






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()
a__       =     logging.get_logger(__name__)

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

def _UpperCAmelCase   (	):
							snake_case__	  =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case__	  =       Image.open(requests.get(a  ,					stream=a ).raw )
							return im

def _UpperCAmelCase   (	a						:	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 _UpperCAmelCase   (	a						:	Union[str, Any]  ,					a						:	Optional[Any]  ,					a						:	Union[str, Any] ):
							snake_case__	  =       dct.pop(a )
							snake_case__	  =       val

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

@torch.no_grad()
def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	int  ,					a						:	Optional[int] ):
							snake_case__	  =       SwiftFormerConfig()

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

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

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

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

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

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https""" ):
																					snake_case__	  =       torch.hub.load_state_dict_from_url(a  ,					map_location="""cpu"""  ,					check_hash=a )
														else:
																					snake_case__	  =       torch.load(a  ,					map_location="""cpu""" )
							snake_case__	  =       checkpoint

							snake_case__	  =       create_rename_keys(a )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(a  ,					a  ,					a )

							# load HuggingFace model
							snake_case__	  =       SwiftFormerForImageClassification(a ).eval()
							hf_model.load_state_dict(a )

							# prepare test inputs
							snake_case__	  =       prepare_img()
							snake_case__	  =       ViTImageProcessor.from_pretrained("""preprocessor_config""" )
							snake_case__	  =       processor(images=a  ,					return_tensors="""pt""" )

							# compare outputs from both models
							snake_case__	  =       get_expected_output(a )
							snake_case__	  =       hf_model(inputs["""pixel_values"""] ).logits

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

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








import random
from typing import Any

def _UpperCAmelCase   (	a						:	list ):
							for _ in range(len(a ) ):
														snake_case__	  =       random.randint(0  ,					len(a ) - 1 )
														snake_case__	  =       random.randint(0  ,					len(a ) - 1 )
														snake_case__  , snake_case__	  =       data[b], data[a]
							return data


if __name__ == "__main__":
	a__       =     [0, 1, 2, 3, 4, 5, 6, 7]
	a__       =     ["""python""", """says""", """hello""", """!"""]
	print("""Fisher-Yates Shuffle:""")
	print("""List""", integers, strings)
	print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
