



import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

a_   :List[str]        =      3
def        lowercase_							(A : int      ):
       print('Generating primitive root of p'      )
       while True:
              snake_case__							:	Optional[int]   	=	random.randrange(3		,					A      )
              if pow(A		,					2		,					A      ) == 1:
                     continue
              if pow(A		,					A		,					A      ) == 1:
                     continue
              return g
def        lowercase_							(A : int      ):
       print('Generating prime p...'      )
       snake_case__							:	Dict   	=	rabin_miller.generate_large_prime(A      )  # select large prime number.
       snake_case__							:	Union[str, Any]   	=	primitive_root(A      )  # one primitive root on modulo p.
       snake_case__							:	Dict   	=	random.randrange(3		,					A      )  # private_key -> have to be greater than 2 for safety.
       snake_case__							:	Any   	=	cryptomath.find_mod_inverse(pow(A		,					A		,					A      )		,					A      )

       snake_case__							:	str   	=	(key_size, e_a, e_a, p)
       snake_case__							:	Union[str, Any]   	=	(key_size, d)

       return public_key, private_key
def        lowercase_							(A : str		,					A : int      ):
       if os.path.exists(F'''{name}_pubkey.txt'''      ) or os.path.exists(F'''{name}_privkey.txt'''      ):
              print('\nWARNING:'      )
              print(
                  F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
                  'Use a different name or delete these files and re-run this program.'      )
              sys.exit()

       snake_case__	,				snake_case__							:	List[Any]   	=	generate_key(A      )
       print(F'''\nWriting public key to file {name}_pubkey.txt...'''      )
       with open(F'''{name}_pubkey.txt'''		,					'w'      ) as fo:
              fo.write(F'''{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'''      )

       print(F'''Writing private key to file {name}_privkey.txt...'''      )
       with open(F'''{name}_privkey.txt'''		,					'w'      ) as fo:
              fo.write(F'''{private_key[0]},{private_key[1]}'''      )
def        lowercase_							():
       print('Making key files...'      )
       make_key_files('elgamal'		,					2_0_4_8      )
       print('Key files generation successful'      )


if __name__ == "__main__":
 main()











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_   :Dict        =      logging.get_logger(__name__)
def        lowercase_							(A : Optional[Any]		,					A : Any=False      ):
       snake_case__							:	List[Any]   	=	OrderedDict()
       for key, value in state_dict.items():
              if encoder_only and not key.startswith('head'      ):
                     snake_case__							:	str   	=	'segformer.encoder.' + key
              if key.startswith('backbone'      ):
                     snake_case__							:	str   	=	key.replace('backbone'		,					'segformer.encoder'      )
              if "patch_embed" in key:
                     # replace for example patch_embed1 by patch_embeddings.0
                     snake_case__							:	Optional[int]   	=	key[key.find('patch_embed'      ) + len('patch_embed'      )]
                     snake_case__							:	int   	=	key.replace(F'''patch_embed{idx}'''		,					F'''patch_embeddings.{int(A      )-1}'''      )
              if "norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('norm'		,					'layer_norm'      )
              if "segformer.encoder.layer_norm" in key:
                     # replace for example layer_norm1 by layer_norm.0
                     snake_case__							:	Tuple   	=	key[key.find('segformer.encoder.layer_norm'      ) + len('segformer.encoder.layer_norm'      )]
                     snake_case__							:	Union[str, Any]   	=	key.replace(F'''layer_norm{idx}'''		,					F'''layer_norm.{int(A      )-1}'''      )
              if "layer_norm1" in key:
                     snake_case__							:	List[Any]   	=	key.replace('layer_norm1'		,					'layer_norm_1'      )
              if "layer_norm2" in key:
                     snake_case__							:	List[Any]   	=	key.replace('layer_norm2'		,					'layer_norm_2'      )
              if "block" in key:
                     # replace for example block1 by block.0
                     snake_case__							:	List[Any]   	=	key[key.find('block'      ) + len('block'      )]
                     snake_case__							:	List[Any]   	=	key.replace(F'''block{idx}'''		,					F'''block.{int(A      )-1}'''      )
              if "attn.q" in key:
                     snake_case__							:	int   	=	key.replace('attn.q'		,					'attention.self.query'      )
              if "attn.proj" in key:
                     snake_case__							:	str   	=	key.replace('attn.proj'		,					'attention.output.dense'      )
              if "attn" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('attn'		,					'attention.self'      )
              if "fc1" in key:
                     snake_case__							:	str   	=	key.replace('fc1'		,					'dense1'      )
              if "fc2" in key:
                     snake_case__							:	Dict   	=	key.replace('fc2'		,					'dense2'      )
              if "linear_pred" in key:
                     snake_case__							:	Union[str, Any]   	=	key.replace('linear_pred'		,					'classifier'      )
              if "linear_fuse" in key:
                     snake_case__							:	List[str]   	=	key.replace('linear_fuse.conv'		,					'linear_fuse'      )
                     snake_case__							:	List[Any]   	=	key.replace('linear_fuse.bn'		,					'batch_norm'      )
              if "linear_c" in key:
                     # replace for example linear_c4 by linear_c.3
                     snake_case__							:	Optional[int]   	=	key[key.find('linear_c'      ) + len('linear_c'      )]
                     snake_case__							:	Tuple   	=	key.replace(F'''linear_c{idx}'''		,					F'''linear_c.{int(A      )-1}'''      )
              if key.startswith('head'      ):
                     snake_case__							:	Tuple   	=	key.replace('head'		,					'classifier'      )
              snake_case__							:	Optional[int]   	=	value

