





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[Any]:
						UpperCAmelCase         =     [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
						UpperCAmelCase         =     True if '''large''' in model_name or '''huge''' in model_name else False
						UpperCAmelCase         =     True if '''large''' in model_name or '''huge''' in model_name else False
						UpperCAmelCase         =     True if '''large''' in model_name or '''huge''' in model_name else False

						if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
												if "fl3" in model_name:
																		UpperCAmelCase         =     [3, 3, 3, 3]
																		UpperCAmelCase         =     [5, 5, 5, 5]
												elif "fl4" in model_name:
																		UpperCAmelCase         =     [4, 4, 4, 4]
																		UpperCAmelCase         =     [3, 3, 3, 3]

						if "tiny" in model_name or "small" in model_name or "base" in model_name:
												UpperCAmelCase         =     [3, 3, 3, 3]
												if "lrf" in model_name:
																		UpperCAmelCase         =     [3, 3, 3, 3]
												else:
																		UpperCAmelCase         =     [2, 2, 2, 2]

						if "tiny" in model_name:
												UpperCAmelCase         =     96
						elif "small" in model_name:
												UpperCAmelCase         =     96
						elif "base" in model_name:
												UpperCAmelCase         =     128
						elif "large" in model_name:
												UpperCAmelCase         =     192
						elif "xlarge" in model_name:
												UpperCAmelCase         =     256
						elif "huge" in model_name:
												UpperCAmelCase         =     352

						# set label information
						UpperCAmelCase         =     '''huggingface/label-files'''
						if "large" in model_name or "huge" in model_name:
												UpperCAmelCase         =     '''imagenet-22k-id2label.json'''
						else:
												UpperCAmelCase         =     '''imagenet-1k-id2label.json'''

						UpperCAmelCase         =     json.load(open(hf_hub_download(_lowercase    ,   _lowercase    ,   repo_type="dataset"    )    ,   "r"    )    )
						UpperCAmelCase         =     {int(_lowercase    ): v for k, v in idalabel.items()}
						UpperCAmelCase         =     {v: k for k, v in idalabel.items()}

						UpperCAmelCase         =     FocalNetConfig(
						    embed_dim=_lowercase    ,   depths=_lowercase    ,   focal_levels=_lowercase    ,   focal_windows=_lowercase    ,   use_conv_embed=_lowercase    ,   idalabel=_lowercase    ,   labelaid=_lowercase    ,   use_post_layernorm=_lowercase    ,   use_layerscale=_lowercase    ,   )

						return config

def    lowerCamelCase_(lowerCamelCase_    ) ->       Any:
						if "patch_embed.proj" in name:
												UpperCAmelCase         =     name.replace("patch_embed.proj"    ,   "embeddings.patch_embeddings.projection"    )
						if "patch_embed.norm" in name:
												UpperCAmelCase         =     name.replace("patch_embed.norm"    ,   "embeddings.norm"    )
						if "layers" in name:
												UpperCAmelCase         =     '''encoder.''' + name
						if "encoder.layers" in name:
												UpperCAmelCase         =     name.replace("encoder.layers"    ,   "encoder.stages"    )
						if "downsample.proj" in name:
												UpperCAmelCase         =     name.replace("downsample.proj"    ,   "downsample.projection"    )
						if "blocks" in name:
												UpperCAmelCase         =     name.replace("blocks"    ,   "layers"    )
						if "modulation.f.weight" in name or "modulation.f.bias" in name:
												UpperCAmelCase         =     name.replace("modulation.f"    ,   "modulation.projection_in"    )
						if "modulation.h.weight" in name or "modulation.h.bias" in name:
												UpperCAmelCase         =     name.replace("modulation.h"    ,   "modulation.projection_context"    )
						if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
												UpperCAmelCase         =     name.replace("modulation.proj"    ,   "modulation.projection_out"    )

						if name == "norm.weight":
												UpperCAmelCase         =     '''layernorm.weight'''
						if name == "norm.bias":
												UpperCAmelCase         =     '''layernorm.bias'''

						if "head" in name:
												UpperCAmelCase         =     name.replace("head"    ,   "classifier"    )
						else:
												UpperCAmelCase         =     '''focalnet.''' + name

						return name

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=False    ) ->       Tuple:
						# fmt: off
						UpperCAmelCase         =     {
						    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
						    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
						    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
						    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
						    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
						    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
						    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
						    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
						    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
						    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
						}
						# fmt: on

