




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=30       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=2       ,)			->		Union[str, Any]:
         UpperCAmelCase_      :							List[str]       =      parent
         UpperCAmelCase_      :							Union[str, Any]       =      batch_size
         UpperCAmelCase_      :							Dict       =      image_size
         UpperCAmelCase_      :							Any       =      patch_size
         UpperCAmelCase_      :							Optional[Any]       =      num_channels
         UpperCAmelCase_      :							str       =      is_training
         UpperCAmelCase_      :							List[str]       =      use_labels
         UpperCAmelCase_      :							int       =      hidden_size
         UpperCAmelCase_      :							Optional[int]       =      num_hidden_layers
         UpperCAmelCase_      :							Optional[Any]       =      num_attention_heads
         UpperCAmelCase_      :							Tuple       =      intermediate_size
         UpperCAmelCase_      :							Any       =      hidden_act
         UpperCAmelCase_      :							int       =      hidden_dropout_prob
         UpperCAmelCase_      :							int       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[Any]       =      type_sequence_label_size
         UpperCAmelCase_      :							Optional[int]       =      initializer_range
         UpperCAmelCase_      :							Any       =      scope
         UpperCAmelCase_      :							List[str]       =      encoder_stride

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_      :							Tuple       =      (image_size // patch_size) ** 2
         UpperCAmelCase_      :							Union[str, Any]       =      num_patches + 1





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Any       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         UpperCAmelCase_      :							Union[str, Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.get_config()

         return config, pixel_values, labels





    def 							a__				(				self  )			->		Tuple:
         return ViTConfig(
             image_size=self.image_size       ,patch_size=self.patch_size       ,num_channels=self.num_channels       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,is_decoder=_SCREAMING_SNAKE_CASE       ,initializer_range=self.initializer_range       ,encoder_stride=self.encoder_stride       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							Dict       =      ViTModel(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      ViTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(
             result.reconstruction.shape       ,(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

         # test greyscale images
         UpperCAmelCase_      :							Optional[Any]       =      1
         UpperCAmelCase_      :							str       =      ViTForMaskedImageModeling(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()

         UpperCAmelCase_      :							Union[str, Any]       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.reconstruction.shape       ,(self.batch_size, 1, self.image_size, self.image_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							int       =      self.type_sequence_label_size
         UpperCAmelCase_      :							str       =      ViTForImageClassification(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							str       =      model(_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)  )

         # test greyscale images
         UpperCAmelCase_      :							Dict       =      1
         UpperCAmelCase_      :							int       =      ViTForImageClassification(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()

         UpperCAmelCase_      :							Any       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         UpperCAmelCase_      :							Dict       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Tuple       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							List[Any]       =      config_and_inputs
         UpperCAmelCase_      :							List[Any]       =      {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (
            ViTModel,
            ViTForImageClassification,
            ViTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase        =				(
        {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
        if is_torch_available()
        else {}
    )
    lowerCAmelCase        =				True

    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							List[str]       =      ViTModelTester(self  )
         UpperCAmelCase_      :							Dict       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,has_text_modality=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		int:
         self.config_tester.run_common_tests()





    @unittest.skip(reason='''ViT does not use inputs_embeds'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_,   UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							str       =      model_class(_SCREAMING_SNAKE_CASE  )
              self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module)  )
              UpperCAmelCase_      :							int       =      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,nn.Linear  )  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_,   UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Any       =      model_class(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[int]       =      inspect.signature(model.forward  )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_      :							str       =      [*signature.parameters.keys()]

              UpperCAmelCase_      :							str       =      ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		Dict:
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							List[str]       =      ViTModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image



@require_torch
@require_vision
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		Tuple:
         return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''  ) if is_vision_available() else None





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Dict       =      ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''  ).to(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      self.default_image_processor
         UpperCAmelCase_      :							Tuple       =      prepare_img()
         UpperCAmelCase_      :							Tuple       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_      :							Union[str, Any]       =      model(**_SCREAMING_SNAKE_CASE  )

