






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





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(_snake_case,						_snake_case       )





def 				lowercase__		(					lowerCamelCase       ):
				# projection head is used in the self-supervised pre-training in MSN,
				# for downstream task it's not needed.
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    [
				    '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(_snake_case,						_snake_case       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    dct.pop(_snake_case       )
				_SCREAMING_SNAKE_CASE   :		int       		=    val





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    ViTMSNConfig()
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    1_000

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

				if "s16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		str       		=    384
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1_536
								_SCREAMING_SNAKE_CASE   :		Any       		=    6
				elif "l16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1_024
								_SCREAMING_SNAKE_CASE   :		int       		=    4_096
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    24
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    16
								_SCREAMING_SNAKE_CASE   :		int       		=    0.1
				elif "b4" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    4
				elif "l7" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    7
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    1_024
								_SCREAMING_SNAKE_CASE   :		Dict       		=    4_096
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    24
								_SCREAMING_SNAKE_CASE   :		int       		=    16
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0.1

				_SCREAMING_SNAKE_CASE   :		Any       		=    ViTMSNModel(_snake_case       )

				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    torch.hub.load_state_dict_from_url(_snake_case,						map_location='cpu'       )['target_encoder']

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

				remove_projection_head(_snake_case       )
				_SCREAMING_SNAKE_CASE   :		int       		=    create_rename_keys(_snake_case,						base_model=_snake_case       )

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

				model.load_state_dict(_snake_case       )
				model.eval()

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

				_SCREAMING_SNAKE_CASE   :		int       		=    Image.open(requests.get(_snake_case,						stream=_snake_case       ).raw       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ViTImageProcessor(
				    size=config.image_size,						image_mean=_snake_case,						image_std=_snake_case       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    image_processor(images=_snake_case,						return_tensors='pt'       )

				# forward pass
				torch.manual_seed(2       )
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(**_snake_case       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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:
								_SCREAMING_SNAKE_CASE   :		Any       		=    torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]       )
				elif "b16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		int       		=    torch.tensor([[14.2_889, -18.9_045, 11.7_281]]       )
				elif "l16" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		int       		=    torch.tensor([[41.5_028, -22.8_681, 45.6_475]]       )
				elif "b4" in checkpoint_url:
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]       )
				else:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]       )

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

				print(f"""Saving model 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 __name__ == "__main__":
			lowerCAmelCase__    						=			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.'
			)

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






UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



