
import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
					from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor







class     __A:




				def __init__(  self	,  _snake_case	,  _snake_case=2	,  _snake_case=32	,  _snake_case=16	,  _snake_case=3	,  _snake_case=True	,  _snake_case=True	,  _snake_case=32	,  _snake_case=4	,  _snake_case=[0, 1, 2, 3]	,  _snake_case=4	,  _snake_case=37	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=0.02	,  _snake_case=3	,  _snake_case=False	,  _snake_case=None	,  )					->   Any:


								'''simple docstring'''




								__a    							=					parent
								__a    							=					batch_size
								__a    							=					image_size
								__a    							=					patch_size
								__a    							=					num_channels
								__a    							=					is_training
								__a    							=					use_labels
								__a    							=					hidden_size
								__a    							=					num_hidden_layers
								__a    							=					backbone_out_indices
								__a    							=					num_attention_heads
								__a    							=					intermediate_size
								__a    							=					hidden_act
								__a    							=					hidden_dropout_prob
								__a    							=					attention_probs_dropout_prob
								__a    							=					initializer_range
								__a    							=					num_labels
								__a    							=					scope
								__a    							=					is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								__a    							=					(image_size // patch_size) ** 2
								__a    							=					num_patches + 1




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


								'''simple docstring'''




								__a    							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

								__a    							=					None
								if self.use_labels:
												__a    							=					ids_tensor([self.batch_size, self.image_size, self.image_size]	,  self.num_labels       )

								__a    							=					self.get_config()

								return config, pixel_values, labels




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


								'''simple docstring'''




								return DPTConfig(
								    image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  backbone_out_indices=self.backbone_out_indices	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  is_decoder=_snake_case	,  initializer_range=self.initializer_range	,  is_hybrid=self.is_hybrid	,  )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case       )					->   Optional[int]:


								'''simple docstring'''




								__a    							=					DPTModel(config=_snake_case       )
								model.to(_snake_case       )
								model.eval()
								__a    							=					model(_snake_case       )
								self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.seq_length, self.hidden_size)       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case       )					->   str:


								'''simple docstring'''




								__a    							=					self.num_labels
								__a    							=					DPTForDepthEstimation(_snake_case       )
								model.to(_snake_case       )
								model.eval()
								__a    							=					model(_snake_case       )
								self.parent.assertEqual(result.predicted_depth.shape	,  (self.batch_size, self.image_size, self.image_size)       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					self.num_labels
								__a    							=					DPTForSemanticSegmentation(_snake_case       )
								model.to(_snake_case       )
								model.eval()
								__a    							=					model(_snake_case	,  labels=_snake_case       )
								self.parent.assertEqual(
								    result.logits.shape	,  (self.batch_size, self.num_labels, self.image_size, self.image_size)       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								__a    							=					self.prepare_config_and_inputs()
								__a							,  __a							,  __a    							=					config_and_inputs
								__a    							=					{'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class     __A(				a						,     a						,     unittest.TestCase    ):
				snake_case_		     =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				snake_case_		     =					(
				    {
				        '''depth-estimation''': DPTForDepthEstimation,
				        '''feature-extraction''': DPTModel,
				        '''image-segmentation''': DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				snake_case_		     =					False
				snake_case_		     =					False
				snake_case_		     =					False




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a    							=					DPTModelTester(self       )
								__a    							=					ConfigTester(self	,  config_class=_snake_case	,  has_text_modality=_snake_case	,  hidden_size=37       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								self.config_tester.run_common_tests()




				@unittest.skip(reason='''DPT does not use inputs_embeds'''       )
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__a    							=					model_class(_snake_case       )
												self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)       )
												__a    							=					model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(_snake_case	,  nn.Linear       )       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__a    							=					model_class(_snake_case       )
												__a    							=					inspect.signature(model.forward       )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												__a    							=					[*signature.parameters.keys()]

												__a    							=					['''pixel_values''']
												self.assertListEqual(arg_names[:1]	,  _snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case       )