       return new_state_dict
def        lowercase_							(A : Tuple		,					A : Optional[int]      ):
       # for each of the encoder blocks:
       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)
                     snake_case__							:	List[str]   	=	state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''      )
                     snake_case__							:	Optional[Any]   	=	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
                     snake_case__							:	str   	=	kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     snake_case__							:	Dict   	=	kv_bias[: config.hidden_sizes[i]]
                     snake_case__							:	List[str]   	=	kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     snake_case__							:	List[Any]   	=	kv_bias[
                         config.hidden_sizes[i] :
                     ]
def        lowercase_							():
       snake_case__							:	Union[str, Any]   	=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       snake_case__							:	Dict   	=	Image.open(requests.get(A		,					stream=A      ).raw      )

       return image
@torch.no_grad()
def        lowercase_							(A : Any		,					A : Union[str, Any]		,					A : Optional[Any]      ):
       snake_case__							:	List[str]   	=	SegformerConfig()
       snake_case__							:	Dict   	=	False

       # set attributes based on model_name
       snake_case__							:	Optional[int]   	=	'huggingface/label-files'
       if "segformer" in model_name:
              snake_case__							:	str   	=	model_name[len('segformer.'      ) : len('segformer.'      ) + 2]
              if "ade" in model_name:
                     snake_case__							:	Optional[int]   	=	1_5_0
                     snake_case__							:	int   	=	'ade20k-id2label.json'
                     snake_case__							:	List[Any]   	=	(1, 1_5_0, 1_2_8, 1_2_8)
              elif "city" in model_name:
                     snake_case__							:	str   	=	1_9
                     snake_case__							:	List[str]   	=	'cityscapes-id2label.json'
                     snake_case__							:	Optional[Any]   	=	(1, 1_9, 1_2_8, 1_2_8)
              else:
                     raise ValueError(F'''Model {model_name} not supported'''      )
       elif "mit" in model_name:
              snake_case__							:	str   	=	True
              snake_case__							:	Union[str, Any]   	=	model_name[4:6]
              snake_case__							:	Optional[Any]   	=	1_0_0_0
              snake_case__							:	Optional[int]   	=	'imagenet-1k-id2label.json'
              snake_case__							:	List[Any]   	=	(1, 1_0_0_0)
       else:
              raise ValueError(F'''Model {model_name} not supported'''      )

