
"""simple docstring"""




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()
lowerCAmelCase__							=				logging.get_logger(__name__)



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

		return new_state_dict



def        _lowerCamelCase ( __a,							__a    ):
		# 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)
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'    )
						SCREAMING_SNAKE_CASE_						 = 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
						SCREAMING_SNAKE_CASE_						 = kv_weight[
						    : config.hidden_sizes[i], :
						]
						SCREAMING_SNAKE_CASE_						 = kv_bias[: config.hidden_sizes[i]]
						SCREAMING_SNAKE_CASE_						 = kv_weight[
						    config.hidden_sizes[i] :, :
						]
						SCREAMING_SNAKE_CASE_						 = kv_bias[
						    config.hidden_sizes[i] :
						]



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )

		return image



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = SegformerConfig()
		SCREAMING_SNAKE_CASE_						 = False

		# set attributes based on model_name
		SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
		if "segformer" in model_name:
				SCREAMING_SNAKE_CASE_						 = model_name[len('''segformer.'''    ) : len('''segformer.'''    ) + 2]
				if "ade" in model_name:
						SCREAMING_SNAKE_CASE_						 = 150
						SCREAMING_SNAKE_CASE_						 = '''ade20k-id2label.json'''
						SCREAMING_SNAKE_CASE_						 = (1, 150, 128, 128)
				elif "city" in model_name:
						SCREAMING_SNAKE_CASE_						 = 19
						SCREAMING_SNAKE_CASE_						 = '''cityscapes-id2label.json'''
						SCREAMING_SNAKE_CASE_						 = (1, 19, 128, 128)
				else:
						raise ValueError(F'Model {model_name} not supported'    )
		elif "mit" in model_name:
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = model_name[4:6]
				SCREAMING_SNAKE_CASE_						 = 1_000
				SCREAMING_SNAKE_CASE_						 = '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE_						 = (1, 1_000)
		else:
				raise ValueError(F'Model {model_name} not supported'    )

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

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

		# prepare image
		SCREAMING_SNAKE_CASE_						 = prepare_img()
		SCREAMING_SNAKE_CASE_						 = image_processor(images=__a,							return_tensors='''pt'''    ).pixel_values

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

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

		# rename keys
		SCREAMING_SNAKE_CASE_						 = 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:
				SCREAMING_SNAKE_CASE_						 = False
				SCREAMING_SNAKE_CASE_						 = SegformerForImageClassification(__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = SegformerForSemanticSegmentation(__a    )
		model.load_state_dict(__a    )
		model.eval()

		# forward pass
		SCREAMING_SNAKE_CASE_						 = model(__a    )
		SCREAMING_SNAKE_CASE_						 = outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
				        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
				        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
				    ]    )
		elif model_name == "segformer.b1.512x512.ade.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
				        [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
				        [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
				    ]    )
		elif model_name == "segformer.b2.512x512.ade.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
				        [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
				        [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
				    ]    )
		elif model_name == "segformer.b3.512x512.ade.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
				        [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
				        [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
				    ]    )
		elif model_name == "segformer.b4.512x512.ade.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
				        [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
				        [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
				    ]    )
		elif model_name == "segformer.b5.640x640.ade.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
				        [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
				        [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
				    ]    )
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
				        [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
				        [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
				    ]    )
		elif model_name == "segformer.b0.512x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
				        [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
				        [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
				    ]    )
		elif model_name == "segformer.b0.640x1280.city.160k":
				SCREAMING_SNAKE_CASE_						 = 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":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
				        [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
				        [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
				    ]    )
		elif model_name == "segformer.b1.1024x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
				        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
				        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
				    ]    )
		elif model_name == "segformer.b2.1024x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
				        [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
				        [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
				    ]    )
		elif model_name == "segformer.b3.1024x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
				        [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
				        [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
				    ]    )
		elif model_name == "segformer.b4.1024x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
				        [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
				        [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
				    ]    )
		elif model_name == "segformer.b5.1024x1024.city.160k":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [
				        [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
				        [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
				        [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
				    ]    )
		else:
				SCREAMING_SNAKE_CASE_						 = 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__":
						lowerCAmelCase__							=				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.'
						)
						lowerCAmelCase__							=				parser.parse_args()
						convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""




import cva
import numpy as np





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if k in (0.04, 0.06):
											SCREAMING_SNAKE_CASE_						 = k
											SCREAMING_SNAKE_CASE_						 = window_size
									else:
											raise ValueError('''invalid k value'''    )

							def __str__(self    ):

									"""simple docstring"""







									return str(self.k    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = cva.imread(SCREAMING_SNAKE_CASE_       ,       0    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = img.shape
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = img.copy()
									SCREAMING_SNAKE_CASE_						 = cva.cvtColor(SCREAMING_SNAKE_CASE_       ,       cva.COLOR_GRAY2RGB    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = np.gradient(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = dx**2
									SCREAMING_SNAKE_CASE_						 = dy**2
									SCREAMING_SNAKE_CASE_						 = dx * dy
									SCREAMING_SNAKE_CASE_						 = 0.04
									SCREAMING_SNAKE_CASE_						 = self.window_size // 2
									for y in range(SCREAMING_SNAKE_CASE_       ,       h - offset    ):
											for x in range(SCREAMING_SNAKE_CASE_       ,       w - offset    ):
													SCREAMING_SNAKE_CASE_						 = ixx[
													    y - offset : y + offset + 1, x - offset : x + offset + 1
													].sum()
													SCREAMING_SNAKE_CASE_						 = iyy[
													    y - offset : y + offset + 1, x - offset : x + offset + 1
													].sum()
													SCREAMING_SNAKE_CASE_						 = ixy[
													    y - offset : y + offset + 1, x - offset : x + offset + 1
													].sum()

													SCREAMING_SNAKE_CASE_						 = (wxx * wyy) - (wxy**2)
													SCREAMING_SNAKE_CASE_						 = wxx + wyy
													SCREAMING_SNAKE_CASE_						 = det - k * (trace**2)
													# Can change the value
													if r > 0.5:
															corner_list.append([x, y, r]    )
															color_img.itemset((y, x, 0)       ,       0    )
															color_img.itemset((y, x, 1)       ,       0    )
															color_img.itemset((y, x, 2)       ,       2_55    )
									return color_img, corner_list


if __name__ == "__main__":
						lowerCAmelCase__							=				HarrisCorner(0.04, 3)
						lowerCAmelCase__,  lowerCAmelCase__							=				edge_detect.detect('path_to_image')
						cva.imwrite('detect.png', color_img)