





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 	lowerCAmelCase_					(			__a      ,       __a=False						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: Dict         =[]
  for i in range(config.num_hidden_layers						):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")						)
    rename_keys.append(
        (F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")						)
    rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")						)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("module.cls_token", "vit.embeddings.cls_token"),
          ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
          ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
          ("module.pos_embed", "vit.embeddings.position_embeddings"),
      ]						)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("module.norm.weight", "layernorm.weight"),
            ("module.norm.bias", "layernorm.bias"),
        ]						)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCamelCase__: int         =[(pair[0], pair[1][4:]) if pair[1].startswith("vit"						) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("norm.weight", "vit.layernorm.weight"),
            ("norm.bias", "vit.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]						)

  return rename_keys





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=False						)	->						Any:







  """simple docstring"""





  for i in range(config.num_hidden_layers						):
    if base_model:
      lowerCamelCase__: List[Any]         =""
    else:
      lowerCamelCase__: Dict         ="vit."
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCamelCase__: Tuple         =state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight"""						)
    lowerCamelCase__: int         =state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias"""						)
    # next, add query, keys and values (in that order) to the state dict
    lowerCamelCase__: int         =in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase__: Optional[int]         =in_proj_bias[: config.hidden_size]
    lowerCamelCase__: Union[str, Any]         =in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase__: str         =in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCamelCase__: Dict         =in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase__: str         =in_proj_bias[-config.hidden_size :]





def 	lowerCAmelCase_					(			__a						)	->						Optional[Any]:







  """simple docstring"""





  lowerCamelCase__: Tuple         =["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a						)	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Any         =[
      "module.fc.fc1.weight",
      "module.fc.fc1.bias",
      "module.fc.bn1.weight",
      "module.fc.bn1.bias",
      "module.fc.bn1.running_mean",
      "module.fc.bn1.running_var",
      "module.fc.bn1.num_batches_tracked",
      "module.fc.fc2.weight",
      "module.fc.fc2.bias",
      "module.fc.bn2.weight",
      "module.fc.bn2.bias",
      "module.fc.bn2.running_mean",
      "module.fc.bn2.running_var",
      "module.fc.bn2.num_batches_tracked",
      "module.fc.fc3.weight",
      "module.fc.fc3.bias",
  ]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: int         =dct.pop(__a						)
  lowerCamelCase__: Tuple         =val





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						str:







  """simple docstring"""





  lowerCamelCase__: List[str]         =ViTMSNConfig()
  lowerCamelCase__: Optional[Any]         =1000

  lowerCamelCase__: Dict         ="datasets/huggingface/label-files"
  lowerCamelCase__: List[str]         ="imagenet-1k-id2label.json"
  lowerCamelCase__: List[str]         =json.load(open(hf_hub_download(__a      ,       __a						)      ,       "r"						)						)
  lowerCamelCase__: int         ={int(__a						): v for k, v in idalabel.items()}
  lowerCamelCase__: List[str]         =idalabel
  lowerCamelCase__: Union[str, Any]         ={v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    lowerCamelCase__: Dict         =384
    lowerCamelCase__: Union[str, Any]         =1536
    lowerCamelCase__: Dict         =6
  elif "l16" in checkpoint_url:
    lowerCamelCase__: str         =1024
    lowerCamelCase__: List[Any]         =4096
    lowerCamelCase__: List[str]         =24
    lowerCamelCase__: List[Any]         =16
    lowerCamelCase__: Any         =0.1
  elif "b4" in checkpoint_url:
    lowerCamelCase__: List[str]         =4
  elif "l7" in checkpoint_url:
    lowerCamelCase__: Optional[Any]         =7
    lowerCamelCase__: int         =1024
    lowerCamelCase__: int         =4096
    lowerCamelCase__: Tuple         =24
    lowerCamelCase__: List[Any]         =16
    lowerCamelCase__: Tuple         =0.1

  lowerCamelCase__: Union[str, Any]         =ViTMSNModel(__a						)

  lowerCamelCase__: Tuple         =torch.hub.load_state_dict_from_url(__a      ,       map_location="cpu"						)["target_encoder"]

  lowerCamelCase__: Union[str, Any]         =ViTImageProcessor(size=config.image_size						)

  remove_projection_head(__a						)
  lowerCamelCase__: Optional[Any]         =create_rename_keys(__a      ,       base_model=__a						)

  for src, dest in rename_keys:
    rename_key(__a      ,       __a      ,       __a						)
  read_in_q_k_v(__a      ,       __a      ,       base_model=__a						)

  model.load_state_dict(__a						)
  model.eval()

  lowerCamelCase__: Dict         ="http://images.cocodataset.org/val2017/000000039769.jpg"

  lowerCamelCase__: Any         =Image.open(requests.get(__a      ,       stream=__a						).raw						)
  lowerCamelCase__: Union[str, Any]         =ViTImageProcessor(
      size=config.image_size      ,       image_mean=__a      ,       image_std=__a						)
  lowerCamelCase__: List[str]         =image_processor(images=__a      ,       return_tensors="pt"						)

  # forward pass
  torch.manual_seed(2						)
  lowerCamelCase__: Union[str, Any]         =model(**__a						)
  lowerCamelCase__: List[str]         =outputs.last_hidden_state

  # The following Colab Notebook was used to generate these outputs:
  # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
  if "s16" in checkpoint_url:
    lowerCamelCase__: str         =torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]						)
  elif "b16" in checkpoint_url:
    lowerCamelCase__: Tuple         =torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]						)
  elif "l16" in checkpoint_url:
    lowerCamelCase__: List[Any]         =torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]						)
  elif "b4" in checkpoint_url:
    lowerCamelCase__: Union[str, Any]         =torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]						)
  else:
    lowerCamelCase__: Dict         =torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]						)

