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






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any=False   )				->		str:







	'''simple docstring'''


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

	return new_state_dict






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[int]   )				->		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)
			__magic_name__		:		Optional[Any]	   =     state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''   )
			__magic_name__		:		str	   =     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
			__magic_name__		:		Any	   =     kv_weight[
			    : config.hidden_sizes[i], :
			]
			__magic_name__		:		str	   =     kv_bias[: config.hidden_sizes[i]]
			__magic_name__		:		Dict	   =     kv_weight[
			    config.hidden_sizes[i] :, :
			]
			__magic_name__		:		int	   =     kv_bias[
			    config.hidden_sizes[i] :
			]






def   lowerCAmelCase_						(					)				->		int:







	'''simple docstring'''


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

	return image






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Dict      ,   _snake_case      :  Optional[Any]   )				->		Union[str, Any]:







	'''simple docstring'''


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

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

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

	# load image processor (only resize + normalize)
	__magic_name__		:		List[str]	   =     SegformerImageProcessor(
	    image_scale=(512, 512)      ,   keep_ratio=A__      ,   align=A__      ,   do_random_crop=A__   )

	# prepare image
	__magic_name__		:		Tuple	   =     prepare_img()
	__magic_name__		:		Dict	   =     image_processor(images=A__      ,   return_tensors="pt"   ).pixel_values

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

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

	# rename keys
	__magic_name__		:		str	   =     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:
		__magic_name__		:		Union[str, Any]	   =     False
		__magic_name__		:		Dict	   =     SegformerForImageClassification(A__   )
	else:
		__magic_name__		:		Optional[Any]	   =     SegformerForSemanticSegmentation(A__   )
	model.load_state_dict(A__   )
	model.eval()

	# forward pass
	__magic_name__		:		List[str]	   =     model(A__   )
	__magic_name__		:		int	   =     outputs.logits

	# set expected_slice based on model name
	# ADE20k checkpoints
	if model_name == "segformer.b0.512x512.ade.160k":
		__magic_name__		:		str	   =     torch.tensor(
		    [
		        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
		        [[-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":
		__magic_name__		:		List[Any]	   =     torch.tensor(
		    [
		        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3529, -10.0304], [-7.5_208, -9.4_103, -9.6_239]],
		        [[-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":
		__magic_name__		:		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":
		__magic_name__		:		int	   =     torch.tensor(
		    [
		        [[-9.0_878, -10.2081, -10.1891], [-9.3_144, -10.7941, -10.9843], [-9.2_294, -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":
		__magic_name__		:		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":
		__magic_name__		:		Optional[Any]	   =     torch.tensor(
		    [
		        [[-9.5_524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5_842, -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":
		__magic_name__		:		Optional[int]	   =     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]],
		        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
		    ]   )
	elif model_name == "segformer.b0.512x1024.city.160k":
		__magic_name__		:		Any	   =     torch.tensor(
		    [
		        [[-7.8_217, -9.8_767, -10.1717], [-9.4_438, -10.9058, -11.4047], [-9.7_939, -12.3495, -12.1079]],
		        [[-7.1_514, -9.5_336, -10.0860], [-9.7_776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
		        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
		    ]   )
	elif model_name == "segformer.b0.640x1280.city.160k":
		__magic_name__		:		int	   =     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":
		__magic_name__		:		List[Any]	   =     torch.tensor(
		    [
		        [[-9.4_959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
		        [[-9.8_905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
		        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
		    ]   )
	elif model_name == "segformer.b1.1024x1024.city.160k":
		__magic_name__		:		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]],
		        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
		    ]   )
	elif model_name == "segformer.b2.1024x1024.city.160k":
		__magic_name__		:		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]],
		        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
		    ]   )
	elif model_name == "segformer.b3.1024x1024.city.160k":
		__magic_name__		:		List[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]],
		        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
		    ]   )
	elif model_name == "segformer.b4.1024x1024.city.160k":
		__magic_name__		:		int	   =     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]],
		        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
		    ]   )
	elif model_name == "segformer.b5.1024x1024.city.160k":
		__magic_name__		:		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]],
		        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
		    ]   )
	else:
		__magic_name__		:		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__":
				snake_case :		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."
				)
				snake_case :		int							      =  parser.parse_args()
				convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = x
 __lowercase							  = y
 for step in range(A__      ):  # noqa: B007
  __lowercase							  = a * a - b * b + x
  __lowercase							  = 2 * a * b + y
  __lowercase							  = a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return (255, 255, 255)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(A__     ,				1     ,				1      )      )


def 		_A     (    A__ = 800     ,				A__ = 600     ,				A__ = -0.6     ,				A__ = 0     ,				A__ = 3.2     ,				A__ = 50     ,				A__ = True     ,				):






 """simple docstring"""



 __lowercase							  = Image.new('''RGB'''     ,				(image_width, image_height)      )
 __lowercase							  = img.load()

 # loop through the image-coordinates
 for image_x in range(A__      ):
  for image_y in range(A__      ):
   # determine the figure-coordinates based on the image-coordinates
   __lowercase							  = figure_width / image_width * image_height
   __lowercase							  = figure_center_x + (image_x / image_width - 0.5) * figure_width
   __lowercase							  = figure_center_y + (image_y / image_height - 0.5) * figure_height

   __lowercase							  = get_distance(A__     ,				A__     ,				A__      )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __lowercase							  = get_color_coded_rgb(A__      )
   else:
    __lowercase							  = get_black_and_white_rgb(A__      )

 return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 lowerCAmelCase__											=	get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