       # set config attributes
       snake_case__							:	str   	=	json.load(open(hf_hub_download(A		,					A		,					repo_type='dataset'      )		,					'r'      )      )
       snake_case__							:	List[Any]   	=	{int(A      ): v for k, v in idalabel.items()}
       snake_case__							:	Union[str, Any]   	=	idalabel
       snake_case__							:	Tuple   	=	{v: k for k, v in idalabel.items()}
       if size == "b0":
              pass
       elif size == "b1":
              snake_case__							:	List[Any]   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	Tuple   	=	2_5_6
       elif size == "b2":
              snake_case__							:	List[str]   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	int   	=	7_6_8
              snake_case__							:	List[Any]   	=	[3, 4, 6, 3]
       elif size == "b3":
              snake_case__							:	Optional[Any]   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	int   	=	7_6_8
              snake_case__							:	Optional[Any]   	=	[3, 4, 1_8, 3]
       elif size == "b4":
              snake_case__							:	str   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	Optional[Any]   	=	7_6_8
              snake_case__							:	Union[str, Any]   	=	[3, 8, 2_7, 3]
       elif size == "b5":
              snake_case__							:	List[str]   	=	[6_4, 1_2_8, 3_2_0, 5_1_2]
              snake_case__							:	Optional[Any]   	=	7_6_8
              snake_case__							:	Any   	=	[3, 6, 4_0, 3]
       else:
              raise ValueError(F'''Size {size} not supported'''      )

       # load image processor (only resize + normalize)
       snake_case__							:	Dict   	=	SegformerImageProcessor(
           image_scale=(5_1_2, 5_1_2)		,					keep_ratio=A		,					align=A		,					do_random_crop=A      )

       # prepare image
       snake_case__							:	List[str]   	=	prepare_img()
       snake_case__							:	Dict   	=	image_processor(images=A		,					return_tensors='pt'      ).pixel_values

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

       # load original state dict
       if encoder_only:
              snake_case__							:	Tuple   	=	torch.load(A		,					map_location=torch.device('cpu'      )      )
       else:
              snake_case__							:	int   	=	torch.load(A		,					map_location=torch.device('cpu'      )      )['state_dict']

       # rename keys
       snake_case__							:	List[Any]   	=	rename_keys(A		,					encoder_only=A      )
       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(A		,					A      )

       # create HuggingFace model and load state dict
       if encoder_only:
              snake_case__							:	str   	=	False
              snake_case__							:	List[Any]   	=	SegformerForImageClassification(A      )
       else:
              snake_case__							:	Dict   	=	SegformerForSemanticSegmentation(A      )
       model.load_state_dict(A      )
       model.eval()

       # forward pass
       snake_case__							:	int   	=	model(A      )
       snake_case__							:	Any   	=	outputs.logits

       # set expected_slice based on model name
       # ADE20k checkpoints
       if model_name == "segformer.b0.512x512.ade.160k":
              snake_case__							:	Dict   	=	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":
              snake_case__							:	Optional[int]   	=	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":
              snake_case__							:	List[Any]   	=	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":
              snake_case__							:	Union[str, Any]   	=	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":
              snake_case__							:	Dict   	=	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":
              snake_case__							:	List[Any]   	=	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":
              snake_case__							:	str   	=	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":
              snake_case__							:	Tuple   	=	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":
              snake_case__							:	Any   	=	torch.tensor(
                  [
                      [
                          [-1.1_372e01, -1.2_787e01, -1.3_477e01],
                          [-1.2_536e01, -1.4_194e01, -1.4_409e01],
                          [-1.3_217e01, -1.4_888e01, -1.5_327e01],
                      ],
                      [
                          [-1.4_791e01, -1.7_122e01, -1.8_277e01],
                          [-1.7_163e01, -1.9_192e01, -1.9_533e01],
                          [-1.7_897e01, -1.9_991e01, -2.0_315e01],
                      ],
                      [
                          [7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
                          [4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
                          [3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
                      ],
                  ]      )
       elif model_name == "segformer.b0.768x768.city.160k":
              snake_case__							:	Optional[int]   	=	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":
              snake_case__							:	Union[str, Any]   	=	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":
              snake_case__							:	List[str]   	=	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":
              snake_case__							:	List[Any]   	=	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":
              snake_case__							:	str   	=	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":
              snake_case__							:	List[str]   	=	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:
              snake_case__							:	Tuple   	=	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]		,					A		,					atol=1e-2      )

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


if __name__ == "__main__":
 a_   :Optional[int]        =      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_   :Union[str, Any]        =      parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