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


								'''simple docstring'''




								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

												__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()
												__a    							=					True

												if model_class in get_values(_snake_case       ):
																continue

												__a    							=					model_class(_snake_case       )
												model.to(_snake_case       )
												model.train()
												__a    							=					self._prepare_for_class(_snake_case	,  _snake_case	,  return_labels=_snake_case       )
												__a    							=					model(**_snake_case       ).loss
												loss.backward()




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

												__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()
												__a    							=					False
												__a    							=					True

												if model_class in get_values(_snake_case       ) or not model_class.supports_gradient_checkpointing:
																continue
												__a    							=					model_class(_snake_case       )
												model.to(_snake_case       )
												model.gradient_checkpointing_enable()
												model.train()
												__a    							=					self._prepare_for_class(_snake_case	,  _snake_case	,  return_labels=_snake_case       )
												__a    							=					model(**_snake_case       ).loss
												loss.backward()




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()

								__a    							=					_config_zero_init(_snake_case       )
								for model_class in self.all_model_classes:
												__a    							=					model_class(config=_snake_case       )
												# Skip the check for the backbone
												__a    							=					[]
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				__a    							=					[F"""{name}.{key}""" for key in module.state_dict().keys()]
																				break

												for name, param in model.named_parameters():
																if param.requires_grad:
																				if name in backbone_params:
																								continue
																				self.assertIn(
																				    ((param.data.mean() * 1E9).round() / 1E9).item()	,  [0.0, 1.0]	,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""	,  )




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


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__a    							=					DPTModel.from_pretrained(_snake_case       )
												self.assertIsNotNone(_snake_case       )



def 					__lowerCAmelCase    (    ) ->						Tuple:
				__a    							=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
				return image







@require_torch
@require_vision
@slow
class     __A(				unittest.TestCase    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								__a    							=					DPTImageProcessor.from_pretrained('''Intel/dpt-large'''       )
								__a    							=					DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''       ).to(_snake_case       )

								__a    							=					prepare_img()
								__a    							=					image_processor(images=_snake_case	,  return_tensors='''pt'''       ).to(_snake_case       )

								# forward pass
								with torch.no_grad():
												__a    							=					model(**_snake_case       )
												__a    							=					outputs.predicted_depth

								# verify the predicted depth
								__a    							=					torch.Size((1, 384, 384)       )
								self.assertEqual(predicted_depth.shape	,  _snake_case       )

								__a    							=					torch.tensor(
								    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]       ).to(_snake_case       )

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''       )
								__a    							=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''       ).to(_snake_case       )

								__a    							=					prepare_img()
								__a    							=					image_processor(images=_snake_case	,  return_tensors='''pt'''       ).to(_snake_case       )

								# forward pass
								with torch.no_grad():
												__a    							=					model(**_snake_case       )

								# verify the logits
								__a    							=					torch.Size((1, 150, 480, 480)       )
								self.assertEqual(outputs.logits.shape	,  _snake_case       )

								__a    							=					torch.tensor(
								    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]       ).to(_snake_case       )

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




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


								'''simple docstring'''




								__a    							=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''       )
								__a    							=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''       ).to(_snake_case       )

								__a    							=					prepare_img()
								__a    							=					image_processor(images=_snake_case	,  return_tensors='''pt'''       ).to(_snake_case       )

								# forward pass
								with torch.no_grad():
												__a    							=					model(**_snake_case       )

								__a    							=					outputs.logits.detach().cpu()

								__a    							=					image_processor.post_process_semantic_segmentation(outputs=_snake_case	,  target_sizes=[(500, 300)]       )
								__a    							=					torch.Size((500, 300)       )
								self.assertEqual(segmentation[0].shape	,  _snake_case       )

