelCase  :		List[str]			    =    convert_checkpoint_helper(__A ,			__A						)

    model.load_state_dict(__A						)
    model.eval()
    model.save_pretrained(__A						)

    print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
    lowerCAmelCase      :				Tuple     			=argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The json file for Nystromformer model config.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    lowerCAmelCase      :				Any     			=parser.parse_args()
    convert_nystromformer_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      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "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 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

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

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




