


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 							__lowercase       (			__lowerCAmelCase							:    str       ):
							a__					=					[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 1_8, 2]
							a__					=					True if 'large' in model_name or 'huge' in model_name else False
							a__					=					True if 'large' in model_name or 'huge' in model_name else False
							a__					=					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:
																					a__					=					[3, 3, 3, 3]
																					a__					=					[5, 5, 5, 5]
														elif "fl4" in model_name:
																					a__					=					[4, 4, 4, 4]
																					a__					=					[3, 3, 3, 3]

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

							if "tiny" in model_name:
														a__					=					9_6
							elif "small" in model_name:
														a__					=					9_6
							elif "base" in model_name:
														a__					=					1_2_8
							elif "large" in model_name:
														a__					=					1_9_2
							elif "xlarge" in model_name:
														a__					=					2_5_6
							elif "huge" in model_name:
														a__					=					3_5_2

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

							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					{v: k for k, v in idalabel.items()}

							a__					=					FocalNetConfig(
							    embed_dim=__lowerCAmelCase ,   depths=__lowerCAmelCase ,   focal_levels=__lowerCAmelCase ,   focal_windows=__lowerCAmelCase ,   use_conv_embed=__lowerCAmelCase ,   idalabel=__lowerCAmelCase ,   labelaid=__lowerCAmelCase ,   use_post_layernorm=__lowerCAmelCase ,   use_layerscale=__lowerCAmelCase ,   )

							return config






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int]       ):
							if "patch_embed.proj" in name:
														a__					=					name.replace('patch_embed.proj' ,   'embeddings.patch_embeddings.projection'       )
							if "patch_embed.norm" in name:
														a__					=					name.replace('patch_embed.norm' ,   'embeddings.norm'       )
							if "layers" in name:
														a__					=					'encoder.' + name
							if "encoder.layers" in name:
														a__					=					name.replace('encoder.layers' ,   'encoder.stages'       )
							if "downsample.proj" in name:
														a__					=					name.replace('downsample.proj' ,   'downsample.projection'       )
							if "blocks" in name:
														a__					=					name.replace('blocks' ,   'layers'       )
							if "modulation.f.weight" in name or "modulation.f.bias" in name:
														a__					=					name.replace('modulation.f' ,   'modulation.projection_in'       )
							if "modulation.h.weight" in name or "modulation.h.bias" in name:
														a__					=					name.replace('modulation.h' ,   'modulation.projection_context'       )
							if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
														a__					=					name.replace('modulation.proj' ,   'modulation.projection_out'       )

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

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

							return name






def 							__lowercase       (			__lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict=False       ):
							# fmt: off
							a__					=					{
							    '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

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

							# rename keys
							for key in state_dict.copy().keys():
														a__					=					state_dict.pop(__lowerCAmelCase       )
														a__					=					val

							a__					=					get_focalnet_config(__lowerCAmelCase       )
							a__					=					FocalNetForImageClassification(__lowerCAmelCase       )
							model.eval()

							# load state dict
							model.load_state_dict(__lowerCAmelCase       )

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

							a__					=					BitImageProcessor(
							    do_resize=__lowerCAmelCase ,   size={'shortest_edge': 2_5_6} ,   resample=PILImageResampling.BILINEAR ,   do_center_crop=__lowerCAmelCase ,   crop_size=2_2_4 ,   do_normalize=__lowerCAmelCase ,   image_mean=__lowerCAmelCase ,   image_std=__lowerCAmelCase ,   )
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							a__					=					transforms.Compose(
							    [
							        transforms.Resize(2_5_6       ),
							        transforms.CenterCrop(2_2_4       ),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.485, 0.456, 0.406] ,   std=[0.229, 0.224, 0.225]       ),
							    ]       )

							a__					=					image_transforms(__lowerCAmelCase       ).unsqueeze(0       )

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

							a__					=					model(**__lowerCAmelCase       )

							a__					=					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":
														a__					=					torch.tensor([0.2_166, -0.4_368, 0.2_191]       )
							elif model_name == "focalnet-tiny-lrf":
														a__					=					torch.tensor([1.1_669, 0.0_125, -0.1_695]       )
							elif model_name == "focalnet-small":
														a__					=					torch.tensor([0.4_917, -0.0_430, 0.1_341]       )
							elif model_name == "focalnet-small-lrf":
														a__					=					torch.tensor([-0.2_588, -0.5_342, -0.2_331]       )
							elif model_name == "focalnet-base":
														a__					=					torch.tensor([-0.1_655, -0.4_090, -0.1_730]       )
							elif model_name == "focalnet-base-lrf":
														a__					=					torch.tensor([0.5_306, -0.0_483, -0.3_928]       )
							assert torch.allclose(outputs.logits[0, :3] ,   __lowerCAmelCase ,   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(__lowerCAmelCase       )
														processor.save_pretrained(__lowerCAmelCase       )

							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__":
						snake_case							:	Dict					      =				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.''',
						)

						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from collections import defaultdict
from math import ceil, sqrt






def 							__lowercase       (			__lowerCAmelCase							:    int = 1_0_0_0_0_0_0 ,   __lowerCAmelCase							:    int = 1_0       ):
							a__					=					defaultdict(__lowerCAmelCase       )

							for outer_width in range(3 ,   (t_limit // 4) + 2       ):
														if outer_width * outer_width > t_limit:
																					a__					=					max(
																					    ceil(sqrt(outer_width * outer_width - t_limit       )       ) ,   1       )
														else:
																					a__					=					1

														hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

														for hole_width in range(__lowerCAmelCase ,   outer_width - 1 ,   2       ):
																					count[outer_width * outer_width - hole_width * hole_width] += 1

							return sum(1 for n in count.values() if 1 <= n <= 1_0       )


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