						UpperCAmelCase         =     model_name_to_url[model_name]
						print("Checkpoint URL: "    ,   _lowercase    )
						UpperCAmelCase         =     torch.hub.load_state_dict_from_url(_lowercase    ,   map_location="cpu"    )['''model''']

						# rename keys
						for key in state_dict.copy().keys():
												UpperCAmelCase         =     state_dict.pop(_lowercase    )
												UpperCAmelCase         =     val

						UpperCAmelCase         =     get_focalnet_config(_lowercase    )
						UpperCAmelCase         =     FocalNetForImageClassification(_lowercase    )
						model.eval()

						# load state dict
						model.load_state_dict(_lowercase    )

						# verify conversion
						UpperCAmelCase         =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''

						UpperCAmelCase         =     BitImageProcessor(
						    do_resize=_lowercase    ,   size={"shortest_edge": 256}    ,   resample=PILImageResampling.BILINEAR    ,   do_center_crop=_lowercase    ,   crop_size=224    ,   do_normalize=_lowercase    ,   image_mean=_lowercase    ,   image_std=_lowercase    ,   )
						UpperCAmelCase         =     Image.open(requests.get(_lowercase    ,   stream=_lowercase    ).raw    )
						UpperCAmelCase         =     processor(images=_lowercase    ,   return_tensors="pt"    )

						UpperCAmelCase         =     transforms.Compose(
						    [
						        transforms.Resize(256    ),
						        transforms.CenterCrop(224    ),
						        transforms.ToTensor(),
						        transforms.Normalize(mean=[0.485, 0.456, 0.406]    ,   std=[0.229, 0.224, 0.225]    ),
						    ]    )

						UpperCAmelCase         =     image_transforms(_lowercase    ).unsqueeze(0    )

						# verify pixel_values
						assert torch.allclose(inputs.pixel_values    ,   _lowercase    ,   atol=1e-4    )

						UpperCAmelCase         =     model(**_lowercase    )

						UpperCAmelCase         =     outputs.logits.argmax(-1    ).item()
						print("Predicted class:"    ,   model.config.idalabel[predicted_class_idx]    )

						print("First values of logits:"    ,   outputs.logits[0, :3]    )

						if model_name == "focalnet-tiny":
												UpperCAmelCase         =     torch.tensor([0.2166, -0.4368, 0.2191]    )
						elif model_name == "focalnet-tiny-lrf":
												UpperCAmelCase         =     torch.tensor([1.1669, 0.0125, -0.1695]    )
						elif model_name == "focalnet-small":
												UpperCAmelCase         =     torch.tensor([0.4917, -0.0430, 0.1341]    )
						elif model_name == "focalnet-small-lrf":
												UpperCAmelCase         =     torch.tensor([-0.2588, -0.5342, -0.2331]    )
						elif model_name == "focalnet-base":
												UpperCAmelCase         =     torch.tensor([-0.1655, -0.4090, -0.1730]    )
						elif model_name == "focalnet-base-lrf":
												UpperCAmelCase         =     torch.tensor([0.5306, -0.0483, -0.3928]    )
						assert torch.allclose(outputs.logits[0, :3]    ,   _lowercase    ,   atol=1e-4    )
						print("Looks ok!"    )

						if pytorch_dump_folder_path is not None:
												print(F'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'    )
												model.save_pretrained(_lowercase    )
												processor.save_pretrained(_lowercase    )

						if push_to_hub:
												print(F'Pushing model and processor of {model_name} to the hub...'    )
												model.push_to_hub(F'{model_name}'    )
												processor.push_to_hub(F'{model_name}'    )


if __name__ == "__main__":
		__lowerCamelCase     :							List[Any]  							=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="focalnet-tiny",
		    type=str,
		    help="Name of the FocalNet model you\'d like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub",
		    action="store_true",
		    help="Whether to push the model and processor to the hub.",
		)

		__lowerCamelCase     :							Dict  							=				parser.parse_args()
		convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def    lowerCamelCase_(lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     len(lowerCamelCase_    )
						for i in range(lowerCamelCase_    ):
												for j in range(i + 1    ,   lowerCamelCase_    ):
																		if numbers[j] < numbers[i]:
																								UpperCAmelCase       ,		UpperCAmelCase         =     numbers[j], numbers[i]
						return numbers


if __name__ == "__main__":
		__lowerCamelCase     :							int  							=				input("Enter numbers separated by a comma:\n").strip()
		__lowerCamelCase     :							Dict  							=				[int(item) for item in user_input.split(",")]
		print(exchange_sort(unsorted))



