





'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase			    =    logging.get_logger(__name__)

__UpperCAmelCase			    =    torch.device("""cpu""")



def 						__A						(   ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							List[Any]						=       "http://images.cocodataset.org/val2017/000000039769.jpg"
		SCREAMING_SNAKE_CASE      :							Any						=       Image.open(requests.get(__snake_case       ,       stream=__snake_case     ).raw     )
		return im



def 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]     )

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]     )

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]     )

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]     )



def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       dct.pop(__snake_case     )
		SCREAMING_SNAKE_CASE      :							Optional[int]						=       val



def 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							int						=       []
		for k in state_dict.keys():
				SCREAMING_SNAKE_CASE      :							List[str]						=       k
				if ".pwconv" in k:
						SCREAMING_SNAKE_CASE      :							List[Any]						=       k_new.replace(""".pwconv"""       ,       """.point_wise_conv"""     )
				if ".dwconv" in k:
						SCREAMING_SNAKE_CASE      :							Any						=       k_new.replace(""".dwconv"""       ,       """.depth_wise_conv"""     )
				if ".Proj." in k:
						SCREAMING_SNAKE_CASE      :							Optional[Any]						=       k_new.replace(""".Proj."""       ,       """.proj."""     )
				if "patch_embed" in k_new:
						SCREAMING_SNAKE_CASE      :							int						=       k_new.replace("""patch_embed"""       ,       """swiftformer.patch_embed.patch_embedding"""     )
				if "network" in k_new:
						SCREAMING_SNAKE_CASE      :							int						=       k_new.split("""."""     )
						if ls[2].isdigit():
								SCREAMING_SNAKE_CASE      :							Optional[int]						=       "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]     )
						else:
								SCREAMING_SNAKE_CASE      :							str						=       k_new.replace("""network"""       ,       """swiftformer.encoder.network"""     )
				rename_keys.append((k, k_new)     )
		return rename_keys



@torch.no_grad()
def 						__A						(   lowerCamelCase_       ,       lowerCamelCase_       ,       lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       10_00
		SCREAMING_SNAKE_CASE      :							int						=       "huggingface/label-files"
		SCREAMING_SNAKE_CASE      :							List[Any]						=       "imagenet-1k-id2label.json"
		SCREAMING_SNAKE_CASE      :							List[str]						=       json.load(open(hf_hub_download(__snake_case       ,       __snake_case       ,       repo_type="""dataset"""     )       ,       """r"""     )     )
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       {int(__snake_case     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE      :							Optional[int]						=       idalabel
		SCREAMING_SNAKE_CASE      :							Any						=       {v: k for k, v in idalabel.items()}

		# size of the architecture
		if swiftformer_name == "swiftformer_xs":
				SCREAMING_SNAKE_CASE      :							int						=       [3, 3, 6, 4]
				SCREAMING_SNAKE_CASE      :							Dict						=       [48, 56, 1_12, 2_20]

		elif swiftformer_name == "swiftformer_s":
				SCREAMING_SNAKE_CASE      :							Any						=       [3, 3, 9, 6]
				SCREAMING_SNAKE_CASE      :							Tuple						=       [48, 64, 1_68, 2_24]

		elif swiftformer_name == "swiftformer_l1":
				SCREAMING_SNAKE_CASE      :							Optional[int]						=       [4, 3, 10, 5]
				SCREAMING_SNAKE_CASE      :							List[Any]						=       [48, 96, 1_92, 3_84]

		elif swiftformer_name == "swiftformer_l3":
				SCREAMING_SNAKE_CASE      :							str						=       [4, 4, 12, 6]
				SCREAMING_SNAKE_CASE      :							Tuple						=       [64, 1_28, 3_20, 5_12]

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith("""https"""     ):
						SCREAMING_SNAKE_CASE      :							str						=       torch.hub.load_state_dict_from_url(__snake_case       ,       map_location="""cpu"""       ,       check_hash=__snake_case     )
				else:
						SCREAMING_SNAKE_CASE      :							int						=       torch.load(__snake_case       ,       map_location="""cpu"""     )
		SCREAMING_SNAKE_CASE      :							List[str]						=       checkpoint

		SCREAMING_SNAKE_CASE      :							Dict						=       create_rename_keys(__snake_case     )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(__snake_case       ,       __snake_case       ,       __snake_case     )

		# load HuggingFace model
		SCREAMING_SNAKE_CASE      :							Any						=       SwiftFormerForImageClassification(__snake_case     ).eval()
		hf_model.load_state_dict(__snake_case     )

		# prepare test inputs
		SCREAMING_SNAKE_CASE      :							Optional[int]						=       prepare_img()
		SCREAMING_SNAKE_CASE      :							Optional[int]						=       ViTImageProcessor.from_pretrained("""preprocessor_config"""     )
		SCREAMING_SNAKE_CASE      :							List[str]						=       processor(images=__snake_case       ,       return_tensors="""pt"""     )

		# compare outputs from both models
		SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       get_expected_output(__snake_case     )
		SCREAMING_SNAKE_CASE      :							int						=       hf_model(inputs["""pixel_values"""]     ).logits

		assert hf_logits.shape == torch.Size([1, 10_00]     )
		assert torch.allclose(hf_logits[0, 0:5]       ,       __snake_case       ,       atol=1E-3     )

		Path(__snake_case     ).mkdir(exist_ok=__snake_case     )
		print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''     )
		hf_model.save_pretrained(__snake_case     )


if __name__ == "__main__":
							__UpperCAmelCase			    =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swiftformer_name""",
							    default="""swiftformer_xs""",
							    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
							    type=str,
							    help="""Name of the SwiftFormer model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""./converted_outputs/""",
							    type=str,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

							__UpperCAmelCase			    =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





"""simple docstring"""




def   __magic_name__	(      __snake_case				:       list  ) ->				list:

       if len(__snake_case  ) < 2:
              return collection

       def circle_sort_util(__snake_case				:       list     , __snake_case				:       int     , __snake_case				:       int  ) -> bool:
              lowercase    :				List[Any]       		=							False

              if low == high:
                     return swapped

              lowercase    :				Union[str, Any]       		=							low
              lowercase    :				str       		=							high

              while left < right:
                     if collection[left] > collection[right]:
                            lowercase		,    lowercase    :				Optional[Any]       		=							(
                                collection[right],
                                collection[left],
                            )
                            lowercase    :				Tuple       		=							True

                     left += 1
                     right -= 1

              if left == right and collection[left] > collection[right + 1]:
                     lowercase		,    lowercase    :				str       		=							(
                         collection[right + 1],
                         collection[left],
                     )

                     lowercase    :				Union[str, Any]       		=							True

              lowercase    :				Any       		=							low + int((high - low) / 2  )
              lowercase    :				Tuple       		=							circle_sort_util(__snake_case     , __snake_case     , __snake_case  )
              lowercase    :				List[Any]       		=							circle_sort_util(__snake_case     , mid + 1     , __snake_case  )

              return swapped or left_swap or right_swap

       lowercase    :				int       		=							True

       while is_not_sorted is True:
              lowercase    :				int       		=							circle_sort_util(__snake_case     , 0     , len(__snake_case  ) - 1  )

       return collection


if __name__ == "__main__":
   _A			:       str   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Dict   		=       [int(item) for item in user_input.split(""",""")]
   print(circle_sort(unsorted))


