



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Union[str, Any]:
   UpperCamelCase    :  Tuple							=	SwinConfig(image_size=192	)

   if "base" in model_name:
      UpperCamelCase    :  Dict							=	6
      UpperCamelCase    :  str							=	128
      UpperCamelCase    :  List[Any]							=	(2, 2, 18, 2)
      UpperCamelCase    :  Optional[int]							=	(4, 8, 16, 32)
   elif "large" in model_name:
      UpperCamelCase    :  List[str]							=	12
      UpperCamelCase    :  Tuple							=	192
      UpperCamelCase    :  List[Any]							=	(2, 2, 18, 2)
      UpperCamelCase    :  str							=	(6, 12, 24, 48)
   else:
      raise ValueError('Model not supported, only supports base and large variants'	)

   UpperCamelCase    :  Optional[int]							=	window_size
   UpperCamelCase    :  int							=	embed_dim
   UpperCamelCase    :  Any							=	depths
   UpperCamelCase    :  List[str]							=	num_heads

   return config
def 		UpperCamelCase	(   snake_case__       :    str	)	->     List[Any]:
   if "encoder.mask_token" in name:
      UpperCamelCase    :  Dict							=	name.replace('encoder.mask_token'       ,     'embeddings.mask_token'	)
   if "encoder.patch_embed.proj" in name:
      UpperCamelCase    :  Optional[int]							=	name.replace('encoder.patch_embed.proj'       ,     'embeddings.patch_embeddings.projection'	)
   if "encoder.patch_embed.norm" in name:
      UpperCamelCase    :  List[Any]							=	name.replace('encoder.patch_embed.norm'       ,     'embeddings.norm'	)
   if "attn.proj" in name:
      UpperCamelCase    :  Optional[int]							=	name.replace('attn.proj'       ,     'attention.output.dense'	)
   if "attn" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('attn'       ,     'attention.self'	)
   if "norm1" in name:
      UpperCamelCase    :  Optional[int]							=	name.replace('norm1'       ,     'layernorm_before'	)
   if "norm2" in name:
      UpperCamelCase    :  Dict							=	name.replace('norm2'       ,     'layernorm_after'	)
   if "mlp.fc1" in name:
      UpperCamelCase    :  Dict							=	name.replace('mlp.fc1'       ,     'intermediate.dense'	)
   if "mlp.fc2" in name:
      UpperCamelCase    :  Optional[Any]							=	name.replace('mlp.fc2'       ,     'output.dense'	)

   if name == "encoder.norm.weight":
      UpperCamelCase    :  Optional[int]							=	'layernorm.weight'
   if name == "encoder.norm.bias":
      UpperCamelCase    :  List[str]							=	'layernorm.bias'

   if "decoder" in name:
      pass
   else:
      UpperCamelCase    :  Optional[Any]							=	'swin.' + name

   return name
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Dict:
   for key in orig_state_dict.copy().keys():
      UpperCamelCase    :  Tuple							=	orig_state_dict.pop(snake_case__	)

      if "attn_mask" in key:
         pass
      elif "qkv" in key:
         UpperCamelCase    :  Any							=	key.split('.'	)
         UpperCamelCase    :  Union[str, Any]							=	int(key_split[2]	)
         UpperCamelCase    :  Union[str, Any]							=	int(key_split[4]	)
         UpperCamelCase    :  Union[str, Any]							=	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            UpperCamelCase    :  Optional[int]							=	val[:dim, :]
            UpperCamelCase    :  Tuple							=	val[
                dim : dim * 2, :
            ]
            UpperCamelCase    :  Tuple							=	val[-dim:, :]
         else:
            UpperCamelCase    :  List[str]							=	val[
                :dim
            ]
            UpperCamelCase    :  List[str]							=	val[
                dim : dim * 2
            ]
            UpperCamelCase    :  Any							=	val[
                -dim:
            ]
      else:
         UpperCamelCase    :  Optional[int]							=	val

   return orig_state_dict
def 		UpperCamelCase	(   snake_case__       :    List[Any]       ,     snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    List[str]	)	->     Optional[Any]:
   UpperCamelCase    :  Any							=	torch.load(snake_case__       ,     map_location='cpu'	)['model']

   UpperCamelCase    :  int							=	get_swin_config(snake_case__	)
   UpperCamelCase    :  Optional[int]							=	SwinForMaskedImageModeling(snake_case__	)
   model.eval()

   UpperCamelCase    :  Union[str, Any]							=	convert_state_dict(snake_case__       ,     snake_case__	)
   model.load_state_dict(snake_case__	)

   UpperCamelCase    :  str							=	'http://images.cocodataset.org/val2017/000000039769.jpg'

   UpperCamelCase    :  Tuple							=	ViTImageProcessor(size={'height': 192, 'width': 192}	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)
   UpperCamelCase    :  List[str]							=	image_processor(images=snake_case__       ,     return_tensors='pt'	)

   with torch.no_grad():
      UpperCamelCase    :  Any							=	model(**snake_case__	).logits

   print(outputs.keys()	)
   print('Looks ok!'	)

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
      model.save_pretrained(snake_case__	)

      print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
      image_processor.save_pretrained(snake_case__	)

   if push_to_hub:
      print(F"""Pushing model and image processor for {model_name} to hub"""	)
      model.push_to_hub(F"""microsoft/{model_name}"""	)
      image_processor.push_to_hub(F"""microsoft/{model_name}"""	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''swin-base-simmim-window6-192''',
        type=str,
        choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
        help='''Name of the Swin SimMIM model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--checkpoint_path''',
        default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
        type=str,
        help='''Path to the original PyTorch checkpoint (.pth file).''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )

    __UpperCAmelCase          =       parser.parse_args()
    convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Optional[int]:
   monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'       ,     set()	)
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Any:



   class 				lowerCAmelCase_		:
          def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
             UpperCamelCase    :  Tuple							=	metric_id






   class 				lowerCAmelCase_		:
          UpperCAmelCase__				:					Optional[int]        =  [MetricMock(a__							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
          def 					snake_case_       (			self       )     ->		str:
             return self._metrics






   monkeypatch.setattr('datasets.inspect.huggingface_hub'       ,     HfhMock()	)


@pytest.mark.parametrize(
    'func, args'       ,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]	)
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Any       ,     snake_case__       :    List[str]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   if "tmp_path" in args:
      UpperCamelCase    :  Dict							=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args	)
   with pytest.warns(snake_case__       ,     match='https://huggingface.co/docs/evaluate'	):
      func(*snake_case__	)



