



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		       =      logging.get_logger(__name__)

lowerCamelCase		       =      torch.device("""cpu""")






def     SCREAMING_SNAKE_CASE(      )	->						Any:
				a__	:							List[str]      						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				a__	:							int      						= Image.open(requests.get(UpperCAmelCase_	,				stream=UpperCAmelCase_ ).raw )
				return im






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						int:
				a__	:							Optional[Any]      						= dct.pop(UpperCAmelCase_ )
				a__	:							List[str]      						= val






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Dict:
				a__	:							str      						= []
				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__	:							Any      						= k_new.replace(".dwconv"	,				".depth_wise_conv" )
								if ".Proj." in k:
												a__	:							List[Any]      						= k_new.replace(".Proj."	,				".proj." )
								if "patch_embed" in k_new:
												a__	:							Union[str, Any]      						= k_new.replace("patch_embed"	,				"swiftformer.patch_embed.patch_embedding" )
								if "network" in k_new:
												a__	:							Optional[Any]      						= k_new.split("." )
												if ls[2].isdigit():
																a__	:							int      						= 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:] )
												else:
																a__	:							List[Any]      						= k_new.replace("network"	,				"swiftformer.encoder.network" )
								rename_keys.append((k, k_new) )
				return rename_keys






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							Any      						= SwiftFormerConfig()

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

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

				elif swiftformer_name == "swiftformer_s":
								a__	:							Any      						= [3, 3, 9, 6]
								a__	:							Any      						= [48, 64, 1_68, 2_24]

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

				elif swiftformer_name == "swiftformer_l3":
								a__	:							Union[str, Any]      						= [4, 4, 12, 6]
								a__	:							Dict      						= [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" ):
												a__	:							int      						= torch.hub.load_state_dict_from_url(UpperCAmelCase_	,				map_location="cpu"	,				check_hash=UpperCAmelCase_ )
								else:
												a__	:							Tuple      						= torch.load(UpperCAmelCase_	,				map_location="cpu" )
				a__	:							Tuple      						= checkpoint

				a__	:							Any      						= create_rename_keys(UpperCAmelCase_ )
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(UpperCAmelCase_	,				UpperCAmelCase_	,				UpperCAmelCase_ )

				# load HuggingFace model
				a__	:							List[str]      						= SwiftFormerForImageClassification(UpperCAmelCase_ ).eval()
				hf_model.load_state_dict(UpperCAmelCase_ )

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

				# compare outputs from both models
				a__	:							List[Any]      						= get_expected_output(UpperCAmelCase_ )
				a__	:							Dict      						= hf_model(inputs["pixel_values"] ).logits

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






'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						n * (n + 1) * (2 * n + 1) / 6
     __lowerCamelCase  :      Union[str, Any]														=						(n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares				)


if __name__ == "__main__":
      print(f'''{solution() = }''')






