

"""simple docstring"""




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    (       __UpperCamelCase				,     __UpperCamelCase=False   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Any        =       []
 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"
  UpperCAmelCase__       :	Any        =       [(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    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=False   ):
 '''simple docstring'''





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





 UpperCAmelCase__       :	List[Any]        =       ["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(__UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[int]        =       [
     """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(__UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[Any]        =       dct.pop(__UpperCamelCase   )
 UpperCAmelCase__       :	Tuple        =       val
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Dict        =       ViTMSNConfig()
 UpperCAmelCase__       :	Any        =       1000

 UpperCAmelCase__       :	List[Any]        =       """datasets/huggingface/label-files"""
 UpperCAmelCase__       :	int        =       """imagenet-1k-id2label.json"""
 UpperCAmelCase__       :	List[Any]        =       json.load(open(hf_hub_download(__UpperCamelCase				,     __UpperCamelCase   )				,     """r"""   )   )
 UpperCAmelCase__       :	Tuple        =       {int(__UpperCamelCase   ): v for k, v in idalabel.items()}
 UpperCAmelCase__       :	Optional[int]        =       idalabel
 UpperCAmelCase__       :	Any        =       {v: k for k, v in idalabel.items()}

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

 UpperCAmelCase__       :	int        =       ViTMSNModel(__UpperCamelCase   )

 UpperCAmelCase__       :	Any        =       torch.hub.load_state_dict_from_url(__UpperCamelCase				,     map_location="""cpu"""   )["""target_encoder"""]

 UpperCAmelCase__       :	List[str]        =       ViTImageProcessor(size=config.image_size   )

 remove_projection_head(__UpperCamelCase   )
 UpperCAmelCase__       :	Union[str, Any]        =       create_rename_keys(__UpperCamelCase				,     base_model=__UpperCamelCase   )

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

 model.load_state_dict(__UpperCamelCase   )
 model.eval()

 UpperCAmelCase__       :	Any        =       """http://images.cocodataset.org/val2017/000000039769.jpg"""

 UpperCAmelCase__       :	List[Any]        =       Image.open(requests.get(__UpperCamelCase				,     stream=__UpperCamelCase   ).raw   )
 UpperCAmelCase__       :	Tuple        =       ViTImageProcessor(
     size=config.image_size				,     image_mean=__UpperCamelCase				,     image_std=__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[Any]        =       image_processor(images=__UpperCamelCase				,     return_tensors="""pt"""   )

 # forward pass
 torch.manual_seed(2   )
 UpperCAmelCase__       :	Any        =       model(**__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[Any]        =       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:
  UpperCAmelCase__       :	int        =       torch.tensor([[-1.0915, -1.4876, -1.1809]]   )
 elif "b16" in checkpoint_url:
  UpperCAmelCase__       :	Optional[Any]        =       torch.tensor([[14.2889, -18.9045, 11.7281]]   )
 elif "l16" in checkpoint_url:
  UpperCAmelCase__       :	int        =       torch.tensor([[41.5028, -22.8681, 45.6475]]   )
 elif "b4" in checkpoint_url:
  UpperCAmelCase__       :	Dict        =       torch.tensor([[-4.3868, 5.2932, -0.4137]]   )
 else:
  UpperCAmelCase__       :	Optional[Any]        =       torch.tensor([[-0.1792, -0.6465, 2.4263]]   )

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

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

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


if __name__ == "__main__":
      __UpperCAmelCase			=					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.'
      )

      __UpperCAmelCase			=					parser.parse_args()
      convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