         # verify the logits
         UpperCAmelCase_      :							Optional[int]       =      torch.Size((1, 1_000)  )
         self.assertEqual(outputs.logits.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      torch.tensor([-0.27_44, 0.82_15, -0.08_36]  ).to(_SCREAMING_SNAKE_CASE  )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





    @slow
    def 							a__				(				self  )			->		Dict:
         # ViT models have an `interpolate_pos_encoding` argument in their forward method,
         # allowing to interpolate the pre-trained position embeddings in order to use
         # the model on higher resolutions. The DINO model by Facebook AI leverages this
         # to visualize self-attention on higher resolution images.
         UpperCAmelCase_      :							Optional[int]       =      ViTModel.from_pretrained('''facebook/dino-vits8'''  ).to(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''       ,size=480  )
         UpperCAmelCase_      :							Tuple       =      prepare_img()
         UpperCAmelCase_      :							List[Any]       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  )
         UpperCAmelCase_      :							List[str]       =      inputs.pixel_values.to(_SCREAMING_SNAKE_CASE  )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_      :							Tuple       =      model(_SCREAMING_SNAKE_CASE       ,interpolate_pos_encoding=_SCREAMING_SNAKE_CASE  )

         # verify the logits
         UpperCAmelCase_      :							str       =      torch.Size((1, 3_601, 384)  )
         self.assertEqual(outputs.last_hidden_state.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      torch.tensor(
             [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]  ).to(_SCREAMING_SNAKE_CASE  )

         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





    @slow
    @require_accelerate
    @require_torch_gpu
    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[int]       =      ViTModel.from_pretrained('''facebook/dino-vits8'''       ,torch_dtype=torch.floataa       ,device_map='''auto'''  )
         UpperCAmelCase_      :							Any       =      self.default_image_processor

         UpperCAmelCase_      :							Any       =      prepare_img()
         UpperCAmelCase_      :							Any       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  )
         UpperCAmelCase_      :							str       =      inputs.pixel_values.to(_SCREAMING_SNAKE_CASE  )

         # forward pass to make sure inference works in fp16
         with torch.no_grad():
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE  )





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

     if model_size == "tiny":
          UpperCAmelCase_      :							Tuple       =      96
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 6, 2)
          UpperCAmelCase_      :							str       =      (3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_      :							Dict       =      96
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							List[str]       =      (3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_      :							Tuple       =      128
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Optional[int]       =      (4, 8, 16, 32)
     else:
          UpperCAmelCase_      :							List[str]       =      192
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Union[str, Any]       =      (6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if "patch_embed.proj" in name:
          UpperCAmelCase_      :							str       =      name.replace('''patch_embed.proj'''			,						'''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''patch_embed.norm'''			,						'''embeddings.norm'''				)
     if "layers" in name:
          UpperCAmelCase_      :							Tuple       =      '''encoder.''' + name
     if "attn.proj" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''attn.proj'''			,						'''attention.output.dense'''				)
     if "attn" in name:
          UpperCAmelCase_      :							int       =      name.replace('''attn'''			,						'''attention.self'''				)
     if "norm1" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''norm1'''			,						'''layernorm_before'''				)
     if "norm2" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''norm2'''			,						'''layernorm_after'''				)
     if "mlp.fc1" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''mlp.fc1'''			,						'''intermediate.dense'''				)
     if "mlp.fc2" in name:
          UpperCAmelCase_      :							Optional[int]       =      name.replace('''mlp.fc2'''			,						'''output.dense'''				)

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_      :							str       =      key.split('''.'''				)
               UpperCAmelCase_      :							str       =      int(key_split[1]				)
               UpperCAmelCase_      :							Optional[Any]       =      int(key_split[3]				)
               UpperCAmelCase_      :							Optional[int]       =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

     assert torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

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


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)