


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            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=False	):





			"""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(SCREAMING_SNAKE_CASE_	)-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(SCREAMING_SNAKE_CASE_	)-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(SCREAMING_SNAKE_CASE_	)-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(SCREAMING_SNAKE_CASE_	)-1}'	)
						if key.startswith("""head"""	):
									UpperCamelCase         =					key.replace("""head"""       ,  """classifier"""	)
						UpperCamelCase         =					value

			return new_state_dict






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_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 							_lowercase    (		):





			"""simple docstring"""




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

			return image






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_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         =					150
									UpperCamelCase         =					"""ade20k-id2label.json"""
									UpperCamelCase         =					(1, 150, 128, 128)
						elif "city" in model_name:
									UpperCamelCase         =					19
									UpperCamelCase         =					"""cityscapes-id2label.json"""
									UpperCamelCase         =					(1, 19, 128, 128)
						else:
									raise ValueError(f'Model {model_name} not supported'	)
			elif "mit" in model_name:
						UpperCamelCase         =					True
						UpperCamelCase         =					model_name[4:6]
						UpperCamelCase         =					1_000
						UpperCamelCase         =					"""imagenet-1k-id2label.json"""
						UpperCamelCase         =					(1, 1_000)
			else:
						raise ValueError(f'Model {model_name} not supported'	)

			# set config attributes
			UpperCamelCase         =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)       ,  """r"""	)	)
			UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): 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, 128, 320, 512]
						UpperCamelCase         =					256
			elif size == "b2":
						UpperCamelCase         =					[64, 128, 320, 512]
						UpperCamelCase         =					768
						UpperCamelCase         =					[3, 4, 6, 3]
			elif size == "b3":
						UpperCamelCase         =					[64, 128, 320, 512]
						UpperCamelCase         =					768
						UpperCamelCase         =					[3, 4, 18, 3]
			elif size == "b4":
						UpperCamelCase         =					[64, 128, 320, 512]
						UpperCamelCase         =					768
						UpperCamelCase         =					[3, 8, 27, 3]
			elif size == "b5":
						UpperCamelCase         =					[64, 128, 320, 512]
						UpperCamelCase         =					768
						UpperCamelCase         =					[3, 6, 40, 3]
			else:
						raise ValueError(f'Size {size} not supported'	)

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

			# prepare image
			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(images=SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	).pixel_values

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

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

			# rename keys
			UpperCamelCase         =					rename_keys(SCREAMING_SNAKE_CASE_       ,  encoder_only=SCREAMING_SNAKE_CASE_	)
			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(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# create HuggingFace model and load state dict
			if encoder_only:
						UpperCamelCase         =					False
						UpperCamelCase         =					SegformerForImageClassification(SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					SegformerForSemanticSegmentation(SCREAMING_SNAKE_CASE_	)
			model.load_state_dict(SCREAMING_SNAKE_CASE_	)
			model.eval()

			# forward pass
			UpperCamelCase         =					model(SCREAMING_SNAKE_CASE_	)
			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]],
						        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
						        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
						    ]	)
			elif model_name == "segformer.b1.512x512.ade.160k":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3_529, -10.0_304], [-7.5208, -9.4103, -9.6239]],
						        [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
						        [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
						    ]	)
			elif model_name == "segformer.b2.512x512.ade.160k":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
						        [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
						        [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
						    ]	)
			elif model_name == "segformer.b3.512x512.ade.160k":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-9.0878, -10.2_081, -10.1_891], [-9.3144, -10.7_941, -10.9_843], [-9.2294, -10.3_855, -10.5_704]],
						        [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
						        [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
						    ]	)
			elif model_name == "segformer.b4.512x512.ade.160k":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
						        [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
						        [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
						    ]	)
			elif model_name == "segformer.b5.640x640.ade.160k":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-9.5524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5842, -12.8_851, -13.9_414]],
						        [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
						        [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
						    ]	)
			# Cityscapes checkpoints
			elif model_name == "segformer.b0.1024x1024.city.160k":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
						        [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
						        [[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, -10.1_717], [-9.4438, -10.9_058, -11.4_047], [-9.7939, -12.3_495, -12.1_079]],
						        [[-7.1514, -9.5336, -10.0_860], [-9.7776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
						        [[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(
						    [
						        [
						            [-1.1372e01, -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":
						UpperCamelCase         =					torch.tensor(
						    [
						        [[-9.4959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
						        [[-9.8905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
						        [[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(
						    [
						        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
						        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
						        [[-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(
						    [
						        [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
						        [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
						        [[-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(
						    [
						        [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
						        [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
						        [[-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(
						    [
						        [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
						        [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
						        [[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(
						    [
						        [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
						        [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
						        [[-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]       ,  SCREAMING_SNAKE_CASE_       ,  atol=1e-2	)

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


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








from ..utils import DummyObject, requires_backends




class    UpperCAmelCase					(       metaclass=__snake_case    ):
			lowercase						= ["""keras_nlp"""]




			def __init__(						self							:		List[str]       ,       *__magic_name__							:		str       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						requires_backends(self       ,       ["""keras_nlp"""]						)




