






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      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=False	)->  Union[str, Any]:





						"""simple docstring"""



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

						return new_state_dict


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  List[str]:





						"""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)
																		_UpperCAmelCase									=			state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""	)
																		_UpperCAmelCase									=			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									=			kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		_UpperCAmelCase									=			kv_bias[: config.hidden_sizes[i]]
																		_UpperCAmelCase									=			kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		_UpperCAmelCase									=			kv_bias[
																		    config.hidden_sizes[i] :
																		]


def __A    (      )->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCAmelCase									=			Image.open(requests.get(__lowerCAmelCase ,		stream=__lowerCAmelCase	).raw	)

						return image


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Tuple:





						"""simple docstring"""



						_UpperCAmelCase									=			SegformerConfig()
						_UpperCAmelCase									=			False

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

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

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

						# prepare image
						_UpperCAmelCase									=			prepare_img()
						_UpperCAmelCase									=			image_processor(images=__lowerCAmelCase ,		return_tensors='pt'	).pixel_values

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

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

						# rename keys
						_UpperCAmelCase									=			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									=			False
												_UpperCAmelCase									=			SegformerForImageClassification(__lowerCAmelCase	)
						else:
												_UpperCAmelCase									=			SegformerForSemanticSegmentation(__lowerCAmelCase	)
						model.load_state_dict(__lowerCAmelCase	)
						model.eval()

						# forward pass
						_UpperCAmelCase									=			model(__lowerCAmelCase	)
						_UpperCAmelCase									=			outputs.logits

						# set expected_slice based on model name
						# ADE20k checkpoints
						if model_name == "segformer.b0.512x512.ade.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
												        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
												        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
												    ]	)
						elif model_name == "segformer.b1.512x512.ade.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
												        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
												        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
												    ]	)
						elif model_name == "segformer.b2.512x512.ade.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
												        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
												        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
												    ]	)
						elif model_name == "segformer.b3.512x512.ade.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
												        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
												        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
												    ]	)
						elif model_name == "segformer.b4.512x512.ade.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
												        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
												        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
												    ]	)
						elif model_name == "segformer.b5.640x640.ade.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
												        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
												        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
												    ]	)
						# Cityscapes checkpoints
						elif model_name == "segformer.b0.1024x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
												        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
												        [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
												    ]	)
						elif model_name == "segformer.b0.512x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
												        [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
												        [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
												    ]	)
						elif model_name == "segformer.b0.640x1280.city.160k":
												_UpperCAmelCase									=			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									=			torch.tensor(
												    [
												        [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
												        [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
												        [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
												    ]	)
						elif model_name == "segformer.b1.1024x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
												        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
												        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
												    ]	)
						elif model_name == "segformer.b2.1024x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
												        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
												        [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
												    ]	)
						elif model_name == "segformer.b3.1024x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
												        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
												        [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
												    ]	)
						elif model_name == "segformer.b4.1024x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
												        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
												        [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
												    ]	)
						elif model_name == "segformer.b5.1024x1024.city.160k":
												_UpperCAmelCase									=			torch.tensor(
												    [
												        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
												        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
												        [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
												    ]	)
						else:
												_UpperCAmelCase									=			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      	=  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      	=  parser.parse_args()
		convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A					:  Dict											=      {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A					:  str											=      ["TimmBackbone"]


if TYPE_CHECKING:
  from .configuration_timm_backbone import TimmBackboneConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timm_backbone import TimmBackbone

else:
  import sys

  __A					:  Dict											=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



