


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





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

    return new_state_dict





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	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)
            UpperCAmelCase__			:		Dict													=				state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""						)
            UpperCAmelCase__			:		List[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
            UpperCAmelCase__			:		List[str]													=				kv_weight[
                : config.hidden_sizes[i], :
            ]
            UpperCAmelCase__			:		Tuple													=				kv_bias[: config.hidden_sizes[i]]
            UpperCAmelCase__			:		Optional[Any]													=				kv_weight[
                config.hidden_sizes[i] :, :
            ]
            UpperCAmelCase__			:		List[Any]													=				kv_bias[
                config.hidden_sizes[i] :
            ]





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

    return image





@torch.no_grad()
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		str													=				SegformerConfig()
    UpperCAmelCase__			:		Optional[Any]													=				False

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

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

    # load image processor (only resize + normalize)
    UpperCAmelCase__			:		List[Any]													=				SegformerImageProcessor(
        image_scale=(5_12, 5_12)  ,					keep_ratio=__lowerCamelCase  ,					align=__lowerCamelCase  ,					do_random_crop=__lowerCamelCase						)

    # prepare image
    UpperCAmelCase__			:		List[str]													=				prepare_img()
    UpperCAmelCase__			:		int													=				image_processor(images=__lowerCamelCase  ,					return_tensors='''pt'''						).pixel_values

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

    # load original state dict
    if encoder_only:
        UpperCAmelCase__			:		Tuple													=				torch.load(__lowerCamelCase  ,					map_location=torch.device('''cpu'''						)						)
    else:
        UpperCAmelCase__			:		List[Any]													=				torch.load(__lowerCamelCase  ,					map_location=torch.device('''cpu'''						)						)['''state_dict''']

    # rename keys
    UpperCAmelCase__			:		Tuple													=				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:
        UpperCAmelCase__			:		Optional[int]													=				False
        UpperCAmelCase__			:		List[Any]													=				SegformerForImageClassification(__lowerCamelCase						)
    else:
        UpperCAmelCase__			:		Optional[Any]													=				SegformerForSemanticSegmentation(__lowerCamelCase						)
    model.load_state_dict(__lowerCamelCase						)
    model.eval()

    # forward pass
    UpperCAmelCase__			:		Optional[int]													=				model(__lowerCamelCase						)
    UpperCAmelCase__			:		Optional[int]													=				outputs.logits

    # set expected_slice based on model name
    # ADE20k checkpoints
    if model_name == "segformer.b0.512x512.ade.160k":
        UpperCAmelCase__			:		List[Any]													=				torch.tensor(
            [
                [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
                [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
                [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
            ]						)
    elif model_name == "segformer.b1.512x512.ade.160k":
        UpperCAmelCase__			:		Dict													=				torch.tensor(
            [
                [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
                [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
                [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
            ]						)
    elif model_name == "segformer.b2.512x512.ade.160k":
        UpperCAmelCase__			:		Tuple													=				torch.tensor(
            [
                [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
                [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
                [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
            ]						)
    elif model_name == "segformer.b3.512x512.ade.160k":
        UpperCAmelCase__			:		str													=				torch.tensor(
            [
                [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
                [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
                [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
            ]						)
    elif model_name == "segformer.b4.512x512.ade.160k":
        UpperCAmelCase__			:		Dict													=				torch.tensor(
            [
                [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
                [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
                [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
            ]						)
    elif model_name == "segformer.b5.640x640.ade.160k":
        UpperCAmelCase__			:		str													=				torch.tensor(
            [
                [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
                [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
                [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
            ]						)
    # Cityscapes checkpoints
    elif model_name == "segformer.b0.1024x1024.city.160k":
        UpperCAmelCase__			:		int													=				torch.tensor(
            [
                [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
                [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
                [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
            ]						)
    elif model_name == "segformer.b0.512x1024.city.160k":
        UpperCAmelCase__			:		Tuple													=				torch.tensor(
            [
                [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
                [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
                [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
            ]						)
    elif model_name == "segformer.b0.640x1280.city.160k":
        UpperCAmelCase__			:		Dict													=				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__			:		Tuple													=				torch.tensor(
            [
                [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
                [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
                [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
            ]						)
    elif model_name == "segformer.b1.1024x1024.city.160k":
        UpperCAmelCase__			:		Any													=				torch.tensor(
            [
                [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
                [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
                [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
            ]						)
    elif model_name == "segformer.b2.1024x1024.city.160k":
        UpperCAmelCase__			:		List[Any]													=				torch.tensor(
            [
                [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
                [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
                [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
            ]						)
    elif model_name == "segformer.b3.1024x1024.city.160k":
        UpperCAmelCase__			:		Tuple													=				torch.tensor(
            [
                [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
                [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
                [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
            ]						)
    elif model_name == "segformer.b4.1024x1024.city.160k":
        UpperCAmelCase__			:		Optional[int]													=				torch.tensor(
            [
                [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
                [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
                [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
            ]						)
    elif model_name == "segformer.b5.1024x1024.city.160k":
        UpperCAmelCase__			:		Any													=				torch.tensor(
            [
                [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
                [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
                [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
            ]						)
    else:
        UpperCAmelCase__			:		int													=				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__":
  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)







'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline



