





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_					:   Optional[int] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   str 							=						torch.device("""cpu""")






def 							_lowerCAmelCase (  )	->			List[Any]:
     lowerCAmelCase_					:       str    =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     lowerCAmelCase_					:       int    =		Image.open(requests.get(_a					,   stream=_a			).raw			)
     return im






def 							_lowerCAmelCase (  _a   :					Optional[int]			)	->			str:
     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]			)

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]			)

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]			)

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]			)






def 							_lowerCAmelCase (  _a   :					Union[str, Any]					,   _a   :					List[str]					,   _a   :					Optional[int]			)	->			int:
     lowerCAmelCase_					:       Tuple    =		dct.pop(_a			)
     lowerCAmelCase_					:       Any    =		val






def 							_lowerCAmelCase (  _a   :					str			)	->			Optional[Any]:
     lowerCAmelCase_					:       str    =		[]
     for k in state_dict.keys():
          lowerCAmelCase_					:       Optional[Any]    =		k
          if ".pwconv" in k:
               lowerCAmelCase_					:       List[Any]    =		k_new.replace(""".pwconv"""					,   """.point_wise_conv"""			)
          if ".dwconv" in k:
               lowerCAmelCase_					:       Union[str, Any]    =		k_new.replace(""".dwconv"""					,   """.depth_wise_conv"""			)
          if ".Proj." in k:
               lowerCAmelCase_					:       Dict    =		k_new.replace(""".Proj."""					,   """.proj."""			)
          if "patch_embed" in k_new:
               lowerCAmelCase_					:       int    =		k_new.replace("""patch_embed"""					,   """swiftformer.patch_embed.patch_embedding"""			)
          if "network" in k_new:
               lowerCAmelCase_					:       Any    =		k_new.split("""."""			)
               if ls[2].isdigit():
                    lowerCAmelCase_					:       int    =		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]			)
               else:
                    lowerCAmelCase_					:       Dict    =		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   :					Union[str, Any]					,   _a   :					Optional[Any]			)	->			Tuple:
     lowerCAmelCase_					:       str    =		SwiftFormerConfig()

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

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          lowerCAmelCase_					:       Any    =		[3, 3, 6, 4]
          lowerCAmelCase_					:       Union[str, Any]    =		[48, 56, 1_12, 2_20]

     elif swiftformer_name == "swiftformer_s":
          lowerCAmelCase_					:       Optional[int]    =		[3, 3, 9, 6]
          lowerCAmelCase_					:       int    =		[48, 64, 1_68, 2_24]

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

     elif swiftformer_name == "swiftformer_l3":
          lowerCAmelCase_					:       Any    =		[4, 4, 12, 6]
          lowerCAmelCase_					:       List[Any]    =		[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"""			):
               lowerCAmelCase_					:       Any    =		torch.hub.load_state_dict_from_url(_a					,   map_location="""cpu"""					,   check_hash=_a			)
          else:
               lowerCAmelCase_					:       List[str]    =		torch.load(_a					,   map_location="""cpu"""			)
     lowerCAmelCase_					:       int    =		checkpoint

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

     # load HuggingFace model
     lowerCAmelCase_					:       Tuple    =		SwiftFormerForImageClassification(_a			).eval()
     hf_model.load_state_dict(_a			)

     # prepare test inputs
     lowerCAmelCase_					:       Tuple    =		prepare_img()
     lowerCAmelCase_					:       Tuple    =		ViTImageProcessor.from_pretrained("""preprocessor_config"""			)
     lowerCAmelCase_					:       Any    =		processor(images=_a					,   return_tensors="""pt"""			)

     # compare outputs from both models
     lowerCAmelCase_					:       Union[str, Any]    =		get_expected_output(_a			)
     lowerCAmelCase_					:       Dict    =		hf_model(inputs["""pixel_values"""]			).logits

     assert hf_logits.shape == torch.Size([1, 10_00]			)
     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__":
      UpperCAmelCase_					:   Dict 							=						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_					:   int 							=						parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







from collections.abc import Sequence






def 							_lowerCAmelCase (  _a   :					Sequence[float]					,   _a   :					bool = False			)	->			float:
     if not arr:
          return 0

     lowerCAmelCase_					:       List[str]    =		0 if allow_empty_subarrays else float("""-inf"""			)
     lowerCAmelCase_					:       List[Any]    =		0.0
     for num in arr:
          lowerCAmelCase_					:       int    =		max(0 if allow_empty_subarrays else num					,   curr_sum + num			)
          lowerCAmelCase_					:       List[str]    =		max(_a					,   _a			)

     return max_sum


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      UpperCAmelCase_					:   int 							=						[-2, 1, -3, 4, -1, 2, 1, -5, 4]
      print(F"""{max_subarray_sum(nums) = }""")
