




"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import YolosImageProcessor




class   a__			(       unittest.TestCase  ):





						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]     ,a__      :	Optional[Any]=7     ,a__      :	int=3     ,a__      :	Union[str, Any]=30     ,a__      :	List[Any]=400     ,a__      :	List[Any]=True     ,a__      :	Tuple=None     ,a__      :	Tuple=True     ,a__      :	List[str]=[0.5, 0.5, 0.5]     ,a__      :	Optional[Any]=[0.5, 0.5, 0.5]     ,a__      :	List[Any]=True     ,a__      :	Optional[int]=1 / 255     ,a__      :	Tuple=True     ,)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
											_lowerCAmelCase:Dict									=  parent
											_lowerCAmelCase:Any									=  batch_size
											_lowerCAmelCase:Any									=  num_channels
											_lowerCAmelCase:List[str]									=  min_resolution
											_lowerCAmelCase:Dict									=  max_resolution
											_lowerCAmelCase:Tuple									=  do_resize
											_lowerCAmelCase:str									=  size
											_lowerCAmelCase:Optional[int]									=  do_normalize
											_lowerCAmelCase:int									=  image_mean
											_lowerCAmelCase:int									=  image_std
											_lowerCAmelCase:Union[str, Any]									=  do_rescale
											_lowerCAmelCase:Optional[Any]									=  rescale_factor
											_lowerCAmelCase:int									=  do_pad





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											}







						def       __UpperCamelCase     (							self      :	str     ,a__      :	int     ,a__      :	List[str]=False)						->       int:




											"""simple docstring"""







											if not batched:
																_lowerCAmelCase:Optional[int]									=  image_inputs[0]
																if isinstance(a__     ,Image.Image):
																					_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  image.size
																else:
																					_lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  image.shape[1], image.shape[2]
																if w < h:
																					_lowerCAmelCase:Tuple									=  int(self.size['''shortest_edge'''] * h / w)
																					_lowerCAmelCase:Tuple									=  self.size['''shortest_edge''']
																elif w > h:
																					_lowerCAmelCase:str									=  self.size['''shortest_edge''']
																					_lowerCAmelCase:List[str]									=  int(self.size['''shortest_edge'''] * w / h)
																else:
																					_lowerCAmelCase:List[str]									=  self.size['''shortest_edge''']
																					_lowerCAmelCase:Union[str, Any]									=  self.size['''shortest_edge''']

											else:
																_lowerCAmelCase:Any									=  []
																for image in image_inputs:
																					_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  self.get_expected_values([image])
																					expected_values.append((expected_height, expected_width))
																_lowerCAmelCase:Tuple									=  max(a__     ,key=lambda a__: item[0])[0]
																_lowerCAmelCase:Any									=  max(a__     ,key=lambda a__: item[1])[1]

											return expected_height, expected_width




@require_torch
@require_vision
class   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = YolosImageProcessor if is_vision_available() else None





						def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  YolosImageProcessingTester(self)





						@property
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       List[str]:




											"""simple docstring"""







											return self.image_processor_tester.prepare_image_processor_dict()





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(a__     ,'''image_mean'''))
											self.assertTrue(hasattr(a__     ,'''image_std'''))
											self.assertTrue(hasattr(a__     ,'''do_normalize'''))
											self.assertTrue(hasattr(a__     ,'''do_resize'''))
											self.assertTrue(hasattr(a__     ,'''size'''))





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size     ,{'''shortest_edge''': 18, '''longest_edge''': 1333})
											self.assertEqual(image_processor.do_pad     ,a__)

											_lowerCAmelCase:Union[str, Any]									=  self.image_processing_class.from_dict(
											    self.image_processor_dict     ,size=42     ,max_size=84     ,pad_and_return_pixel_mask=a__)
											self.assertEqual(image_processor.size     ,{'''shortest_edge''': 42, '''longest_edge''': 84})
											self.assertEqual(image_processor.do_pad     ,a__)





						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	List[Any])						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											_lowerCAmelCase:Union[str, Any]									=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=a__)
											for image in image_inputs:
																self.assertIsInstance(a__     ,Image.Image)

