

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def    A_  (							a  ):

      """simple docstring"""



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

      return orig_key

def    A_  (							a					,   a  ):

      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_				:  Tuple           =     orig_state_dict.pop(a  )

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  SCREAMING_SNAKE_CASE_				:  List[str]           =     val

      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     orig_state_dict['cls.predictions.decoder.bias']
      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.arange(a  ).expand((1, -1)  ) + 2

      return orig_state_dict

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.load(a					,   map_location='cpu'  )['model_state_dict']
      SCREAMING_SNAKE_CASE_				:  Dict           =     YosoConfig.from_json_file(a  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     YosoForMaskedLM(a  )

      SCREAMING_SNAKE_CASE_				:  Tuple           =     convert_checkpoint_helper(config.max_position_embeddings					,   a  )

      print(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     :   Union[str, Any]						     =  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for YOSO model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    lowerCAmelCase     :   str						     =  parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
    import torch

    from transformers import AutoModelForImageClassification

if is_vision_available():
    from transformers import AutoImageProcessor
@require_torch
@require_vision
class  _A		(  unittest.TestCase):

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
        model.to(_SCREAMING_SNAKE_CASE					)

        from datasets import load_dataset

        SCREAMING_SNAKE_CASE_				:  str           =     load_dataset('nielsr/rvlcdip-demo'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dataset['train'][0]['image'].convert('RGB'					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image_processor(_SCREAMING_SNAKE_CASE ,					return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_				:  int           =     model(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     outputs.logits

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     torch.Size((1, 16)					)
        self.assertEqual(logits.shape ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     torch.tensor(
            [-0.4158, -0.4092, -0.4347] ,					device=_SCREAMING_SNAKE_CASE ,					dtype=torch.float ,					)
        self.assertTrue(torch.allclose(logits[0, :3] ,					_SCREAMING_SNAKE_CASE ,					atol=1e-4					)					)






