




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()
UpperCAmelCase_									=	logging.get_logger(__name__)




def 					lowerCamelCase__       (						A__       :					Any  ,			A__       :					Union[str, Any]=False	):





       '''simple docstring'''



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

       return new_state_dict




def 					lowerCamelCase__       (						A__       :					List[Any]  ,			A__       :					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)
                     __lowerCamelCase   =						state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'	)
                     __lowerCamelCase   =						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
                     __lowerCamelCase   =						kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     __lowerCamelCase   =						kv_bias[: config.hidden_sizes[i]]
                     __lowerCamelCase   =						kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     __lowerCamelCase   =						kv_bias[
                         config.hidden_sizes[i] :
                     ]




def 					lowerCamelCase__       (						):





       '''simple docstring'''



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

       return image




@torch.no_grad()
def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Optional[Any]  ,			A__       :					Union[str, Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						SegformerConfig()
       __lowerCamelCase   =						False

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

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

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

       # prepare image
       __lowerCamelCase   =						prepare_img()
       __lowerCamelCase   =						image_processor(images=A__  ,			return_tensors="""pt"""	).pixel_values

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

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

       # rename keys
       __lowerCamelCase   =						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:
              __lowerCamelCase   =						False
              __lowerCamelCase   =						SegformerForImageClassification(A__	)
       else:
              __lowerCamelCase   =						SegformerForSemanticSegmentation(A__	)
       model.load_state_dict(A__	)
       model.eval()

       # forward pass
       __lowerCamelCase   =						model(A__	)
       __lowerCamelCase   =						outputs.logits

       # set expected_slice based on model name
       # ADE20k checkpoints
       if model_name == "segformer.b0.512x512.ade.160k":
              __lowerCamelCase   =						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]],
                      [[-12.1_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":
              __lowerCamelCase   =						torch.tensor(
                  [
                      [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
                      [[-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":
              __lowerCamelCase   =						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":
              __lowerCamelCase   =						torch.tensor(
                  [
                      [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -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":
              __lowerCamelCase   =						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":
              __lowerCamelCase   =						torch.tensor(
                  [
                      [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -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":
              __lowerCamelCase   =						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]],
                      [[0.5_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":
              __lowerCamelCase   =						torch.tensor(
                  [
                      [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
                      [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
                      [[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":
              __lowerCamelCase   =						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":
              __lowerCamelCase   =						torch.tensor(
                  [
                      [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
                      [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
                      [[0.2_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":
              __lowerCamelCase   =						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]],
                      [[-3.6_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":
              __lowerCamelCase   =						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]],
                      [[-4.5_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":
              __lowerCamelCase   =						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]],
                      [[-4.7_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":
              __lowerCamelCase   =						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]],
                      [[1.0_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":
              __lowerCamelCase   =						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]],
                      [[-1.7_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:
              __lowerCamelCase   =						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__":
       UpperCAmelCase_									=	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.'
       )
       UpperCAmelCase_									=	parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]	) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 1 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     __lowerCamelCase   =						cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1	)
                     else:
                            next_generation_row.append(0	)

              next_generation.append(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


if __name__ == "__main__":
       UpperCAmelCase_									=	generate_images(GLIDER, 16)
       images[0].save('out.gif', save_all=True, append_images=images[1:])

