




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				:				Tuple       							=		logging.get_logger(__name__)
def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]=False				):





							'''simple docstring'''


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

							return new_state_dict
def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Any				):





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





							'''simple docstring'''


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

							return image
@torch.no_grad()
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  int			,						UpperCamelCase	:  str				):





							'''simple docstring'''


							_a     					=    SegformerConfig()
							_a     					=    False

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

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

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

							# prepare image
							_a     					=    prepare_img()
							_a     					=    image_processor(images=_lowerCAmelCase			,						return_tensors='''pt'''				).pixel_values

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

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

							# rename keys
							_a     					=    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:
														_a     					=    False
														_a     					=    SegformerForImageClassification(_lowerCAmelCase				)
							else:
														_a     					=    SegformerForSemanticSegmentation(_lowerCAmelCase				)
							model.load_state_dict(_lowerCAmelCase				)
							model.eval()

							# forward pass
							_a     					=    model(_lowerCAmelCase				)
							_a     					=    outputs.logits

							# set expected_slice based on model name
							# ADE20k checkpoints
							if model_name == "segformer.b0.512x512.ade.160k":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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":
														_a     					=    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:
														_a     					=    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__":
				_snake_case				:				int       							=		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				:				int       							=		parser.parse_args()
				convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

