




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 DetaImageProcessor







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def __init__(			self      ,snake_case__      ,snake_case__=7      ,snake_case__=3      ,snake_case__=30      ,snake_case__=400      ,snake_case__=True      ,snake_case__=None      ,snake_case__=True      ,snake_case__=[0.5, 0.5, 0.5]      ,snake_case__=[0.5, 0.5, 0.5]      ,snake_case__=True      ,snake_case__=1 / 255      ,snake_case__=True      ,):
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							SCREAMING_SNAKE_CASE_   : List[str]					  =      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      parent
							SCREAMING_SNAKE_CASE_   : str					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_channels
							SCREAMING_SNAKE_CASE_   : Tuple					  =      min_resolution
							SCREAMING_SNAKE_CASE_   : Any					  =      max_resolution
							SCREAMING_SNAKE_CASE_   : str					  =      do_resize
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      do_normalize
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image_mean
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      image_std
							SCREAMING_SNAKE_CASE_   : Tuple					  =      do_rescale
							SCREAMING_SNAKE_CASE_   : Any					  =      rescale_factor
							SCREAMING_SNAKE_CASE_   : Any					  =      do_pad






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

							else:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []
										for image in image_inputs:
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      self.get_expected_values([image] )
													expected_values.append((expected_height, expected_width) )
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      max(snake_case__      ,key=lambda snake_case__ : item[0] )[0]
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      max(snake_case__      ,key=lambda snake_case__ : item[1] )[1]

							return expected_height, expected_width







@require_torch
@require_vision
class 				lowerCAmelCase_       (					lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Dict						=     DetaImageProcessor if is_vision_available() else None






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      DetaImageProcessingTester(self )






				@property
				def 		snake_case	(			self ):
							return self.image_processor_tester.prepare_image_processor_dict()






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(snake_case__      ,'image_mean' ) )
							self.assertTrue(hasattr(snake_case__      ,'image_std' ) )
							self.assertTrue(hasattr(snake_case__      ,'do_normalize' ) )
							self.assertTrue(hasattr(snake_case__      ,'do_resize' ) )
							self.assertTrue(hasattr(snake_case__      ,'do_rescale' ) )
							self.assertTrue(hasattr(snake_case__      ,'do_pad' ) )
							self.assertTrue(hasattr(snake_case__      ,'size' ) )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : 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      ,snake_case__ )






				def 		snake_case	(			self ):
							pass






				def 		snake_case	(			self ):
							# Initialize image_processing
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							SCREAMING_SNAKE_CASE_   : List[str]					  =      prepare_image_inputs(self.image_processor_tester      ,equal_resolution=snake_case__ )
							for image in image_inputs:
										self.assertIsInstance(snake_case__      ,Image.Image )

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

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : str					  =      self.image_processor_tester.get_expected_values(snake_case__ )

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

							# Test batched
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Any					  =      self.image_processor_tester.get_expected_values(snake_case__      ,batched=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image_processing(snake_case__      ,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 		snake_case	(			self ):
							# Initialize image_processing
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      prepare_image_inputs(self.image_processor_tester      ,equal_resolution=snake_case__      ,numpify=snake_case__ )
							for image in image_inputs:
										self.assertIsInstance(snake_case__      ,np.ndarray )

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

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.image_processor_tester.get_expected_values(snake_case__ )

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

							# Test batched
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image_processing(snake_case__      ,return_tensors='pt' ).pixel_values

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      self.image_processor_tester.get_expected_values(snake_case__      ,batched=snake_case__ )

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






				def 		snake_case	(			self ):
							# Initialize image_processing
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE_   : int					  =      prepare_image_inputs(self.image_processor_tester      ,equal_resolution=snake_case__      ,torchify=snake_case__ )
							for image in image_inputs:
										self.assertIsInstance(snake_case__      ,torch.Tensor )

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

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      self.image_processor_tester.get_expected_values(snake_case__ )

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

							# Test batched
							SCREAMING_SNAKE_CASE_   : List[str]					  =      image_processing(snake_case__      ,return_tensors='pt' ).pixel_values

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      self.image_processor_tester.get_expected_values(snake_case__      ,batched=snake_case__ )

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






				@slow
				def 		snake_case	(			self ):
							# prepare image and target
							SCREAMING_SNAKE_CASE_   : Any					  =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,'r' ) as f:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      json.loads(f.read() )

							SCREAMING_SNAKE_CASE_   : Any					  =      {'image_id': 39769, 'annotations': target}

							# encode them
							SCREAMING_SNAKE_CASE_   : int					  =      DetaImageProcessor()
							SCREAMING_SNAKE_CASE_   : int					  =      image_processing(images=snake_case__      ,annotations=snake_case__      ,return_tensors='pt' )

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

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

							# verify area
							SCREAMING_SNAKE_CASE_   : Dict					  =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,snake_case__ ) )
							# verify boxes
							SCREAMING_SNAKE_CASE_   : int					  =      torch.Size([6, 4] )
							self.assertEqual(encoding['labels'][0]['boxes'].shape      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : str					  =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,snake_case__      ,atol=1E-3 ) )
							# verify image_id
							SCREAMING_SNAKE_CASE_   : int					  =      torch.tensor([39769] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,snake_case__ ) )
							# verify is_crowd
							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.tensor([0, 0, 0, 0, 0, 0] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,snake_case__ ) )
							# verify class_labels
							SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.tensor([75, 75, 63, 65, 17, 17] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,snake_case__ ) )
							# verify orig_size
							SCREAMING_SNAKE_CASE_   : int					  =      torch.tensor([480, 640] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,snake_case__ ) )
							# verify size
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.tensor([800, 1066] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,snake_case__ ) )