								__a    							=					image_processor.post_process_semantic_segmentation(outputs=_snake_case       )
								__a    							=					torch.Size((480, 480)       )
								self.assertEqual(segmentation[0].shape	,  _snake_case       )

import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A  :			Tuple     				=				logging.get_logger(__name__)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				__a    							=					ConvNextVaConfig()

				if "atto" in checkpoint_url:
								__a    							=					[2, 2, 6, 2]
								__a    							=					[40, 80, 160, 320]
				if "femto" in checkpoint_url:
								__a    							=					[2, 2, 6, 2]
								__a    							=					[48, 96, 192, 384]
				if "pico" in checkpoint_url:
								__a    							=					[2, 2, 6, 2]
								__a    							=					[64, 128, 256, 512]
				if "nano" in checkpoint_url:
								__a    							=					[2, 2, 8, 2]
								__a    							=					[80, 160, 320, 640]
				if "tiny" in checkpoint_url:
								__a    							=					[3, 3, 9, 3]
								__a    							=					[96, 192, 384, 768]
				if "base" in checkpoint_url:
								__a    							=					[3, 3, 27, 3]
								__a    							=					[128, 256, 512, 1024]
				if "large" in checkpoint_url:
								__a    							=					[3, 3, 27, 3]
								__a    							=					[192, 384, 768, 1536]
				if "huge" in checkpoint_url:
								__a    							=					[3, 3, 27, 3]
								__a    							=					[352, 704, 1408, 2816]

				__a    							=					1000
				__a    							=					'''imagenet-1k-id2label.json'''
				__a    							=					(1, 1000)

				__a    							=					'''huggingface/label-files'''
				__a    							=					num_labels
				__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
				__a    							=					{int(a__						): v for k, v in idalabel.items()}

				__a    							=					idalabel
				__a    							=					{v: k for k, v in idalabel.items()}
				__a    							=					hidden_sizes
				__a    							=					depths

				return config, expected_shape


def 					__lowerCAmelCase    (    a__						) ->						str:
				if "downsample_layers.0.0" in name:
								__a    							=					name.replace('''downsample_layers.0.0'''		,					'''embeddings.patch_embeddings'''						)
				if "downsample_layers.0.1" in name:
								__a    							=					name.replace('''downsample_layers.0.1'''		,					'''embeddings.norm'''						)  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								__a    							=					name.replace('''downsample_layers.1.0'''		,					'''stages.1.downsampling_layer.0'''						)
				if "downsample_layers.1.1" in name:
								__a    							=					name.replace('''downsample_layers.1.1'''		,					'''stages.1.downsampling_layer.1'''						)
				if "downsample_layers.2.0" in name:
								__a    							=					name.replace('''downsample_layers.2.0'''		,					'''stages.2.downsampling_layer.0'''						)
				if "downsample_layers.2.1" in name:
								__a    							=					name.replace('''downsample_layers.2.1'''		,					'''stages.2.downsampling_layer.1'''						)
				if "downsample_layers.3.0" in name:
								__a    							=					name.replace('''downsample_layers.3.0'''		,					'''stages.3.downsampling_layer.0'''						)
				if "downsample_layers.3.1" in name:
								__a    							=					name.replace('''downsample_layers.3.1'''		,					'''stages.3.downsampling_layer.1'''						)
				if "stages" in name and "downsampling_layer" not in name:
								# stages.0.0. for instance should be renamed to stages.0.layers.0.
								__a    							=					name[: len('''stages.0'''						)] + '''.layers''' + name[len('''stages.0'''						) :]
				if "gamma" in name:
								__a    							=					name.replace('''gamma'''		,					'''weight'''						)
				if "beta" in name:
								__a    							=					name.replace('''beta'''		,					'''bias'''						)
				if "stages" in name:
								__a    							=					name.replace('''stages'''		,					'''encoder.stages'''						)
				if "norm" in name:
								__a    							=					name.replace('''norm'''		,					'''layernorm'''						)
				if "head" in name:
								__a    							=					name.replace('''head'''		,					'''classifier'''						)

				return name


def 					__lowerCAmelCase    (    ) ->						Tuple:
				__a    							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a    							=					Image.open(requests.get(a__		,					stream=a__						).raw						)
				return im


