





"""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()
a		:				Union[str, Any]  			=    logging.get_logger(__name__)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					Union[str, Any]=False      )				->str:






	'''simple docstring'''






	a   :	Dict			=       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   :	Union[str, Any]			=       key.replace("backbone"					, "segformer.encoder"      )
		if "patch_embed" in key:
			# replace for example patch_embed1 by patch_embeddings.0
			a   :	List[Any]			=       key[key.find("patch_embed"      ) + len("patch_embed"      )]
			a   :	str			=       key.replace(F"""patch_embed{idx}"""					, F"""patch_embeddings.{int(lowerCAmelCase__      )-1}"""      )
		if "norm" in key:
			a   :	Tuple			=       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   :	Optional[int]			=       key.replace(F"""layer_norm{idx}"""					, F"""layer_norm.{int(lowerCAmelCase__      )-1}"""      )
		if "layer_norm1" in key:
			a   :	Dict			=       key.replace("layer_norm1"					, "layer_norm_1"      )
		if "layer_norm2" in key:
			a   :	List[str]			=       key.replace("layer_norm2"					, "layer_norm_2"      )
		if "block" in key:
			# replace for example block1 by block.0
			a   :	Dict			=       key[key.find("block"      ) + len("block"      )]
			a   :	Any			=       key.replace(F"""block{idx}"""					, F"""block.{int(lowerCAmelCase__      )-1}"""      )
		if "attn.q" in key:
			a   :	int			=       key.replace("attn.q"					, "attention.self.query"      )
		if "attn.proj" in key:
			a   :	Optional[int]			=       key.replace("attn.proj"					, "attention.output.dense"      )
		if "attn" in key:
			a   :	Dict			=       key.replace("attn"					, "attention.self"      )
		if "fc1" in key:
			a   :	str			=       key.replace("fc1"					, "dense1"      )
		if "fc2" in key:
			a   :	Tuple			=       key.replace("fc2"					, "dense2"      )
		if "linear_pred" in key:
			a   :	Union[str, Any]			=       key.replace("linear_pred"					, "classifier"      )
		if "linear_fuse" in key:
			a   :	int			=       key.replace("linear_fuse.conv"					, "linear_fuse"      )
			a   :	Any			=       key.replace("linear_fuse.bn"					, "batch_norm"      )
		if "linear_c" in key:
			# replace for example linear_c4 by linear_c.3
			a   :	int			=       key[key.find("linear_c"      ) + len("linear_c"      )]
			a   :	Union[str, Any]			=       key.replace(F"""linear_c{idx}"""					, F"""linear_c.{int(lowerCAmelCase__      )-1}"""      )
		if key.startswith("head"      ):
			a   :	Optional[int]			=       key.replace("head"					, "classifier"      )
		a   :	Any			=       value

	return new_state_dict






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Union[str, Any]      )				->Optional[int]:






	'''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   :	List[str]			=       state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
			a   :	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   :	Dict			=       kv_weight[
			    : config.hidden_sizes[i], :
			]
			a   :	List[Any]			=       kv_bias[: config.hidden_sizes[i]]
			a   :	List[str]			=       kv_weight[
			    config.hidden_sizes[i] :, :
			]
			a   :	Dict			=       kv_bias[
			    config.hidden_sizes[i] :
			]






def    _SCREAMING_SNAKE_CASE	(			)				->Union[str, Any]:






	'''simple docstring'''






	a   :	Tuple			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
	a   :	Any			=       Image.open(requests.get(lowerCAmelCase__					, stream=lowerCAmelCase__      ).raw      )

	return image






@torch.no_grad()
def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any					, _lowercase		:					str					, _lowercase		:					Dict      )				->Optional[Any]:






	'''simple docstring'''






	a   :	List[Any]			=       SegformerConfig()
	a   :	Dict			=       False

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

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

	# load image processor (only resize + normalize)
	a   :	List[Any]			=       SegformerImageProcessor(
	    image_scale=(512, 512)					, keep_ratio=lowerCAmelCase__					, align=lowerCAmelCase__					, do_random_crop=lowerCAmelCase__      )

	# prepare image
	a   :	str			=       prepare_img()
	a   :	List[str]			=       image_processor(images=lowerCAmelCase__					, return_tensors="pt"      ).pixel_values

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

	# load original state dict
	if encoder_only:
		a   :	str			=       torch.load(lowerCAmelCase__					, map_location=torch.device("cpu"      )      )
	else:
		a   :	Tuple			=       torch.load(lowerCAmelCase__					, map_location=torch.device("cpu"      )      )["state_dict"]

	# rename keys
	a   :	Dict			=       rename_keys(lowerCAmelCase__					, encoder_only=lowerCAmelCase__      )
	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(lowerCAmelCase__					, lowerCAmelCase__      )

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

	# forward pass
	a   :	Tuple			=       model(lowerCAmelCase__      )
	a   :	Optional[int]			=       outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		a   :	List[str]			=       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   :	List[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   :	Tuple			=       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   :	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":
		a   :	Optional[Any]			=       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   :	Tuple			=       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   :	Union[str, Any]			=       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   :	List[Any]			=       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   :	Dict			=       torch.tensor(
		    [
		        [
		            [-1.1_3_7_2E0_1, -1.2_7_8_7E0_1, -1.3_4_7_7E0_1],
		            [-1.2_5_3_6E0_1, -1.4_1_9_4E0_1, -1.4_4_0_9E0_1],
		            [-1.3_2_1_7E0_1, -1.4_8_8_8E0_1, -1.5_3_2_7E0_1],
		        ],
		        [
		            [-1.4_7_9_1E0_1, -1.7_1_2_2E0_1, -1.8_2_7_7E0_1],
		            [-1.7_1_6_3E0_1, -1.9_1_9_2E0_1, -1.9_5_3_3E0_1],
		            [-1.7_8_9_7E0_1, -1.9_9_9_1E0_1, -2.0_3_1_5E0_1],
		        ],
		        [
		            [7.6_7_2_3E-0_1, 4.1_9_2_1E-0_1, -7.7_8_7_8E-0_2],
		            [4.7_7_7_2E-0_1, 9.5_5_5_7E-0_3, -2.8_0_8_2E-0_1],
		            [3.6_0_3_2E-0_1, -2.4_8_2_6E-0_1, -5.1_1_6_8E-0_1],
		        ],
		    ]      )
	elif model_name == "segformer.b0.768x768.city.160k":
		a   :	Dict			=       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   :	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":
		a   :	Tuple			=       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   :	List[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   :	int			=       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]					, lowerCAmelCase__					, atol=1E-2      )

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


if __name__ == "__main__":
					a		:				Any  			=    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		:				Any  			=    parser.parse_args()
					convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




from ..utils import DummyObject, requires_backends


class  __UpperCamelCase       (		metaclass=a__		):
							lowerCamelCase     :      Optional[Any]         =["""transformers""", """torch""", """note_seq"""]






							def __init__(     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								requires_backends(self   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )


