




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()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       torch.device("cpu")







def 				lowerCAmelCase_	(     ):
      _A							:     Optional[int]			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _A							:     Optional[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







def 				lowerCAmelCase_	(     snake_case_     ):
      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_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     str			=						dct.pop(snake_case_     )
      _A							:     Optional[Any]			=						val







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







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     List[str]			=						SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      _A							:     str			=						1000
      _A							:     List[str]			=						"""huggingface/label-files"""
      _A							:     Union[str, Any]			=						"""imagenet-1k-id2label.json"""
      _A							:     Tuple			=						json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""     ),"""r"""     )     )
      _A							:     int			=						{int(snake_case_     ): v for k, v in idalabel.items()}
      _A							:     Any			=						idalabel
      _A							:     List[str]			=						{v: k for k, v in idalabel.items()}

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

      elif swiftformer_name == "swiftformer_s":
            _A							:     Dict			=						[3, 3, 9, 6]
            _A							:     Union[str, Any]			=						[48, 64, 168, 224]

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

      elif swiftformer_name == "swiftformer_l3":
            _A							:     Tuple			=						[4, 4, 12, 6]
            _A							:     List[Any]			=						[64, 128, 320, 512]

      # load state_dict of original model, remove and rename some keys
      if original_ckpt:
            if original_ckpt.startswith("""https"""     ):
                  _A							:     Optional[Any]			=						torch.hub.load_state_dict_from_url(snake_case_,map_location="""cpu""",check_hash=snake_case_     )
            else:
                  _A							:     List[Any]			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     Dict			=						checkpoint

      _A							:     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
      _A							:     str			=						SwiftFormerForImageClassification(snake_case_     ).eval()
      hf_model.load_state_dict(snake_case_     )

      # prepare test inputs
      _A							:     Any			=						prepare_img()
      _A							:     Optional[Any]			=						ViTImageProcessor.from_pretrained("""preprocessor_config"""     )
      _A							:     Any			=						processor(images=snake_case_,return_tensors="""pt"""     )

      # compare outputs from both models
      _A							:     Union[str, Any]			=						get_expected_output(snake_case_     )
      _A							:     List[Any]			=						hf_model(inputs["""pixel_values"""]     ).logits

      assert hf_logits.shape == torch.Size([1, 1000]     )
      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__":
  _snake_case										=       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.")

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








"""simple docstring"""
from __future__ import annotations

_snake_case     :		str       						= []






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							for i in range(len(UpperCamelCase       )       ):
														if board[row][i] == 1:
																					return False
							for i in range(len(UpperCamelCase       )       ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(UpperCamelCase					,  -1					,  -1       )					,  range(UpperCamelCase					,  -1					,  -1       )       ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(UpperCamelCase					,  -1					,  -1       )					,  range(UpperCamelCase					,  len(UpperCamelCase       )       )       ):
														if board[i][j] == 1:
																					return False
							return True






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							if row >= len(UpperCamelCase       ):
														solution.append(UpperCamelCase       )
														printboard(UpperCamelCase       )
														print()
														return True
							for i in range(len(UpperCamelCase       )       ):
														if is_safe(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
																					A		=  1
																					solve(UpperCamelCase					,  row + 1       )
																					A		=  0
							return False






def 					A__				(			UpperCamelCase       ):
							for i in range(len(UpperCamelCase       )       ):
														for j in range(len(UpperCamelCase       )       ):
																					if board[i][j] == 1:
																												print("Q"					,  end=" "       )
																					else:
																												print("."					,  end=" "       )
														print()


# n=int(input("The no. of queens"))
_snake_case     :		List[str]       						= 8
_snake_case     :		List[str]       						= [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