  # verify logits
  assert torch.allclose(last_hidden_state[:, 0, :3]      ,       __a      ,       atol=1e-4						)

  print(F"""Saving model to {pytorch_dump_folder_path}"""						)
  model.save_pretrained(__a						)

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


if __name__ == "__main__":
     __A							   =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
         type=str,
         help="URL of the checkpoint 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_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    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 SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_3			,				_a=3			,				_a=True			,				_a=True			,				_a=0.1			,				_a=0.1			,				_a=2_2_4			,				_a=1_0_0_0			,				_a=[3, 3, 6, 4]			,				_a=[4_8, 5_6, 1_1_2, 2_2_0]			,				)       ->       Tuple:
												_a      :       Dict   =						parent
												_a      :       Optional[int]   =						batch_size
												_a      :       Optional[Any]   =						num_channels
												_a      :       Union[str, Any]   =						is_training
												_a      :       Tuple   =						use_labels
												_a      :       Dict   =						hidden_dropout_prob
												_a      :       List[Any]   =						attention_probs_dropout_prob
												_a      :       Dict   =						num_labels
												_a      :       List[str]   =						image_size
												_a      :       Dict   =						layer_depths
												_a      :       str   =						embed_dims


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       int   =						None
												if self.use_labels:
																	_a      :       Optional[Any]   =						ids_tensor([self.batch_size]			,				self.num_labels			)

												_a      :       Dict   =						self.get_config()

												return config, pixel_values, labels


							def 							__lowercase							(     self			)       ->       int:
												return SwiftFormerConfig(
												    depths=self.layer_depths			,				embed_dims=self.embed_dims			,				mlp_ratio=4			,				downsamples=[True, True, True, True]			,				hidden_act='''gelu'''			,				num_labels=self.num_labels			,				down_patch_size=3			,				down_stride=2			,				down_pad=1			,				drop_rate=0.0			,				drop_path_rate=0.0			,				use_layer_scale=_a			,				layer_scale_init_value=1e-5			,				)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       str:
												_a      :       List[Any]   =						SwiftFormerModel(config=_a			)
												model.to(_a			)
												model.eval()
												_a      :       Optional[int]   =						model(_a			)
												self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.embed_dims[-1], 7, 7)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.num_labels
												_a      :       Optional[int]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       List[str]   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)

												_a      :       Union[str, Any]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()

												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
												_a      :       Optional[Any]   =						model(_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)




							def 							__lowercase							(     self			)       ->       Tuple:
												((_a) ,   (_a) ,   (_a))      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a      :       List[Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							UpperCAmelCase__			:		Optional[int]             =       (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Optional[Any]             =       False
							UpperCAmelCase__			:		str             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		str             =       False


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Union[str, Any]   =						SwiftFormerModelTester(self			)
												_a      :       int   =						ConfigTester(
												    self			,				config_class=_a			,				has_text_modality=_a			,				hidden_size=3_7			,				num_attention_heads=1_2			,				num_hidden_layers=1_2			,				)


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


							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							def 							__lowercase							(     self			)       ->       Dict:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       Any   =						model_class(_a			)
																	_a      :       int   =						model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_a			,				nn.Linear			)			)


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a      :       List[str]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Any   =						SwiftFormerModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)


							@unittest.skip(reason='''SwiftFormer does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							def 							__lowercase							(     self			)       ->       int:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Optional[int]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

																	with torch.no_grad():
																						_a      :       Union[str, Any]   =						model(**self._prepare_for_class(_a			,				_a			)			)

																	_a      :       Optional[Any]   =						outputs.hidden_states

																	_a      :       Union[str, Any]   =						8
																	self.assertEqual(len(_a			)			,				_a			)  # TODO

																	# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																	# with the width and height being successively divided by 2, after every 2 blocks
																	for i in range(len(_a			)			):
																						self.assertEqual(
																						    hidden_states[i].shape			,				torch.Size(
																						        [
																						            self.model_tester.batch_size,
																						            self.model_tester.embed_dims[i // 2],
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						        ]			)			,				)

												_a ,   _a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       str   =						True
																	check_hidden_states_output(_a			,				_a			,				_a			)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_a      :       List[str]   =						True

																	check_hidden_states_output(_a			,				_a			,				_a			)


							def 							__lowercase							(     self			)       ->       str:
												def _config_zero_init(_a			):
																	_a      :       List[Any]   =						copy.deepcopy(_a			)
																	for key in configs_no_init.__dict__.keys():
																						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																											setattr(_a			,				_a			,				1e-1_0			)
																						if isinstance(getattr(_a			,				_a			,				_a			)			,				_a			):
																											_a      :       int   =						_config_zero_init(getattr(_a			,				_a			)			)
																											setattr(_a			,				_a			,				_a			)
																	return configs_no_init

												_a ,   _a      :       Any   =						self.model_tester.prepare_config_and_inputs_for_common()

												_a      :       Dict   =						_config_zero_init(_a			)
												for model_class in self.all_model_classes:
																	_a      :       Dict   =						model_class(config=_a			)
																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9) / 1e9).round().item()			,				[0.0, 1.0]			,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,				)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



def   __UpperCAmelCase					(      )     ->	Optional[Any]:

					"""simple docstring"""

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







@require_torch
@require_vision
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       str:
												return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''			) if is_vision_available() else None




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''			).to(_a			)

												_a      :       Any   =						self.default_image_processor
												_a      :       Any   =						prepare_img()
												_a      :       Any   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Optional[Any]   =						model(**_a			)

												# verify the logits
												_a      :       List[str]   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       int   =						torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]			).to(_a			)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				_a			,				atol=1e-4			)			)