											# Test not batched input
											_lowerCAmelCase:Tuple									=  image_processing(image_inputs[0]     ,return_tensors='''pt''').pixel_values

											_lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  self.image_processor_tester.get_expected_values(a__)

											self.assertEqual(
											    encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

											# Test batched
											_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  self.image_processor_tester.get_expected_values(a__     ,batched=a__)

											_lowerCAmelCase:int									=  image_processing(a__     ,return_tensors='''pt''').pixel_values
											self.assertEqual(
											    encoded_images.shape     ,(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )     ,)





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											_lowerCAmelCase:Any									=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=a__     ,numpify=a__)
											for image in image_inputs:
																self.assertIsInstance(a__     ,np.ndarray)

											# Test not batched input
											_lowerCAmelCase:Tuple									=  image_processing(image_inputs[0]     ,return_tensors='''pt''').pixel_values

											_lowerCAmelCase							,   _lowerCAmelCase:str									=  self.image_processor_tester.get_expected_values(a__)

											self.assertEqual(
											    encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

											# Test batched
											_lowerCAmelCase:Tuple									=  image_processing(a__     ,return_tensors='''pt''').pixel_values

											_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  self.image_processor_tester.get_expected_values(a__     ,batched=a__)

											self.assertEqual(
											    encoded_images.shape     ,(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )     ,)





						def       __UpperCamelCase     (							self      :	int)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											_lowerCAmelCase:Dict									=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=a__     ,torchify=a__)
											for image in image_inputs:
																self.assertIsInstance(a__     ,torch.Tensor)

											# Test not batched input
											_lowerCAmelCase:Any									=  image_processing(image_inputs[0]     ,return_tensors='''pt''').pixel_values

											_lowerCAmelCase							,   _lowerCAmelCase:int									=  self.image_processor_tester.get_expected_values(a__)

											self.assertEqual(
											    encoded_images.shape     ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,)

											# Test batched
											_lowerCAmelCase:Dict									=  image_processing(a__     ,return_tensors='''pt''').pixel_values

											_lowerCAmelCase							,   _lowerCAmelCase:str									=  self.image_processor_tester.get_expected_values(a__     ,batched=a__)

											self.assertEqual(
											    encoded_images.shape     ,(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )     ,)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  self.image_processing_class(**self.image_processor_dict)
											_lowerCAmelCase:List[str]									=  self.image_processing_class(do_resize=a__     ,do_normalize=a__     ,do_rescale=a__)
											# create random PyTorch tensors
											_lowerCAmelCase:str									=  prepare_image_inputs(self.image_processor_tester     ,equal_resolution=a__     ,torchify=a__)
											for image in image_inputs:
																self.assertIsInstance(a__     ,torch.Tensor)

											# Test whether the method "pad" and calling the image processor return the same tensors
											_lowerCAmelCase:Optional[Any]									=  image_processing_a.pad(a__     ,return_tensors='''pt''')
											_lowerCAmelCase:List[Any]									=  image_processing_a(a__     ,return_tensors='''pt''')

											self.assertTrue(
											    torch.allclose(encoded_images_with_method['''pixel_values''']     ,encoded_images['''pixel_values''']     ,atol=1E-4))





						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
											with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,'''r''') as f:
																_lowerCAmelCase:Optional[int]									=  json.loads(f.read())

											_lowerCAmelCase:Tuple									=  {'''image_id''': 3_9769, '''annotations''': target}

											# encode them
											_lowerCAmelCase:str									=  YolosImageProcessor.from_pretrained('''hustvl/yolos-small''')
											_lowerCAmelCase:Union[str, Any]									=  image_processing(images=a__     ,annotations=a__     ,return_tensors='''pt''')

											# verify pixel values
											_lowerCAmelCase:str									=  torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['''pixel_values'''].shape     ,a__)

											_lowerCAmelCase:Union[str, Any]									=  torch.tensor([0.2796, 0.3138, 0.3481])
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,a__     ,atol=1E-4))