				@slow
				def 		snake_case	(			self ):
							# prepare image, target and masks_path
							SCREAMING_SNAKE_CASE_   : 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:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      json.loads(f.read() )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

							# encode them
							SCREAMING_SNAKE_CASE_   : Any					  =      DetaImageProcessor(format='coco_panoptic' )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      image_processing(images=snake_case__      ,annotations=snake_case__      ,masks_path=snake_case__      ,return_tensors='pt' )

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

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.tensor([0.2796, 0.3138, 0.3481] )
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,snake_case__      ,atol=1E-4 ) )

							# verify area
							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,snake_case__ ) )
							# verify boxes
							SCREAMING_SNAKE_CASE_   : Dict					  =      torch.Size([6, 4] )
							self.assertEqual(encoding['labels'][0]['boxes'].shape      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,snake_case__      ,atol=1E-3 ) )
							# verify image_id
							SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.tensor([39769] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,snake_case__ ) )
							# verify is_crowd
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.tensor([0, 0, 0, 0, 0, 0] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,snake_case__ ) )
							# verify class_labels
							SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.tensor([17, 17, 63, 75, 75, 93] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,snake_case__ ) )
							# verify masks
							SCREAMING_SNAKE_CASE_   : Any					  =      822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,snake_case__ )
							# verify orig_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.tensor([480, 640] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,snake_case__ ) )
							# verify size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.tensor([800, 1066] )
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,snake_case__ ) )










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   __lowerCamelCase   (				__a  :int       )						->							int:






		"""simple docstring"""

		A__     =  3_8_4
		if "tiny" in model_name:
				A__     =  [3, 3, 9, 3]
				A__     =  [9_6, 1_9_2, 3_8_4, 7_6_8]
		if "small" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [9_6, 1_9_2, 3_8_4, 7_6_8]
		if "base" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
				A__     =  5_1_2
		if "large" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
				A__     =  7_6_8
		if "xlarge" in model_name:
				A__     =  [3, 3, 2_7, 3]
				A__     =  [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
				A__     =  1_0_2_4

		# set label information
		A__     =  1_5_0
		A__     =  """huggingface/label-files"""
		A__     =  """ade20k-id2label.json"""
		A__     =  json.load(open(hf_hub_download(__a       ,	__a       ,	repo_type="""dataset"""       )       ,	"""r"""       )       )
		A__     =  {int(__a       ): v for k, v in idalabel.items()}
		A__     =  {v: k for k, v in idalabel.items()}

		A__     =  ConvNextConfig(
		    depths=__a       ,	hidden_sizes=__a       ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
		A__     =  UperNetConfig(
		    backbone_config=__a       ,	auxiliary_in_channels=__a       ,	num_labels=__a       ,	idalabel=__a       ,	labelaid=__a       ,	)

		return config







def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							List[Any]:






		"""simple docstring"""

		A__     =  []

		# 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   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :List[str]       ,	__a  :str       )						->							str:






		"""simple docstring"""

		A__     =  dct.pop(__a       )
		A__     =  val







def   __lowerCamelCase   (				__a  :Any       ,	__a  :int       ,	__a  :Any       )						->							List[Any]:






		"""simple docstring"""

		A__     =  {
		    """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""",
		}
		A__     =  model_name_to_url[model_name]
		A__     =  torch.hub.load_state_dict_from_url(__a       ,	map_location="""cpu"""       )["""state_dict"""]

		A__     =  get_upernet_config(__a       )
		A__     =  UperNetForSemanticSegmentation(__a       )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				A__     =  state_dict.pop(__a       )
				if "bn" in key:
						A__     =  key.replace("""bn"""       ,	"""batch_norm"""       )
				A__     =  val

		# rename keys
		A__     =  create_rename_keys(__a       )
		for src, dest in rename_keys:
				rename_key(__a       ,	__a       ,	__a       )

		model.load_state_dict(__a       )

		# verify on image
		A__     =  """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
		A__     =  Image.open(requests.get(__a       ,	stream=__a       ).raw       ).convert("""RGB"""       )

		A__     =  SegformerImageProcessor()
		A__     =  processor(__a       ,	return_tensors="""pt"""       ).pixel_values

		with torch.no_grad():
				A__     =  model(__a       )

		if model_name == "upernet-convnext-tiny":
				A__     =  torch.tensor(
				    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]       )
		elif model_name == "upernet-convnext-small":
				A__     =  torch.tensor(
				    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]       )
		elif model_name == "upernet-convnext-base":
				A__     =  torch.tensor(
				    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]       )
		elif model_name == "upernet-convnext-large":
				A__     =  torch.tensor(
				    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]       )
		elif model_name == "upernet-convnext-xlarge":
				A__     =  torch.tensor(
				    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]       )
		print("""Logits:"""       ,	outputs.logits[0, 0, :3, :3]       )
		assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,	__a       ,	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(__a       )
				print(F'Saving processor to {pytorch_dump_folder_path}'       )
				processor.save_pretrained(__a       )

		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__":
					A       :   Optional[int]					= 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.'''
					)

					A       :   str					= parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



