
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      :				int          = logging.get_logger(__name__)

_UpperCAmelCase      :				List[Any]          = torch.device("cpu")


def    A (      )     ->						str:
		'''simple docstring'''

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


def    A (      lowercase				)     ->						List[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    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  dct.pop(__UpperCAmelCase				)
		UpperCamelCase						=  val


def    A (      lowercase				)     ->						Tuple:
		'''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    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		UpperCamelCase						=  1_000
		UpperCamelCase						=  'huggingface/label-files'
		UpperCamelCase						=  'imagenet-1k-id2label.json'
		UpperCamelCase						=  json.load(open(hf_hub_download(__UpperCAmelCase					, __UpperCAmelCase					, repo_type='dataset'				)					, 'r'				)				)
		UpperCamelCase						=  {int(__UpperCAmelCase				): 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						=  [48, 56, 112, 220]

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

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

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

		# 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(__UpperCAmelCase					, map_location='cpu'					, check_hash=__UpperCAmelCase				)
				else:
						UpperCamelCase						=  torch.load(__UpperCAmelCase					, map_location='cpu'				)
		UpperCamelCase						=  checkpoint

		UpperCamelCase						=  create_rename_keys(__UpperCAmelCase				)
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(__UpperCAmelCase					, __UpperCAmelCase					, __UpperCAmelCase				)

		# load HuggingFace model
		UpperCamelCase						=  SwiftFormerForImageClassification(__UpperCAmelCase				).eval()
		hf_model.load_state_dict(__UpperCAmelCase				)

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

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

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

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


if __name__ == "__main__":
					_UpperCAmelCase      :				Union[str, Any]          = 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      :				Tuple          = parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowercase				)
		UpperCamelCase			,						UpperCamelCase						=  divmod(lowercase					, 2				)
		return binary_recursive(lowercase				) + str(lowercase				)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  str(lowercase				).strip()
		if not number:
				raise ValueError('No input value was provided'				)
		UpperCamelCase						=  '-' if number.startswith('-'				) else ''
		UpperCamelCase						=  number.lstrip('-'				)
		if not number.isnumeric():
				raise ValueError('Input value is not an integer'				)
		return f'''{negative}0b{binary_recursive(int(lowercase				)				)}'''


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

					testmod()