											# verify area
											_lowerCAmelCase:Dict									=  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,a__))
											# verify boxes
											_lowerCAmelCase:List[str]									=  torch.Size([6, 4])
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,a__)
											_lowerCAmelCase:Optional[Any]									=  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,a__     ,atol=1E-3))
											# verify image_id
											_lowerCAmelCase:Dict									=  torch.tensor([3_9769])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,a__))
											# verify is_crowd
											_lowerCAmelCase:List[Any]									=  torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,a__))
											# verify class_labels
											_lowerCAmelCase:List[Any]									=  torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,a__))
											# verify orig_size
											_lowerCAmelCase:Union[str, Any]									=  torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,a__))
											# verify size
											_lowerCAmelCase:List[Any]									=  torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,a__))







						@slow
						def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
											with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,'''r''') as f:
																_lowerCAmelCase:Optional[Any]									=  json.loads(f.read())

											_lowerCAmelCase:Any									=  {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

											_lowerCAmelCase:List[Any]									=  pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

											# encode them
											_lowerCAmelCase:int									=  YolosImageProcessor(format='''coco_panoptic''')
											_lowerCAmelCase:List[Any]									=  image_processing(images=a__     ,annotations=a__     ,masks_path=a__     ,return_tensors='''pt''')

											# verify pixel values
											_lowerCAmelCase:List[str]									=  torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['''pixel_values'''].shape     ,a__)

											_lowerCAmelCase:Dict									=  torch.tensor([0.2796, 0.3138, 0.3481])
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,a__     ,atol=1E-4))

											# verify area
											_lowerCAmelCase:Dict									=  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,a__))
											# verify boxes
											_lowerCAmelCase:str									=  torch.Size([6, 4])
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,a__)
											_lowerCAmelCase:Union[str, Any]									=  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,a__     ,atol=1E-3))
											# verify image_id
											_lowerCAmelCase:Optional[int]									=  torch.tensor([3_9769])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,a__))
											# verify is_crowd
											_lowerCAmelCase:Dict									=  torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,a__))
											# verify class_labels
											_lowerCAmelCase:str									=  torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,a__))
											# verify masks
											_lowerCAmelCase:Any									=  82_2873
											self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,a__)
											# verify orig_size
											_lowerCAmelCase:Tuple									=  torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,a__))
											# verify size
											_lowerCAmelCase:Any									=  torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,a__))












"""simple docstring"""






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def 							UpperCAmelCase		(		snake_case			:					Tuple    ):
					_lowerCAmelCase:Tuple									=  384
					if "tiny" in model_name:
										_lowerCAmelCase:Any									=  [3, 3, 9, 3]
										_lowerCAmelCase:Union[str, Any]									=  [96, 192, 384, 768]
					if "small" in model_name:
										_lowerCAmelCase:List[str]									=  [3, 3, 27, 3]
										_lowerCAmelCase:Any									=  [96, 192, 384, 768]
					if "base" in model_name:
										_lowerCAmelCase:int									=  [3, 3, 27, 3]
										_lowerCAmelCase:int									=  [128, 256, 512, 1024]
										_lowerCAmelCase:Any									=  512
					if "large" in model_name:
										_lowerCAmelCase:Optional[int]									=  [3, 3, 27, 3]
										_lowerCAmelCase:List[str]									=  [192, 384, 768, 1536]
										_lowerCAmelCase:List[Any]									=  768
					if "xlarge" in model_name:
										_lowerCAmelCase:str									=  [3, 3, 27, 3]
										_lowerCAmelCase:Optional[int]									=  [256, 512, 1024, 2048]
										_lowerCAmelCase:Optional[int]									=  1024

					# set label information
					_lowerCAmelCase:Tuple									=  150
					_lowerCAmelCase:int									=  '''huggingface/label-files'''
					_lowerCAmelCase:Optional[int]									=  '''ade20k-id2label.json'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Dict									=  {int(snake_case    ): v for k, v in idalabel.items()}
					_lowerCAmelCase:Tuple									=  {v: k for k, v in idalabel.items()}

