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()
lowerCAmelCase			:							Optional[int]     =logging.get_logger(__name__)







def 						A__  (					__A   , __A=False	):







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

    return new_state_dict







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    # for each of the encoder blocks:
    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     :		Any 						=						state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""	)
            _lowerCamelCase     :		Union[str, 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
            _lowerCamelCase     :		List[str] 						=						kv_weight[
                : config.hidden_sizes[i], :
            ]
            _lowerCamelCase     :		Tuple 						=						kv_bias[: config.hidden_sizes[i]]
            _lowerCamelCase     :		int 						=						kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _lowerCamelCase     :		Dict 						=						kv_bias[
                config.hidden_sizes[i] :
            ]







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _lowerCamelCase     :		List[str] 						=						Image.open(requests.get(__A   , stream=__A	).raw	)

    return image







@torch.no_grad()
def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						SegformerConfig()
    _lowerCamelCase     :		Union[str, Any] 						=						False

    # set attributes based on model_name
    _lowerCamelCase     :		int 						=						"""huggingface/label-files"""
    if "segformer" in model_name:
        _lowerCamelCase     :		Optional[int] 						=						model_name[len("""segformer."""	) : len("""segformer."""	) + 2]
        if "ade" in model_name:
            _lowerCamelCase     :		int 						=						150
            _lowerCamelCase     :		int 						=						"""ade20k-id2label.json"""
            _lowerCamelCase     :		Tuple 						=						(1, 150, 128, 128)
        elif "city" in model_name:
            _lowerCamelCase     :		Optional[Any] 						=						19
            _lowerCamelCase     :		Tuple 						=						"""cityscapes-id2label.json"""
            _lowerCamelCase     :		Union[str, Any] 						=						(1, 19, 128, 128)
        else:
            raise ValueError(F"""Model {model_name} not supported"""	)
    elif "mit" in model_name:
        _lowerCamelCase     :		Tuple 						=						True
        _lowerCamelCase     :		Optional[Any] 						=						model_name[4:6]
        _lowerCamelCase     :		str 						=						1_000
        _lowerCamelCase     :		Optional[Any] 						=						"""imagenet-1k-id2label.json"""
        _lowerCamelCase     :		str 						=						(1, 1_000)
    else:
        raise ValueError(F"""Model {model_name} not supported"""	)

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

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

    # prepare image
    _lowerCamelCase     :		Optional[Any] 						=						prepare_img()
    _lowerCamelCase     :		Optional[Any] 						=						image_processor(images=__A   , return_tensors="""pt"""	).pixel_values

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

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

    # rename keys
    _lowerCamelCase     :		Any 						=						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     :		str 						=						False
        _lowerCamelCase     :		List[str] 						=						SegformerForImageClassification(__A	)
    else:
        _lowerCamelCase     :		Union[str, Any] 						=						SegformerForSemanticSegmentation(__A	)
    model.load_state_dict(__A	)
    model.eval()

    # forward pass
    _lowerCamelCase     :		str 						=						model(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						outputs.logits

    # set expected_slice based on model name
    # ADE20k checkpoints
    if model_name == "segformer.b0.512x512.ade.160k":
        _lowerCamelCase     :		Tuple 						=						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     :		str 						=						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     :		Any 						=						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     :		Any 						=						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     :		List[Any] 						=						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     :		Any 						=						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     :		Union[str, Any] 						=						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     :		Any 						=						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     :		Dict 						=						torch.tensor(
            [
                [
                    [-1.13_72E01, -1.27_87E01, -1.34_77E01],
                    [-1.25_36E01, -1.41_94E01, -1.44_09E01],
                    [-1.32_17E01, -1.48_88E01, -1.53_27E01],
                ],
                [
                    [-1.47_91E01, -1.71_22E01, -1.82_77E01],
                    [-1.71_63E01, -1.91_92E01, -1.95_33E01],
                    [-1.78_97E01, -1.99_91E01, -2.03_15E01],
                ],
                [
                    [7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
                    [4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
                    [3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
                ],
            ]	)
    elif model_name == "segformer.b0.768x768.city.160k":
        _lowerCamelCase     :		Optional[Any] 						=						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     :		Tuple 						=						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     :		Union[str, Any] 						=						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     :		Union[str, Any] 						=						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     :		str 						=						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     :		int 						=						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     :		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__":
    lowerCAmelCase			:							Tuple     =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."
    )
    lowerCAmelCase			:							List[Any]     =parser.parse_args()
    convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





from __future__ import annotations

lowerCAmelCase			:							int     =[]







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        if board[row][i] == 1:
            return False
    for i in range(len(__A	)	):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , -1   , -1	)	):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , len(__A	)	)	):
        if board[i][j] == 1:
            return False
    return True







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if row >= len(__A	):
        solution.append(__A	)
        printboard(__A	)
        print()
        return True
    for i in range(len(__A	)	):
        if is_safe(__A   , __A   , __A	):
            _lowerCamelCase     :		int 						=						1
            solve(__A   , row + 1	)
            _lowerCamelCase     :		List[str] 						=						0
    return False







def 						A__  (					__A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        for j in range(len(__A	)	):
            if board[i][j] == 1:
                print("""Q"""   , end=""" """	)
            else:
                print("""."""   , end=""" """	)
        print()


# n=int(input("The no. of queens"))
lowerCAmelCase			:							int     =8
lowerCAmelCase			:							Union[str, Any]     =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))




