






"""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()
__a       :	List[Any]             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					# 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)
															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        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)

					return image





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					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(
										    [
										        [
										            [-1.1_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":
										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__":
	__a       :	Dict             = 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.'
	)
	__a       :	Optional[Any]             = parser.parse_args()
	convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







__a       :	List[Any]             = frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'negative_prompt'])

__a       :	Any             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Dict             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Optional[Any]             = frozenset(['prompt', 'image', 'negative_prompt'])

__a       :	List[Any]             = frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

__a       :	Optional[Any]             = frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	int             = frozenset(['image', 'mask_image'])

__a       :	Tuple             = frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Optional[Any]             = frozenset(['example_image', 'image', 'mask_image'])

__a       :	Optional[Any]             = frozenset(['class_labels'])

__a       :	Tuple             = frozenset(['class_labels'])

__a       :	int             = frozenset(['batch_size'])

__a       :	int             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['batch_size'])

__a       :	Tuple             = frozenset([])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Dict             = frozenset(['prompt', 'negative_prompt'])
__a       :	Optional[int]             = frozenset(['input_tokens'])

__a       :	str             = frozenset(['input_tokens'])


