



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()
snake_case    :						Optional[Any]  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase							,     __lowercase=False       )		->		List[Any]:







						"""simple docstring"""

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

						return new_state_dict
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		List[str]:







						"""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)
																		A__	:							Any						    = state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'       )
																		A__	:							List[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
																		A__	:							Tuple						    = kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		A__	:							Tuple						    = kv_bias[: config.hidden_sizes[i]]
																		A__	:							Optional[Any]						    = kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		A__	:							str						    = kv_bias[
																		    config.hidden_sizes[i] :
																		]
def 	snake_case__       (					)		->		Union[str, Any]:







						"""simple docstring"""

						A__	:							str						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__	:							Union[str, Any]						    = Image.open(requests.get(__lowercase							,     stream=__lowercase       ).raw       )

						return image
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							Dict						    = SegformerConfig()
						A__	:							Optional[Any]						    = False

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

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

						# load image processor (only resize + normalize)
						A__	:							List[str]						    = SegformerImageProcessor(
						    image_scale=(5_1_2, 5_1_2)							,     keep_ratio=__lowercase							,     align=__lowercase							,     do_random_crop=__lowercase       )

						# prepare image
						A__	:							str						    = prepare_img()
						A__	:							str						    = image_processor(images=__lowercase							,     return_tensors="pt"       ).pixel_values

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

						# load original state dict
						if encoder_only:
												A__	:							Optional[int]						    = torch.load(__lowercase							,     map_location=torch.device("cpu"       )       )
						else:
												A__	:							int						    = torch.load(__lowercase							,     map_location=torch.device("cpu"       )       )["state_dict"]

						# rename keys
						A__	:							Optional[Any]						    = rename_keys(__lowercase							,     encoder_only=__lowercase       )
						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(__lowercase							,     __lowercase       )

						# create HuggingFace model and load state dict
						if encoder_only:
												A__	:							int						    = False
												A__	:							Optional[Any]						    = SegformerForImageClassification(__lowercase       )
						else:
												A__	:							Optional[int]						    = SegformerForSemanticSegmentation(__lowercase       )
						model.load_state_dict(__lowercase       )
						model.eval()

						# forward pass
						A__	:							Any						    = model(__lowercase       )
						A__	:							str						    = outputs.logits

						# set expected_slice based on model name
						# ADE20k checkpoints
						if model_name == "segformer.b0.512x512.ade.160k":
												A__	:							int						    = 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":
												A__	:							Any						    = 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":
												A__	:							Optional[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":
												A__	:							Tuple						    = 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":
												A__	:							List[str]						    = 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":
												A__	:							str						    = 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":
												A__	:							List[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":
												A__	:							int						    = 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":
												A__	:							str						    = torch.tensor(
												    [
												        [
												            [-1.13_72E01, -1.27_87E01, -1.34_77E01],
												            [-1.25_36E01, -1.41_94E01, -1.44_09E01],
												            [-1.32_17E01, -1.48_88E01, -1.53_27E01],
												        ],
												        [
												            [-1.47_91E01, -1.71_22E01, -1.82_77E01],
												            [-1.71_63E01, -1.91_92E01, -1.95_33E01],
												            [-1.78_97E01, -1.99_91E01, -2.03_15E01],
												        ],
												        [
												            [7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
												            [4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
												            [3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
												        ],
												    ]       )
						elif model_name == "segformer.b0.768x768.city.160k":
												A__	:							Tuple						    = 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":
												A__	:							Optional[int]						    = 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":
												A__	:							Optional[int]						    = 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":
												A__	:							str						    = 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":
												A__	:							Tuple						    = 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":
												A__	:							Union[str, Any]						    = 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:
												A__	:							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]							,     __lowercase							,     atol=1E-2       )

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


if __name__ == "__main__":
		snake_case    :						Dict  =						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.'
		)
		snake_case    :						List[Any]  =						parser.parse_args()
		convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




snake_case    :						Tuple  =						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def 	snake_case__       (					)		->		None:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = input("Enter message: "       )
						A__	:							Tuple						    = input("Enter key [alphanumeric]: "       )
						A__	:							Optional[Any]						    = input("Encrypt/Decrypt [e/d]: "       )

						if mode.lower().startswith("e"       ):
												A__	:							Tuple						    = "encrypt"
												A__	:							str						    = encrypt_message(__lowercase							,     __lowercase       )
						elif mode.lower().startswith("d"       ):
												A__	:							Optional[Any]						    = "decrypt"
												A__	:							Union[str, Any]						    = decrypt_message(__lowercase							,     __lowercase       )

						print(F'\n{mode.title()}ed message:'       )
						print(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "encrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "decrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Dict						    = []
						A__	:							Union[str, Any]						    = 0
						A__	:							List[Any]						    = key.upper()

						for symbol in message:
												A__	:							List[str]						    = LETTERS.find(symbol.upper()       )
												if num != -1:
																		if mode == "encrypt":
																								num += LETTERS.find(key[key_index]       )
																		elif mode == "decrypt":
																								num -= LETTERS.find(key[key_index]       )

																		num %= len(__lowercase       )

																		if symbol.isupper():
																								translated.append(LETTERS[num]       )
																		elif symbol.islower():
																								translated.append(LETTERS[num].lower()       )

																		key_index += 1
																		if key_index == len(__lowercase       ):
																								A__	:							Optional[int]						    = 0
												else:
																		translated.append(__lowercase       )
						return "".join(__lowercase       )


if __name__ == "__main__":
		main()