					_lowerCAmelCase:str									=  ConvNextConfig(
					    depths=snake_case      ,       hidden_sizes=snake_case      ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
					_lowerCAmelCase:Union[str, Any]									=  UperNetConfig(
					    backbone_config=snake_case      ,       auxiliary_in_channels=snake_case      ,       num_labels=snake_case      ,       idalabel=snake_case      ,       labelaid=snake_case      ,       )

					return config





def 							UpperCAmelCase		(		snake_case			:					List[Any]    ):
					_lowerCAmelCase:List[str]									=  []

					# fmt: off
					# stem
					rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')    )
					rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')    )
					rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')    )
					rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''')    )
					# stages
					for i in range(len(config.backbone_config.depths    )    ):
										for j in range(config.backbone_config.depths[i]    ):
															rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')    )
															rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')    )
										if i > 0:
															rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')    )
															rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')    )
															rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')    )
															rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')    )

										rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')    )
										rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')    )

					# decode head
					rename_keys.extend(
					    [
					        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
					        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
					        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
					        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
					    ]    )
					# fmt: on

					return rename_keys





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Tuple      ,       snake_case			:					Optional[int]    ):
					_lowerCAmelCase:int									=  dct.pop(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  val





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					int      ,       snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:Dict									=  {
					    '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
					    '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
					    '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
					    '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
					    '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
					}
					_lowerCAmelCase:Optional[Any]									=  model_name_to_url[model_name]
					_lowerCAmelCase:Optional[Any]									=  torch.hub.load_state_dict_from_url(snake_case      ,       map_location='''cpu'''    )['''state_dict''']

					_lowerCAmelCase:Tuple									=  get_upernet_config(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  UperNetForSemanticSegmentation(snake_case    )
					model.eval()

					# replace "bn" => "batch_norm"
					for key in state_dict.copy().keys():
										_lowerCAmelCase:str									=  state_dict.pop(snake_case    )
										if "bn" in key:
															_lowerCAmelCase:str									=  key.replace('''bn'''      ,       '''batch_norm'''    )
										_lowerCAmelCase:Optional[Any]									=  val

					# rename keys
					_lowerCAmelCase:int									=  create_rename_keys(snake_case    )
					for src, dest in rename_keys:
										rename_key(snake_case      ,       snake_case      ,       snake_case    )

					model.load_state_dict(snake_case    )

					# verify on image
					_lowerCAmelCase:Optional[int]									=  '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
					_lowerCAmelCase:Optional[int]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    ).convert('''RGB'''    )

					_lowerCAmelCase:int									=  SegformerImageProcessor()
					_lowerCAmelCase:Optional[int]									=  processor(snake_case      ,       return_tensors='''pt'''    ).pixel_values

					with torch.no_grad():
										_lowerCAmelCase:str									=  model(snake_case    )

					if model_name == "upernet-convnext-tiny":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor(
										    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]    )
					elif model_name == "upernet-convnext-small":
										_lowerCAmelCase:List[str]									=  torch.tensor(
										    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]    )
					elif model_name == "upernet-convnext-base":
										_lowerCAmelCase:Dict									=  torch.tensor(
										    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]    )
					elif model_name == "upernet-convnext-large":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor(
										    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]    )
					elif model_name == "upernet-convnext-xlarge":
										_lowerCAmelCase:List[str]									=  torch.tensor(
										    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]    )
					print('''Logits:'''      ,       outputs.logits[0, 0, :3, :3]    )
					assert torch.allclose(outputs.logits[0, 0, :3, :3]      ,       snake_case      ,       atol=1e-4    )
					print('''Looks ok!'''    )

					if pytorch_dump_folder_path is not None:
										print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
										model.save_pretrained(snake_case    )
										print(F'Saving processor to {pytorch_dump_folder_path}'    )
										processor.save_pretrained(snake_case    )

					if push_to_hub:
										print(F'Pushing model and processor for {model_name} to hub'    )
										model.push_to_hub(F'openmmlab/{model_name}'    )
										processor.push_to_hub(F'openmmlab/{model_name}'    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''upernet-convnext-tiny''',
				    type=str,
				    choices=[F"upernet-convnext-{size}" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
				    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				UpperCamelCase__						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






