



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

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

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

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					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			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				dct.pop(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				val

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

@torch.no_grad()
def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Any:
					SCREAMING_SNAKE_CASE							:     str						=				SwiftFormerConfig()

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

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

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

					elif swiftformer_name == "swiftformer_l1":
										SCREAMING_SNAKE_CASE							:     Tuple						=				[4, 3, 10, 5]
										SCREAMING_SNAKE_CASE							:     List[Any]						=				[48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										SCREAMING_SNAKE_CASE							:     List[str]						=				[4, 4, 12, 6]
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[64, 128, 320, 512]

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

					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				create_rename_keys(__lowerCAmelCase   )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )

					# load HuggingFace model
					SCREAMING_SNAKE_CASE							:     Tuple						=				SwiftFormerForImageClassification(__lowerCAmelCase   ).eval()
					hf_model.load_state_dict(__lowerCAmelCase   )

					# prepare test inputs
					SCREAMING_SNAKE_CASE							:     Any						=				prepare_img()
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				ViTImageProcessor.from_pretrained('preprocessor_config'   )
					SCREAMING_SNAKE_CASE							:     Dict						=				processor(images=__lowerCAmelCase      ,	return_tensors='pt'   )

					# compare outputs from both models
					SCREAMING_SNAKE_CASE							:     Dict						=				get_expected_output(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				hf_model(inputs['pixel_values']   ).logits

					assert hf_logits.shape == torch.Size([1, 1000]   )
					assert torch.allclose(hf_logits[0, 0:5]      ,	__lowerCAmelCase      ,	atol=1E-3   )

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


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    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    :		Optional[Any]		      =    parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()