


'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def 	a_					( lowerCamelCase	:     Optional[int]				):
   lowerCAmelCase     =			SwinConfig(image_size=192				)

   if "base" in model_name:
      lowerCAmelCase     =			6
      lowerCAmelCase     =			128
      lowerCAmelCase     =			(2, 2, 18, 2)
      lowerCAmelCase     =			(4, 8, 16, 32)
   elif "large" in model_name:
      lowerCAmelCase     =			12
      lowerCAmelCase     =			192
      lowerCAmelCase     =			(2, 2, 18, 2)
      lowerCAmelCase     =			(6, 12, 24, 48)
   else:
      raise ValueError('Model not supported, only supports base and large variants'				)

   lowerCAmelCase     =			window_size
   lowerCAmelCase     =			embed_dim
   lowerCAmelCase     =			depths
   lowerCAmelCase     =			num_heads

   return config





def 	a_					( lowerCamelCase	:     Any				):
   if "encoder.mask_token" in name:
      lowerCAmelCase     =			name.replace('encoder.mask_token'	, 'embeddings.mask_token'				)
   if "encoder.patch_embed.proj" in name:
      lowerCAmelCase     =			name.replace('encoder.patch_embed.proj'	, 'embeddings.patch_embeddings.projection'				)
   if "encoder.patch_embed.norm" in name:
      lowerCAmelCase     =			name.replace('encoder.patch_embed.norm'	, 'embeddings.norm'				)
   if "attn.proj" in name:
      lowerCAmelCase     =			name.replace('attn.proj'	, 'attention.output.dense'				)
   if "attn" in name:
      lowerCAmelCase     =			name.replace('attn'	, 'attention.self'				)
   if "norm1" in name:
      lowerCAmelCase     =			name.replace('norm1'	, 'layernorm_before'				)
   if "norm2" in name:
      lowerCAmelCase     =			name.replace('norm2'	, 'layernorm_after'				)
   if "mlp.fc1" in name:
      lowerCAmelCase     =			name.replace('mlp.fc1'	, 'intermediate.dense'				)
   if "mlp.fc2" in name:
      lowerCAmelCase     =			name.replace('mlp.fc2'	, 'output.dense'				)

   if name == "encoder.norm.weight":
      lowerCAmelCase     =			'layernorm.weight'
   if name == "encoder.norm.bias":
      lowerCAmelCase     =			'layernorm.bias'

   if "decoder" in name:
      pass
   else:
      lowerCAmelCase     =			'swin.' + name

   return name





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Any				):
   for key in orig_state_dict.copy().keys():
      lowerCAmelCase     =			orig_state_dict.pop(lowerCamelCase				)

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

         if "weight" in key:
            lowerCAmelCase     =			val[:dim, :]
            lowerCAmelCase     =			val[
                dim : dim * 2, :
            ]
            lowerCAmelCase     =			val[-dim:, :]
         else:
            lowerCAmelCase     =			val[
                :dim
            ]
            lowerCAmelCase     =			val[
                dim : dim * 2
            ]
            lowerCAmelCase     =			val[
                -dim:
            ]
      else:
         lowerCAmelCase     =			val

   return orig_state_dict





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     str	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location='cpu'				)['model']

   lowerCAmelCase     =			get_swin_config(lowerCamelCase				)
   lowerCAmelCase     =			SwinForMaskedImageModeling(lowerCamelCase				)
   model.eval()

   lowerCAmelCase     =			convert_state_dict(lowerCamelCase	, lowerCamelCase				)
   model.load_state_dict(lowerCamelCase				)

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

   lowerCAmelCase     =			ViTImageProcessor(size={'height': 192, 'width': 192}				)
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)
   lowerCAmelCase     =			image_processor(images=lowerCamelCase	, return_tensors='pt'				)

   with torch.no_grad():
      lowerCAmelCase     =			model(**lowerCamelCase				).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(lowerCamelCase				)

      print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
      image_processor.save_pretrained(lowerCamelCase				)

   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__":
       __snake_case    						=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."""
       )

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









'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





