




"""simple docstring"""






import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
				from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor




class   a__			(       UpperCamelCase_  ):





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




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  self.config_class(**self.inputs_dict)
											self.parent.assertTrue(hasattr(a__     ,'''hidden_sizes'''))
											self.parent.assertTrue(hasattr(a__     ,'''neck_hidden_sizes'''))
											self.parent.assertTrue(hasattr(a__     ,'''num_attention_heads'''))




class   a__			:





						def __init__(							self      :	Any     ,a__      :	Optional[Any]     ,a__      :	Optional[Any]=13     ,a__      :	Optional[Any]=32     ,a__      :	Union[str, Any]=2     ,a__      :	Dict=3     ,a__      :	Union[str, Any]=640     ,a__      :	List[Any]=4     ,a__      :	Optional[Any]="silu"     ,a__      :	Dict=3     ,a__      :	str=32     ,a__      :	int=0.1     ,a__      :	List[Any]=0.1     ,a__      :	int=0.1     ,a__      :	str=0.02     ,a__      :	Tuple=True     ,a__      :	Any=True     ,a__      :	Dict=10     ,a__      :	List[str]=None     ,)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:str									=  parent
											_lowerCAmelCase:str									=  batch_size
											_lowerCAmelCase:Dict									=  image_size
											_lowerCAmelCase:Any									=  patch_size
											_lowerCAmelCase:List[Any]									=  num_channels
											_lowerCAmelCase:List[str]									=  last_hidden_size
											_lowerCAmelCase:Union[str, Any]									=  num_attention_heads
											_lowerCAmelCase:List[Any]									=  hidden_act
											_lowerCAmelCase:int									=  conv_kernel_size
											_lowerCAmelCase:Union[str, Any]									=  output_stride
											_lowerCAmelCase:List[Any]									=  hidden_dropout_prob
											_lowerCAmelCase:Optional[int]									=  attention_probs_dropout_prob
											_lowerCAmelCase:Tuple									=  classifier_dropout_prob
											_lowerCAmelCase:str									=  use_labels
											_lowerCAmelCase:Optional[Any]									=  is_training
											_lowerCAmelCase:str									=  num_labels
											_lowerCAmelCase:Tuple									=  initializer_range
											_lowerCAmelCase:List[Any]									=  scope





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




											"""simple docstring"""







											_lowerCAmelCase:Any									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											_lowerCAmelCase:int									=  None
											_lowerCAmelCase:str									=  None
											if self.use_labels:
																_lowerCAmelCase:Any									=  ids_tensor([self.batch_size]     ,self.num_labels)
																_lowerCAmelCase:Union[str, Any]									=  ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels)

											_lowerCAmelCase:Tuple									=  self.get_config()

											return config, pixel_values, labels, pixel_labels





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




											"""simple docstring"""







											return MobileViTConfig(
											    image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,num_attention_heads=self.num_attention_heads     ,hidden_act=self.hidden_act     ,conv_kernel_size=self.conv_kernel_size     ,output_stride=self.output_stride     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,classifier_dropout_prob=self.classifier_dropout_prob     ,initializer_range=self.initializer_range     ,)





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	Dict     ,a__      :	Union[str, Any]     ,a__      :	Optional[int]     ,a__      :	int)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  MobileViTModel(config=a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:Tuple									=  model(a__)
											self.parent.assertEqual(
											    result.last_hidden_state.shape     ,(
											        self.batch_size,
											        self.last_hidden_size,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     ,)





						def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	int     ,a__      :	int     ,a__      :	str     ,a__      :	Optional[Any])						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:int									=  self.num_labels
											_lowerCAmelCase:Optional[int]									=  MobileViTForImageClassification(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:Dict									=  model(a__     ,labels=a__)
											self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	Union[str, Any]     ,a__      :	Tuple     ,a__      :	List[str]     ,a__      :	Tuple)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.num_labels
											_lowerCAmelCase:Dict									=  MobileViTForSemanticSegmentation(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:Tuple									=  model(a__)
											self.parent.assertEqual(
											    result.logits.shape     ,(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     ,)
											_lowerCAmelCase:int									=  model(a__     ,labels=a__)
											self.parent.assertEqual(
											    result.logits.shape     ,(
											        self.batch_size,
											        self.num_labels,
											        self.image_size // self.output_stride,
											        self.image_size // self.output_stride,
											    )     ,)







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




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  config_and_inputs
											_lowerCAmelCase:Optional[Any]									=  {'''pixel_values''': pixel_values}
											return config, inputs_dict




@require_torch
class   a__			(       UpperCamelCase_					,	UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = (
						    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						snake_case__             = (
						    {
						        '''feature-extraction''': MobileViTModel,
						        '''image-classification''': MobileViTForImageClassification,
						        '''image-segmentation''': MobileViTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						snake_case__             = False
						snake_case__             = False
						snake_case__             = False
						snake_case__             = False





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




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  MobileViTModelTester(self)
											_lowerCAmelCase:Optional[Any]									=  MobileViTConfigTester(self     ,config_class=a__     ,has_text_modality=a__)





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




											"""simple docstring"""







											self.config_tester.run_common_tests()





						@unittest.skip(reason='''MobileViT does not use inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Tuple)						->       str:




											"""simple docstring"""







											pass





						@unittest.skip(reason='''MobileViT does not support input and output embeddings''')
						def       __UpperCamelCase     (							self      :	int)						->       List[Any]:




											"""simple docstring"""







											pass





						@unittest.skip(reason='''MobileViT does not output attentions''')
						def       __UpperCamelCase     (							self      :	List[Any])						->       int:




											"""simple docstring"""







											pass





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




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:int									=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																_lowerCAmelCase:Optional[Any]									=  model_class(a__)
																_lowerCAmelCase:str									=  inspect.signature(model.forward)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_lowerCAmelCase:Tuple									=  [*signature.parameters.keys()]

																_lowerCAmelCase:Optional[Any]									=  ['''pixel_values''']
																self.assertListEqual(arg_names[:1]     ,a__)





						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
						def       __UpperCamelCase     (							self      :	Dict)						->       Optional[Any]:




											"""simple docstring"""







											pass





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




											"""simple docstring"""







											_lowerCAmelCase:str									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a__)





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




											"""simple docstring"""







											def check_hidden_states_output(a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	List[str]):
																_lowerCAmelCase:str									=  model_class(a__)
																model.to(a__)
																model.eval()

																with torch.no_grad():
																					_lowerCAmelCase:List[Any]									=  model(**self._prepare_for_class(a__     ,a__))

																_lowerCAmelCase:Tuple									=  outputs.hidden_states

																_lowerCAmelCase:int									=  5
																self.assertEqual(len(a__)     ,a__)

																# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																_lowerCAmelCase:Optional[int]									=  2
																for i in range(len(a__)):
																					self.assertListEqual(
																					    list(hidden_states[i].shape[-2:])     ,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]     ,)
																					divisor *= 2

																self.assertEqual(self.model_tester.output_stride     ,divisor // 2)

											_lowerCAmelCase							,   _lowerCAmelCase:Optional[int]									=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																_lowerCAmelCase:Union[str, Any]									=  True
																check_hidden_states_output(a__     ,a__     ,a__)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_lowerCAmelCase:Optional[Any]									=  True

																check_hidden_states_output(a__     ,a__     ,a__)





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




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*a__)







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




											"""simple docstring"""







											for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_lowerCAmelCase:List[Any]									=  MobileViTModel.from_pretrained(a__)
																self.assertIsNotNone(a__)





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Union[str, Any]									=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
					return image





@require_torch
@require_vision
class   a__			(       unittest.TestCase  ):





						@cached_property
						def       __UpperCamelCase     (							self      :	Tuple)						->       Any:




											"""simple docstring"""







											return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''') if is_vision_available() else None





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




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''').to(a__)

											_lowerCAmelCase:Any									=  self.default_image_processor
											_lowerCAmelCase:Optional[Any]									=  prepare_img()
											_lowerCAmelCase:List[Any]									=  image_processor(images=a__     ,return_tensors='''pt''').to(a__)

											# forward pass
											with torch.no_grad():
																_lowerCAmelCase:List[Any]									=  model(**a__)

											# verify the logits
											_lowerCAmelCase:List[Any]									=  torch.Size((1, 1000))
											self.assertEqual(outputs.logits.shape     ,a__)

											_lowerCAmelCase:Tuple									=  torch.tensor([-1.9364, -1.2327, -0.4653]).to(a__)

											self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,a__     ,atol=1E-4))





						@slow
						def       __UpperCamelCase     (							self      :	Tuple)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:str									=  MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
											_lowerCAmelCase:int									=  model.to(a__)

											_lowerCAmelCase:List[str]									=  MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

											_lowerCAmelCase:int									=  prepare_img()
											_lowerCAmelCase:Any									=  image_processor(images=a__     ,return_tensors='''pt''').to(a__)

											# forward pass
											with torch.no_grad():
																_lowerCAmelCase:List[Any]									=  model(**a__)
											_lowerCAmelCase:List[str]									=  outputs.logits

											# verify the logits
											_lowerCAmelCase:Any									=  torch.Size((1, 21, 32, 32))
											self.assertEqual(logits.shape     ,a__)

											_lowerCAmelCase:Any									=  torch.tensor(
											    [
											        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
											        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
											        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
											    ]     ,device=a__     ,)

											self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,a__     ,atol=1E-4))







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




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
											_lowerCAmelCase:Any									=  model.to(a__)

											_lowerCAmelCase:Any									=  MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

											_lowerCAmelCase:Union[str, Any]									=  prepare_img()
											_lowerCAmelCase:Union[str, Any]									=  image_processor(images=a__     ,return_tensors='''pt''').to(a__)

											# forward pass
											with torch.no_grad():
																_lowerCAmelCase:str									=  model(**a__)

											_lowerCAmelCase:List[str]									=  outputs.logits.detach().cpu()

											_lowerCAmelCase:Tuple									=  image_processor.post_process_semantic_segmentation(outputs=a__     ,target_sizes=[(50, 60)])
											_lowerCAmelCase:int									=  torch.Size((50, 60))
											self.assertEqual(segmentation[0].shape     ,a__)

											_lowerCAmelCase:List[Any]									=  image_processor.post_process_semantic_segmentation(outputs=a__)
											_lowerCAmelCase:str									=  torch.Size((32, 32))
											self.assertEqual(segmentation[0].shape     ,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)






