import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_      =      logging.get_logger(__name__)






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=False       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		OrderedDict()
			for key, value in state_dict.items():
						if encoder_only and not key.startswith('''head'''       ):
									lowercase							=		'''segformer.encoder.''' + key
						if key.startswith('''backbone'''       ):
									lowercase							=		key.replace('''backbone''',		'''segformer.encoder'''       )
						if "patch_embed" in key:
									# replace for example patch_embed1 by patch_embeddings.0
									lowercase							=		key[key.find('''patch_embed'''       ) + len('''patch_embed'''       )]
									lowercase							=		key.replace(f'patch_embed{idx}',		f'patch_embeddings.{int(UpperCAmelCase       )-1}'       )
						if "norm" in key:
									lowercase							=		key.replace('''norm''',		'''layer_norm'''       )
						if "segformer.encoder.layer_norm" in key:
									# replace for example layer_norm1 by layer_norm.0
									lowercase							=		key[key.find('''segformer.encoder.layer_norm'''       ) + len('''segformer.encoder.layer_norm'''       )]
									lowercase							=		key.replace(f'layer_norm{idx}',		f'layer_norm.{int(UpperCAmelCase       )-1}'       )
						if "layer_norm1" in key:
									lowercase							=		key.replace('''layer_norm1''',		'''layer_norm_1'''       )
						if "layer_norm2" in key:
									lowercase							=		key.replace('''layer_norm2''',		'''layer_norm_2'''       )
						if "block" in key:
									# replace for example block1 by block.0
									lowercase							=		key[key.find('''block'''       ) + len('''block'''       )]
									lowercase							=		key.replace(f'block{idx}',		f'block.{int(UpperCAmelCase       )-1}'       )
						if "attn.q" in key:
									lowercase							=		key.replace('''attn.q''',		'''attention.self.query'''       )
						if "attn.proj" in key:
									lowercase							=		key.replace('''attn.proj''',		'''attention.output.dense'''       )
						if "attn" in key:
									lowercase							=		key.replace('''attn''',		'''attention.self'''       )
						if "fc1" in key:
									lowercase							=		key.replace('''fc1''',		'''dense1'''       )
						if "fc2" in key:
									lowercase							=		key.replace('''fc2''',		'''dense2'''       )
						if "linear_pred" in key:
									lowercase							=		key.replace('''linear_pred''',		'''classifier'''       )
						if "linear_fuse" in key:
									lowercase							=		key.replace('''linear_fuse.conv''',		'''linear_fuse'''       )
									lowercase							=		key.replace('''linear_fuse.bn''',		'''batch_norm'''       )
						if "linear_c" in key:
									# replace for example linear_c4 by linear_c.3
									lowercase							=		key[key.find('''linear_c'''       ) + len('''linear_c'''       )]
									lowercase							=		key.replace(f'linear_c{idx}',		f'linear_c.{int(UpperCAmelCase       )-1}'       )
						if key.startswith('''head'''       ):
									lowercase							=		key.replace('''head''',		'''classifier'''       )
						lowercase							=		value

			return new_state_dict






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			for i in range(config.num_encoder_blocks       ):
						for j in range(config.depths[i]       ):
									# read in weights + bias of keys and values (which is a single matrix in the original implementation)
									lowercase							=		state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'       )
									lowercase							=		state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'       )
									# next, add keys and values (in that order) to the state dict
									lowercase							=		kv_weight[
									    : config.hidden_sizes[i], :
									]
									lowercase							=		kv_bias[: config.hidden_sizes[i]]
									lowercase							=		kv_weight[
									    config.hidden_sizes[i] :, :
									]
									lowercase							=		kv_bias[
									    config.hidden_sizes[i] :
									]






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







			lowercase							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowercase							=		Image.open(requests.get(UpperCAmelCase,		stream=UpperCAmelCase       ).raw       )

			return image






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			lowercase							=		SegformerConfig()
			lowercase							=		False

			# set attributes based on model_name
			lowercase							=		'''huggingface/label-files'''
			if "segformer" in model_name:
						lowercase							=		model_name[len('''segformer.'''       ) : len('''segformer.'''       ) + 2]
						if "ade" in model_name:
									lowercase							=		150
									lowercase							=		'''ade20k-id2label.json'''
									lowercase							=		(1, 150, 128, 128)
						elif "city" in model_name:
									lowercase							=		19
									lowercase							=		'''cityscapes-id2label.json'''
									lowercase							=		(1, 19, 128, 128)
						else:
									raise ValueError(f'Model {model_name} not supported'       )
			elif "mit" in model_name:
						lowercase							=		True
						lowercase							=		model_name[4:6]
						lowercase							=		1000
						lowercase							=		'''imagenet-1k-id2label.json'''
						lowercase							=		(1, 1000)
			else:
						raise ValueError(f'Model {model_name} not supported'       )

