


'''simple docstring'''
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   : Dict             =				logging.get_logger(__name__)

lowerCAmelCase   : int             =				torch.device("""cpu""")




def 	_A  (       )  ->				str:
	lowercase						:		Dict   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		Any   					=		Image.open(requests.get(A							,stream=A							).raw							)
	return im




def 	_A  (       A							)  ->				Optional[int]:
	if swiftformer_name == "swiftformer_xs":
		return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]							)

	elif swiftformer_name == "swiftformer_s":
		return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]							)

	elif swiftformer_name == "swiftformer_l1":
		return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]							)

	elif swiftformer_name == "swiftformer_l3":
		return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]							)




def 	_A  (       A							,A							,A							)  ->				List[Any]:
	lowercase						:		Tuple   					=		dct.pop(A							)
	lowercase						:		Dict   					=		val




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




@torch.no_grad()
def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		List[str]   					=		SwiftFormerConfig()

	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	lowercase						:		Dict   					=		1_0_0_0
	lowercase						:		Tuple   					=		"huggingface/label-files"
	lowercase						:		Dict   					=		"imagenet-1k-id2label.json"
	lowercase						:		Any   					=		json.load(open(hf_hub_download(A							,A							,repo_type="dataset"							)							,"r"							)							)
	lowercase						:		Any   					=		{int(A							): v for k, v in idalabel.items()}
	lowercase						:		Union[str, Any]   					=		idalabel
	lowercase						:		List[str]   					=		{v: k for k, v in idalabel.items()}

	# size of the architecture
	if swiftformer_name == "swiftformer_xs":
		lowercase						:		List[str]   					=		[3, 3, 6, 4]
		lowercase						:		Any   					=		[4_8, 5_6, 1_1_2, 2_2_0]

	elif swiftformer_name == "swiftformer_s":
		lowercase						:		List[str]   					=		[3, 3, 9, 6]
		lowercase						:		Dict   					=		[4_8, 6_4, 1_6_8, 2_2_4]

	elif swiftformer_name == "swiftformer_l1":
		lowercase						:		List[Any]   					=		[4, 3, 1_0, 5]
		lowercase						:		List[str]   					=		[4_8, 9_6, 1_9_2, 3_8_4]

	elif swiftformer_name == "swiftformer_l3":
		lowercase						:		List[Any]   					=		[4, 4, 1_2, 6]
		lowercase						:		int   					=		[6_4, 1_2_8, 3_2_0, 5_1_2]

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

	lowercase						:		List[str]   					=		create_rename_keys(A							)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(A							,A							,A							)

	# load HuggingFace model
	lowercase						:		str   					=		SwiftFormerForImageClassification(A							).eval()
	hf_model.load_state_dict(A							)

	# prepare test inputs
	lowercase						:		List[Any]   					=		prepare_img()
	lowercase						:		Union[str, Any]   					=		ViTImageProcessor.from_pretrained("preprocessor_config"							)
	lowercase						:		int   					=		processor(images=A							,return_tensors="pt"							)

	# compare outputs from both models
	lowercase						:		Optional[int]   					=		get_expected_output(A							)
	lowercase						:		Union[str, Any]   					=		hf_model(inputs["pixel_values"]							).logits

	assert hf_logits.shape == torch.Size([1, 1_0_0_0]							)
	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__":
					lowerCAmelCase   : Dict             =				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)






'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


