





'''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()
lowercase_					= logging.get_logger(__name__)





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

    return new_state_dict





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Any    )						->str:
    # 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)
            _SCREAMING_SNAKE_CASE									=			state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'    )
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			kv_weight[
                : config.hidden_sizes[i], :
            ]
            _SCREAMING_SNAKE_CASE									=			kv_bias[: config.hidden_sizes[i]]
            _SCREAMING_SNAKE_CASE									=			kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _SCREAMING_SNAKE_CASE									=			kv_bias[
                config.hidden_sizes[i] :
            ]





def lowerCamelCase						(     )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )

    return image





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Tuple    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			SegformerConfig()
    _SCREAMING_SNAKE_CASE									=			False

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

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

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

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

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

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

    # rename keys
    _SCREAMING_SNAKE_CASE									=			rename_keys(__lowerCamelCase					,       encoder_only=__lowerCamelCase    )
    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(__lowerCamelCase					,       __lowerCamelCase    )

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

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

    # set expected_slice based on model name
    # ADE20k checkpoints
    if model_name == "segformer.b0.512x512.ade.160k":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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":
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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]					,       __lowerCamelCase					,       atol=1e-2    )

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


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










'''simple docstring'''

from typing import List

from .keymap import KEYMAP, get_character





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Optional[int]:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += [key]
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator





def lowerCamelCase						(     *__lowerCamelCase :    List[str]    )						->Dict:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += keys
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __new__(		cls ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			super().__new__(cls ,		A ,		A ,		A       )
        if not hasattr(A ,		"""key_handler"""       ):
            setattr(A ,		"""key_handler""" ,		{}       )
        setattr(A ,		"""handle_input""" ,		KeyHandler.handle_input       )

        for value in attrs.values():
            _SCREAMING_SNAKE_CASE									=			getattr(A ,		"""handle_key""" ,		[]       )
            for key in handled_keys:
                _SCREAMING_SNAKE_CASE									=			value
        return new_cls





    @staticmethod
    def 			snake_case_(		cls       )		->   str:
        _SCREAMING_SNAKE_CASE									=			get_character()
        if char != KEYMAP["undefined"]:
            _SCREAMING_SNAKE_CASE									=			ord(A       )
        _SCREAMING_SNAKE_CASE									=			cls.key_handler.get(A       )
        if handler:
            _SCREAMING_SNAKE_CASE									=			char
            return handler(cls       )
        else:
            return None





def lowerCamelCase						(     cls :    Any    )						->Dict:
    return KeyHandler(cls.__name__					,       cls.__bases__					,       cls.__dict__.copy()    )



