




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()
_lowerCamelCase      			=						logging.get_logger(__name__)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						List[str]=False				)  ->							Dict:
				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(_UpperCAmelCase				)-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(_UpperCAmelCase				)-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(_UpperCAmelCase				)-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(_UpperCAmelCase				)-1}'				)
								if key.startswith('''head'''				):
												UpperCAmelCase_										=					key.replace('''head'''	,					'''classifier'''				)
								UpperCAmelCase_										=					value

				return new_state_dict




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						List[str]				)  ->							List[Any]:
				# 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_										=					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  SCREAMING_SNAKE_CASE     (	)  ->							str:
				UpperCAmelCase_										=					'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase_										=					Image.open(requests.get(_UpperCAmelCase	,					stream=_UpperCAmelCase				).raw				)

				return image




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						Union[str, Any]				)  ->							List[str]:
				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_										=					1000
								UpperCAmelCase_										=					'imagenet-1k-id2label.json'
								UpperCAmelCase_										=					(1, 1000)
				else:
								raise ValueError(f'Model {model_name} not supported'				)

				# set config attributes
				UpperCAmelCase_										=					json.load(open(hf_hub_download(_UpperCAmelCase	,					_UpperCAmelCase	,					repo_type='''dataset'''				)	,					'''r'''				)				)
				UpperCAmelCase_										=					{int(_UpperCAmelCase				): 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=_UpperCAmelCase	,					align=_UpperCAmelCase	,					do_random_crop=_UpperCAmelCase				)

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

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

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

				# rename keys
				UpperCAmelCase_										=					rename_keys(_UpperCAmelCase	,					encoder_only=_UpperCAmelCase				)
				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(_UpperCAmelCase	,					_UpperCAmelCase				)

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

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

				# set expected_slice based on model name
				# ADE20k checkpoints
				if model_name == "segformer.b0.512x512.ade.160k":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
								        [[-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":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3529, -10.0304], [-7.5_208, -9.4_103, -9.6_239]],
								        [[-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":
								UpperCAmelCase_										=					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":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [[-9.0_878, -10.2081, -10.1891], [-9.3_144, -10.7941, -10.9843], [-9.2_294, -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":
								UpperCAmelCase_										=					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":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [[-9.5_524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5_842, -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":
								UpperCAmelCase_										=					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]],
								        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
								    ]				)
				elif model_name == "segformer.b0.512x1024.city.160k":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [[-7.8_217, -9.8_767, -10.1717], [-9.4_438, -10.9058, -11.4047], [-9.7_939, -12.3495, -12.1079]],
								        [[-7.1_514, -9.5_336, -10.0860], [-9.7_776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
								        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
								    ]				)
				elif model_name == "segformer.b0.640x1280.city.160k":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [
								            [-1.1_372e01, -1.2_787e01, -1.3_477e01],
								            [-1.2_536e01, -1.4_194e01, -1.4_409e01],
								            [-1.3_217e01, -1.4_888e01, -1.5_327e01],
								        ],
								        [
								            [-1.4_791e01, -1.7_122e01, -1.8_277e01],
								            [-1.7_163e01, -1.9_192e01, -1.9_533e01],
								            [-1.7_897e01, -1.9_991e01, -2.0_315e01],
								        ],
								        [
								            [7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
								            [4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
								            [3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
								        ],
								    ]				)
				elif model_name == "segformer.b0.768x768.city.160k":
								UpperCAmelCase_										=					torch.tensor(
								    [
								        [[-9.4_959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
								        [[-9.8_905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
								        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
								    ]				)
				elif model_name == "segformer.b1.1024x1024.city.160k":
								UpperCAmelCase_										=					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]],
								        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
								    ]				)
				elif model_name == "segformer.b2.1024x1024.city.160k":
								UpperCAmelCase_										=					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]],
								        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
								    ]				)
				elif model_name == "segformer.b3.1024x1024.city.160k":
								UpperCAmelCase_										=					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]],
								        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
								    ]				)
				elif model_name == "segformer.b4.1024x1024.city.160k":
								UpperCAmelCase_										=					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]],
								        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
								    ]				)
				elif model_name == "segformer.b5.1024x1024.city.160k":
								UpperCAmelCase_										=					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]],
								        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
								    ]				)
				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]	,					_UpperCAmelCase	,					atol=1e-2				)

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


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