			# set config attributes
			lowercase							=		json.load(open(hf_hub_download(UpperCAmelCase,		UpperCAmelCase,		repo_type='''dataset'''       ),		'''r'''       )       )
			lowercase							=		{int(UpperCAmelCase       ): v for k, v in idalabel.items()}
			lowercase							=		idalabel
			lowercase							=		{v: k for k, v in idalabel.items()}
			if size == "b0":
						pass
			elif size == "b1":
						lowercase							=		[64, 128, 320, 512]
						lowercase							=		256
			elif size == "b2":
						lowercase							=		[64, 128, 320, 512]
						lowercase							=		768
						lowercase							=		[3, 4, 6, 3]
			elif size == "b3":
						lowercase							=		[64, 128, 320, 512]
						lowercase							=		768
						lowercase							=		[3, 4, 18, 3]
			elif size == "b4":
						lowercase							=		[64, 128, 320, 512]
						lowercase							=		768
						lowercase							=		[3, 8, 27, 3]
			elif size == "b5":
						lowercase							=		[64, 128, 320, 512]
						lowercase							=		768
						lowercase							=		[3, 6, 40, 3]
			else:
						raise ValueError(f'Size {size} not supported'       )

			# load image processor (only resize + normalize)
			lowercase							=		SegformerImageProcessor(
			    image_scale=(512, 512),		keep_ratio=UpperCAmelCase,		align=UpperCAmelCase,		do_random_crop=UpperCAmelCase       )

			# prepare image
			lowercase							=		prepare_img()
			lowercase							=		image_processor(images=UpperCAmelCase,		return_tensors='''pt'''       ).pixel_values

			logger.info(f'Converting model {model_name}...'       )

			# load original state dict
			if encoder_only:
						lowercase							=		torch.load(UpperCAmelCase,		map_location=torch.device('''cpu'''       )       )
			else:
						lowercase							=		torch.load(UpperCAmelCase,		map_location=torch.device('''cpu'''       )       )['''state_dict''']

			# rename keys
			lowercase							=		rename_keys(UpperCAmelCase,		encoder_only=UpperCAmelCase       )
			if not encoder_only:
						del state_dict["decode_head.conv_seg.weight"]
						del state_dict["decode_head.conv_seg.bias"]

			# key and value matrices need special treatment
			read_in_k_v(UpperCAmelCase,		UpperCAmelCase       )

			# create HuggingFace model and load state dict
			if encoder_only:
						lowercase							=		False
						lowercase							=		SegformerForImageClassification(UpperCAmelCase       )
			else:
						lowercase							=		SegformerForSemanticSegmentation(UpperCAmelCase       )
			model.load_state_dict(UpperCAmelCase       )
			model.eval()

			# forward pass
			lowercase							=		model(UpperCAmelCase       )
			lowercase							=		outputs.logits

			# set expected_slice based on model name
			# ADE20k checkpoints
			if model_name == "segformer.b0.512x512.ade.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
						        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
						        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
						    ]       )
			elif model_name == "segformer.b1.512x512.ade.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
						        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
						        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
						    ]       )
			elif model_name == "segformer.b2.512x512.ade.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
						        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
						        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
						    ]       )
			elif model_name == "segformer.b3.512x512.ade.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
						        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
						        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
						    ]       )
			elif model_name == "segformer.b4.512x512.ade.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
						        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
						        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
						    ]       )
			elif model_name == "segformer.b5.640x640.ade.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
						        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
						        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
						    ]       )
			# Cityscapes checkpoints
			elif model_name == "segformer.b0.1024x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
						        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
						        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
						    ]       )
			elif model_name == "segformer.b0.512x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
						        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
						        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
						    ]       )
			elif model_name == "segformer.b0.640x1280.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [
						            [-1.1372e01, -1.2787e01, -1.3477e01],
						            [-1.2536e01, -1.4194e01, -1.4409e01],
						            [-1.3217e01, -1.4888e01, -1.5327e01],
						        ],
						        [
						            [-1.4791e01, -1.7122e01, -1.8277e01],
						            [-1.7163e01, -1.9192e01, -1.9533e01],
						            [-1.7897e01, -1.9991e01, -2.0315e01],
						        ],
						        [
						            [7.6723e-01, 4.1921e-01, -7.7878e-02],
						            [4.7772e-01, 9.5557e-03, -2.8082e-01],
						            [3.6032e-01, -2.4826e-01, -5.1168e-01],
						        ],
						    ]       )
			elif model_name == "segformer.b0.768x768.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
						        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
						        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
						    ]       )
			elif model_name == "segformer.b1.1024x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
						        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
						        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
						    ]       )
			elif model_name == "segformer.b2.1024x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
						        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
						        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
						    ]       )
			elif model_name == "segformer.b3.1024x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
						        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
						        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
						    ]       )
			elif model_name == "segformer.b4.1024x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
						        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
						        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
						    ]       )
			elif model_name == "segformer.b5.1024x1024.city.160k":
						lowercase							=		torch.tensor(
						    [
						        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
						        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
						        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
						    ]       )
			else:
						lowercase							=		logits.argmax(-1       ).item()
						print('''Predicted class:''',		model.config.idalabel[predicted_class_idx]       )

