
"""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														=		logging.get_logger(__name__)



def 				lowerCamelCase__		(      __snake_case,					__snake_case=False     )							->		List[str]:


					"""simple docstring"""





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

					return new_state_dict



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		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)
															_UpperCamelCase										=  state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''     )
															_UpperCamelCase										=  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
															_UpperCamelCase										=  kv_weight[
															    : config.hidden_sizes[i], :
															]
															_UpperCamelCase										=  kv_bias[: config.hidden_sizes[i]]
															_UpperCamelCase										=  kv_weight[
															    config.hidden_sizes[i] :, :
															]
															_UpperCamelCase										=  kv_bias[
															    config.hidden_sizes[i] :
															]



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  "http://images.cocodataset.org/val2017/000000039769.jpg"
					_UpperCamelCase										=  Image.open(requests.get(a_,					stream=a_     ).raw     )

					return image



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  SegformerConfig()
					_UpperCamelCase										=  False

					# set attributes based on model_name
					_UpperCamelCase										=  "huggingface/label-files"
					if "segformer" in model_name:
										_UpperCamelCase										=  model_name[len('''segformer.'''     ) : len('''segformer.'''     ) + 2]
										if "ade" in model_name:
															_UpperCamelCase										=  1_50
															_UpperCamelCase										=  "ade20k-id2label.json"
															_UpperCamelCase										=  (1, 1_50, 1_28, 1_28)
										elif "city" in model_name:
															_UpperCamelCase										=  19
															_UpperCamelCase										=  "cityscapes-id2label.json"
															_UpperCamelCase										=  (1, 19, 1_28, 1_28)
										else:
															raise ValueError(F'''Model {model_name} not supported'''     )
					elif "mit" in model_name:
										_UpperCamelCase										=  True
										_UpperCamelCase										=  model_name[4:6]
										_UpperCamelCase										=  10_00
										_UpperCamelCase										=  "imagenet-1k-id2label.json"
										_UpperCamelCase										=  (1, 10_00)
					else:
										raise ValueError(F'''Model {model_name} not supported'''     )

					# set config attributes
					_UpperCamelCase										=  json.load(open(hf_hub_download(a_,					a_,					repo_type='''dataset'''     ),					'''r'''     )     )
					_UpperCamelCase										=  {int(a_     ): v for k, v in idalabel.items()}
					_UpperCamelCase										=  idalabel
					_UpperCamelCase										=  {v: k for k, v in idalabel.items()}
					if size == "b0":
										pass
					elif size == "b1":
										_UpperCamelCase										=  [64, 1_28, 3_20, 5_12]
										_UpperCamelCase										=  2_56
					elif size == "b2":
										_UpperCamelCase										=  [64, 1_28, 3_20, 5_12]
										_UpperCamelCase										=  7_68
										_UpperCamelCase										=  [3, 4, 6, 3]
					elif size == "b3":
										_UpperCamelCase										=  [64, 1_28, 3_20, 5_12]
										_UpperCamelCase										=  7_68
										_UpperCamelCase										=  [3, 4, 18, 3]
					elif size == "b4":
										_UpperCamelCase										=  [64, 1_28, 3_20, 5_12]
										_UpperCamelCase										=  7_68
										_UpperCamelCase										=  [3, 8, 27, 3]
					elif size == "b5":
										_UpperCamelCase										=  [64, 1_28, 3_20, 5_12]
										_UpperCamelCase										=  7_68
										_UpperCamelCase										=  [3, 6, 40, 3]
					else:
										raise ValueError(F'''Size {size} not supported'''     )

					# load image processor (only resize + normalize)
					_UpperCamelCase										=  SegformerImageProcessor(
					    image_scale=(5_12, 5_12),					keep_ratio=a_,					align=a_,					do_random_crop=a_     )

					# prepare image
					_UpperCamelCase										=  prepare_img()
					_UpperCamelCase										=  image_processor(images=a_,					return_tensors='''pt'''     ).pixel_values

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

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

					# rename keys
					_UpperCamelCase										=  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:
										_UpperCamelCase										=  False
										_UpperCamelCase										=  SegformerForImageClassification(a_     )
					else:
										_UpperCamelCase										=  SegformerForSemanticSegmentation(a_     )
					model.load_state_dict(a_     )
					model.eval()

					# forward pass
					_UpperCamelCase										=  model(a_     )
					_UpperCamelCase										=  outputs.logits

					# set expected_slice based on model name
					# ADE20k checkpoints
					if model_name == "segformer.b0.512x512.ade.160k":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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":
										_UpperCamelCase										=  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:
										_UpperCamelCase										=  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														=		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														=		parser.parse_args()
				convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""





class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  name
							_UpperCamelCase										=  value
							_UpperCamelCase										=  weight



		def __repr__( self)	->	List[str]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.value



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






							'''simple docstring'''
							return self.name



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.weight




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.value / self.weight





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for i in range(len(__snake_case     )     ):
										menu.append(Things(name[i],					value[i],					weight[i]     )     )
					return menu



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  sorted(__snake_case,					key=__snake_case,					reverse=__snake_case     )
					_UpperCamelCase										=  []
					_UpperCamelCase			,  _UpperCamelCase										=  0.0, 0.0
					for i in range(len(__snake_case     )     ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]     )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()






