



'''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()
lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						torch.device("cpu")




def lowerCAmelCase						():



     """simple docstring"""



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




def lowerCAmelCase						(__A):



     """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						,			__A						,			__A):



     """simple docstring"""



     _a											=						dct.pop(__A)
     _a											=						val




def lowerCAmelCase						(__A):



     """simple docstring"""



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




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



     """simple docstring"""



     _a											=						SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     _a											=						1_000
     _a											=						'''huggingface/label-files'''
     _a											=						'''imagenet-1k-id2label.json'''
     _a											=						json.load(open(hf_hub_download(__A						,			__A						,			repo_type='''dataset''')						,			'''r'''))
     _a											=						{int(__A): v for k, v in idalabel.items()}
     _a											=						idalabel
     _a											=						{v: k for k, v in idalabel.items()}

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          _a											=						[3, 3, 6, 4]
          _a											=						[48, 56, 112, 220]

     elif swiftformer_name == "swiftformer_s":
          _a											=						[3, 3, 9, 6]
          _a											=						[48, 64, 168, 224]

     elif swiftformer_name == "swiftformer_l1":
          _a											=						[4, 3, 10, 5]
          _a											=						[48, 96, 192, 384]

     elif swiftformer_name == "swiftformer_l3":
          _a											=						[4, 4, 12, 6]
          _a											=						[64, 128, 320, 512]

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

     _a											=						create_rename_keys(__A)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(__A						,			__A						,			__A)

     # load HuggingFace model
     _a											=						SwiftFormerForImageClassification(__A).eval()
     hf_model.load_state_dict(__A)

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

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

     assert hf_logits.shape == torch.Size([1, 1_000])
     assert torch.allclose(hf_logits[0, 0:5]						,			__A						,			atol=1e-3)

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


if __name__ == "__main__":
  lowercase_	 =						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.")

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








'''simple docstring'''





lowercase_	 =						256
# Modulus to hash a string
lowercase_	 =						1_000_003




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						len(__A)
     _a											=						len(__A)
     if p_len > t_len:
          return False

     _a											=						0
     _a											=						0
     _a											=						1

     # Calculating the hash of pattern and substring of text
     for i in range(__A):
          _a											=						(ord(pattern[i]) + p_hash * alphabet_size) % modulus
          _a											=						(ord(text[i]) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          _a											=						(modulus_power * alphabet_size) % modulus

     for i in range(0						,			t_len - p_len + 1):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          _a											=						(
              (text_hash - ord(text[i]) * modulus_power) * alphabet_size
              + ord(text[i + p_len])
          ) % modulus
     return False




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						'''abc1abc12'''
     _a											=						'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     _a											=						'''alskfjaldsk23adsfabcabc'''
     assert rabin_karp(__A						,			__A) and not rabin_karp(__A						,			__A)

     # Test 2)
     _a											=						'''ABABX'''
     _a											=						'''ABABZABABYABABX'''
     assert rabin_karp(__A						,			__A)

     # Test 3)
     _a											=						'''AAAB'''
     _a											=						'''ABAAAAAB'''
     assert rabin_karp(__A						,			__A)

     # Test 4)
     _a											=						'''abcdabcy'''
     _a											=						'''abcxabcdabxabcdabcdabcy'''
     assert rabin_karp(__A						,			__A)

     # Test 5)
     _a											=						'''Lü'''
     _a											=						'''Lüsai'''
     assert rabin_karp(__A						,			__A)
     _a											=						'''Lue'''
     assert not rabin_karp(__A						,			__A)
     print('''Success.''')


if __name__ == "__main__":
  test_rabin_karp()