			# verify logits
			if not encoder_only:
						assert logits.shape == expected_shape
						assert torch.allclose(logits[0, :3, :3, :3],		UpperCAmelCase,		atol=1e-2       )

			# finally, save model and image processor
			logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'       )
			Path(UpperCAmelCase       ).mkdir(exist_ok=UpperCAmelCase       )
			model.save_pretrained(UpperCAmelCase       )
			image_processor.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
						A_      =      argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name",
						    default="segformer.b0.512x512.ade.160k",
						    type=str,
						    help="Name of the model you'd like to convert.",
						)
						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
						)
						A_      =      parser.parse_args()
						convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt






def        __UpperCAmelCase    (       UpperCAmelCase       )->       bool:



			"""simple docstring"""







			lowercase							=		int(number**0.5       )
			return number == sq * sq






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       tuple[int, int]:



			"""simple docstring"""







			lowercase							=		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
			lowercase							=		x_den * y_den * z_den
			lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
			top //= hcf
			bottom //= hcf
			return top, bottom






def        __UpperCAmelCase    (       UpperCAmelCase = 35       )->       int:



			"""simple docstring"""







			lowercase							=		set()
			lowercase							=		42
			lowercase							=		Fraction(0       )
			lowercase							=		42

			for x_num in range(1,		order + 1       ):
						for x_den in range(x_num + 1,		order + 1       ):
									for y_num in range(1,		order + 1       ):
												for y_den in range(y_num + 1,		order + 1       ):
															# n=1
															lowercase							=		x_num * y_den + x_den * y_num
															lowercase							=		x_den * y_den
															lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
															z_num //= hcf
															z_den //= hcf
															if 0 < z_num < z_den <= order:
																		lowercase							=		add_three(
																		    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																		unique_s.add(UpperCAmelCase       )

															# n=2
															lowercase							=		(
															    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
															)
															lowercase							=		x_den * x_den * y_den * y_den
															if is_sq(UpperCAmelCase       ) and is_sq(UpperCAmelCase       ):
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
																		z_num //= hcf
																		z_den //= hcf
																		if 0 < z_num < z_den <= order:
																					lowercase							=		add_three(
																					    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																					unique_s.add(UpperCAmelCase       )

                    # n=-1
															lowercase							=		x_num * y_num
															lowercase							=		x_den * y_num + x_num * y_den
															lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
															z_num //= hcf
															z_den //= hcf
															if 0 < z_num < z_den <= order:
																		lowercase							=		add_three(
																		    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																		unique_s.add(UpperCAmelCase       )

															# n=2
															lowercase							=		x_num * x_num * y_num * y_num
															lowercase							=		(
															    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
															)
															if is_sq(UpperCAmelCase       ) and is_sq(UpperCAmelCase       ):
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		int(sqrt(UpperCAmelCase       )       )
																		lowercase							=		gcd(UpperCAmelCase,		UpperCAmelCase       )
																		z_num //= hcf
																		z_den //= hcf
																		if 0 < z_num < z_den <= order:
																					lowercase							=		add_three(
																					    UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
																					unique_s.add(UpperCAmelCase       )

			for num, den in unique_s:
						total += Fraction(UpperCAmelCase,		UpperCAmelCase       )

			return total.denominator + total.numerator


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