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				if "224" in checkpoint_url:
								__a    							=					224
								__a    							=					224 / 256
				elif "384" in checkpoint_url:
								__a    							=					384
								__a    							=					None
				else:
								__a    							=					512
								__a    							=					None

				return ConvNextImageProcessor(
				    size=a__		,					crop_pct=a__		,					image_mean=[0.485, 0.456, 0.406]		,					image_std=[0.229, 0.224, 0.225]		,					resample=PILImageResampling.BICUBIC		,					)


@torch.no_grad()
def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						List[Any]:
				print('''Downloading original model from checkpoint...'''						)
				# define ConvNeXTV2 configuration based on URL
				__a							,  __a    							=					get_convnextva_config(a__						)
				# load original state_dict from URL
				__a    							=					torch.hub.load_state_dict_from_url(a__						)['''model''']

				print('''Converting model parameters...'''						)
				# rename keys
				for key in state_dict.copy().keys():
								__a    							=					state_dict.pop(a__						)
								__a    							=					val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								__a    							=					state_dict.pop(a__						)
								if not key.startswith('''classifier'''						):
												__a    							=					'''convnextv2.''' + key
								__a    							=					val

				# load HuggingFace model
				__a    							=					ConvNextVaForImageClassification(a__						)
				model.load_state_dict(a__						)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				__a    							=					convert_preprocessor(a__						)
				__a    							=					preprocessor(images=prepare_img()		,					return_tensors='''pt'''						)
				__a    							=					model(**a__						).logits

				# note: the logits below were obtained without center cropping
				if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								__a    							=					torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								__a    							=					torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								__a    							=					torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								__a    							=					torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								__a    							=					torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								__a    							=					torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								__a    							=					torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								__a    							=					torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								__a    							=					torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								__a    							=					torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]						)
				else:
								raise ValueError(F"""Unknown URL: {checkpoint_url}"""						)

				assert torch.allclose(logits[0, :5]		,					a__		,					atol=1e-3						)
				assert logits.shape == expected_shape
				print('''Model outputs match the original results!'''						)

				if save_model:
								print('''Saving model to local...'''						)
								# Create folder to save model
								if not os.path.isdir(a__						):
												os.mkdir(a__						)

								model.save_pretrained(a__						)
								preprocessor.save_pretrained(a__						)

				__a    							=					'''convnextv2'''
				if "atto" in checkpoint_url:
								model_name += "-atto"
				if "femto" in checkpoint_url:
								model_name += "-femto"
				if "pico" in checkpoint_url:
								model_name += "-pico"
				if "nano" in checkpoint_url:
								model_name += "-nano"
				elif "tiny" in checkpoint_url:
								model_name += "-tiny"
				elif "base" in checkpoint_url:
								model_name += "-base"
				elif "large" in checkpoint_url:
								model_name += "-large"
				elif "huge" in checkpoint_url:
								model_name += "-huge"
				if "22k" in checkpoint_url and "1k" not in checkpoint_url:
								model_name += "-22k"
				elif "22k" in checkpoint_url and "1k" in checkpoint_url:
								model_name += "-22k-1k"
				elif "1k" in checkpoint_url:
								model_name += "-1k"
				if "224" in checkpoint_url:
								model_name += "-224"
				elif "384" in checkpoint_url:
								model_name += "-384"
				elif "512" in checkpoint_url:
								model_name += "-512"

				if push_to_hub:
								print(F"""Pushing {model_name} to the hub..."""						)
								model.push_to_hub(a__						)
								preprocessor.push_to_hub(a__						)


if __name__ == "__main__":
					A  :			Optional[int]     				=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--checkpoint_url',
					    default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
					    type=str,
					    help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default='model',
					    type=str,
					    help='Path to the output PyTorch model directory.',
					)
					parser.add_argument('--save_model', action='store_true', help='Save model to local')
					parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

					A  :			int     				=				parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)