


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()
lowerCAmelCase__    :List[str]       =				logging.get_logger(__name__)

lowerCAmelCase__    :Any       =				torch.device('''cpu''')

def 			lowerCAmelCase__					(     )    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
							_UpperCAmelCase											=     Image.open(requests.get(lowerCAmelCase__     ,    stream=lowerCAmelCase__			).raw			)
							return im

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						str:


							'''simple docstring'''





							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]			)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]			)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]			)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]			)

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Dict     ,    a__:						Any			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     dct.pop(lowerCAmelCase__			)
							_UpperCAmelCase											=     val

def 			lowerCAmelCase__					(     a__:						Union[str, Any]			)    ->						Union[str, Any]:


							'''simple docstring'''





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

@torch.no_grad()
def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						Tuple     ,    a__:						List[Any]			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							_UpperCAmelCase											=     1_0_0_0
							_UpperCAmelCase											=     'huggingface/label-files'
							_UpperCAmelCase											=     'imagenet-1k-id2label.json'
							_UpperCAmelCase											=     json.load(open(hf_hub_download(lowerCAmelCase__     ,    lowerCAmelCase__     ,    repo_type='dataset'			)     ,    'r'			)			)
							_UpperCAmelCase											=     {int(lowerCAmelCase__			): v for k, v in idalabel.items()}
							_UpperCAmelCase											=     idalabel
							_UpperCAmelCase											=     {v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														_UpperCAmelCase											=     [3, 3, 6, 4]
														_UpperCAmelCase											=     [4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														_UpperCAmelCase											=     [3, 3, 9, 6]
														_UpperCAmelCase											=     [4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														_UpperCAmelCase											=     [4, 3, 1_0, 5]
														_UpperCAmelCase											=     [4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														_UpperCAmelCase											=     [4, 4, 1_2, 6]
														_UpperCAmelCase											=     [6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'			):
																					_UpperCAmelCase											=     torch.hub.load_state_dict_from_url(lowerCAmelCase__     ,    map_location='cpu'     ,    check_hash=lowerCAmelCase__			)
														else:
																					_UpperCAmelCase											=     torch.load(lowerCAmelCase__     ,    map_location='cpu'			)
							_UpperCAmelCase											=     checkpoint

							_UpperCAmelCase											=     create_rename_keys(lowerCAmelCase__			)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__			)

							# load HuggingFace model
							_UpperCAmelCase											=     SwiftFormerForImageClassification(lowerCAmelCase__			).eval()
							hf_model.load_state_dict(lowerCAmelCase__			)

							# prepare test inputs
							_UpperCAmelCase											=     prepare_img()
							_UpperCAmelCase											=     ViTImageProcessor.from_pretrained('preprocessor_config'			)
							_UpperCAmelCase											=     processor(images=lowerCAmelCase__     ,    return_tensors='pt'			)

							# compare outputs from both models
							_UpperCAmelCase											=     get_expected_output(lowerCAmelCase__			)
							_UpperCAmelCase											=     hf_model(inputs['pixel_values']			).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]			)
							assert torch.allclose(hf_logits[0, 0:5]     ,    lowerCAmelCase__     ,    atol=1e-3			)

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


if __name__ == "__main__":
		lowerCAmelCase__    :List[str]       =				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.''')

		lowerCAmelCase__    :str       =				parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




def  a   (lowerCAmelCase__   ):
   __a					=  False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __a					=  True
      for i in range(0		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False

      for i in range(1		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False
   return input_list


if __name__ == "__main__":
  print('Enter list to be sorted')
  SCREAMING_SNAKE_CASE							    =							[int(x) for x in input().split()]
  # inputing elements of the list in one line
  SCREAMING_SNAKE_CASE							    =							odd_even_sort(input_list)
  print('The sorted list is')
  print(sorted_list)



