




'''simple docstring'''







import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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 transformers import UperNetForSemanticSegmentation
			from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class _lowercase			:




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any=13    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Optional[Any]=3    ,							UpperCamelCase__:       int=4    ,							UpperCamelCase__:       List[str]=[10, 20, 30, 40]    ,							UpperCamelCase__:       Tuple=[2, 2, 3, 2]    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Optional[Any]=True    ,							UpperCamelCase__:       Optional[int]=37    ,							UpperCamelCase__:       Dict="gelu"    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       Optional[Any]=0.02    ,							UpperCamelCase__:       Optional[int]=["stage2", "stage3", "stage4"]    ,							UpperCamelCase__:       List[str]=3    ,							UpperCamelCase__:       Optional[Any]=None    ,							):
														lowerCamelCase__      :			Optional[Any] 						= parent
														lowerCamelCase__      :			Union[str, Any] 						= batch_size
														lowerCamelCase__      :			Dict 						= image_size
														lowerCamelCase__      :			Optional[Any] 						= num_channels
														lowerCamelCase__      :			Optional[Any] 						= num_stages
														lowerCamelCase__      :			Optional[Any] 						= hidden_sizes
														lowerCamelCase__      :			List[str] 						= depths
														lowerCamelCase__      :			Any 						= is_training
														lowerCamelCase__      :			Dict 						= use_labels
														lowerCamelCase__      :			List[str] 						= intermediate_size
														lowerCamelCase__      :			Any 						= hidden_act
														lowerCamelCase__      :			int 						= type_sequence_label_size
														lowerCamelCase__      :			Optional[Any] 						= initializer_range
														lowerCamelCase__      :			Any 						= out_features
														lowerCamelCase__      :			str 						= num_labels
														lowerCamelCase__      :			Optional[Any] 						= scope
														lowerCamelCase__      :			Optional[Any] 						= num_stages




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCamelCase__      :			Tuple 						= None
														if self.use_labels:
																					lowerCamelCase__      :			str 						= ids_tensor([self.batch_size]    ,							self.type_sequence_label_size  )

														lowerCamelCase__      :			Union[str, Any] 						= self.get_config()

														return config, pixel_values, labels




							def 	lowerCamelCase_       (							self:       Any  ):
														return ConvNextConfig(
														    num_channels=self.num_channels    ,							num_stages=self.num_stages    ,							hidden_sizes=self.hidden_sizes    ,							depths=self.depths    ,							is_training=self.is_training    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							out_features=self.out_features    ,							)




							def 	lowerCamelCase_       (							self:       Dict  ):
														return UperNetConfig(
														    backbone_config=self.get_backbone_config()    ,							hidden_size=512    ,							pool_scales=[1, 2, 3, 6]    ,							use_auxiliary_head=_lowerCAmelCase    ,							auxiliary_loss_weight=0.4    ,							auxiliary_in_channels=40    ,							auxiliary_channels=256    ,							auxiliary_num_convs=1    ,							auxiliary_concat_input=_lowerCAmelCase    ,							loss_ignore_index=255    ,							num_labels=self.num_labels    ,							)




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       List[Any]  ):
														lowerCamelCase__      :			List[Any] 						= UperNetForSemanticSegmentation(config=_lowerCAmelCase  )
														model.to(_lowerCAmelCase  )
														model.eval()
														lowerCamelCase__      :			Dict 						= model(_lowerCAmelCase  )
														self.parent.assertEqual(
														    result.logits.shape    ,							(self.batch_size, self.num_labels, self.image_size, self.image_size)  )




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			str 						= self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__						
														)						,	(
														    lowerCamelCase__						
														)						,	(
														    lowerCamelCase__						
														)						,							
														)      :			str 						= config_and_inputs
														lowerCamelCase__      :			Union[str, Any] 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowerCAmelCase       ,   _lowerCAmelCase       ,   unittest.TestCase	):
							a          = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
							a          = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
							a          = False
							a          = False
							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Any 						= UperNetModelTester(self  )
														lowerCamelCase__      :			Any 						= ConfigTester(self    ,							config_class=_lowerCAmelCase    ,							has_text_modality=_lowerCAmelCase    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()




							def 	lowerCamelCase_       (							self:       Any  ):
														return




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__      :			List[Any] 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							_lowerCAmelCase  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Optional[Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase  )




							@unittest.skip(reason="""UperNet does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




							@unittest.skip(reason="""UperNet does not support input and output embeddings"""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




							@unittest.skip(reason="""UperNet does not have a base model"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip(reason="""UperNet does not have a base model"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@require_torch_multi_gpu
							@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       Dict  ):







														def check_hidden_states_output(UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Dict  ):
																					lowerCamelCase__      :			Tuple 						= model_class(_lowerCAmelCase  )
																					model.to(_lowerCAmelCase  )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__      :			int 						= model(**self._prepare_for_class(_lowerCAmelCase    ,							_lowerCAmelCase  )  )

																					lowerCamelCase__      :			List[Any] 						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																					lowerCamelCase__      :			Tuple 						= self.model_tester.num_stages
																					self.assertEqual(len(_lowerCAmelCase  )    ,							expected_num_stages + 1  )

																					# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]  )    ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,							)

														lowerCamelCase__						,	lowerCamelCase__      :			Tuple 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			str 						= True
																					check_hidden_states_output(_lowerCAmelCase    ,							_lowerCAmelCase    ,							_lowerCAmelCase  )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__      :			str 						= True

																					check_hidden_states_output(_lowerCAmelCase    ,							_lowerCAmelCase    ,							_lowerCAmelCase  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__      :			List[Any] 						= _config_zero_init(_lowerCAmelCase  )
														lowerCamelCase__      :			Optional[int] 						= _config_zero_init(configs_no_init.backbone_config  )
														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Union[str, Any] 						= model_class(config=_lowerCAmelCase  )
																					for name, param in model.named_parameters():
																												if param.requires_grad:
																																			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(reason="""UperNet does not have tied weights"""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):







														for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Any 						= UperNetForSemanticSegmentation.from_pretrained(_lowerCAmelCase  )
																					self.assertIsNotNone(_lowerCAmelCase  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[int]:
							lowerCamelCase__      :			str 						= hf_hub_download(
							    repo_id="""hf-internal-testing/fixtures_ade20k"""       ,    repo_type="""dataset"""       ,    filename="""ADE_val_00000001.jpg"""   )
							lowerCamelCase__      :			int 						= Image.open(UpperCamelCase   ).convert("""RGB"""   )
							return image



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




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			List[str] 						= AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny"""  )
														lowerCamelCase__      :			Union[str, Any] 						= UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny"""  ).to(_lowerCAmelCase  )

														lowerCamelCase__      :			int 						= prepare_img()
														lowerCamelCase__      :			int 						= processor(images=_lowerCAmelCase    ,							return_tensors="""pt"""  ).to(_lowerCAmelCase  )

														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(**_lowerCAmelCase  )

														lowerCamelCase__      :			str 						= torch.Size((1, model.config.num_labels, 512, 512)  )
														self.assertEqual(outputs.logits.shape    ,							_lowerCAmelCase  )

														lowerCamelCase__      :			Dict 						= torch.tensor(
														    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]  ).to(_lowerCAmelCase  )
														self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,							_lowerCAmelCase    ,							atol=1e-4  )  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[int] 						= AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny"""  )
														lowerCamelCase__      :			Union[str, Any] 						= UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny"""  ).to(_lowerCAmelCase  )

														lowerCamelCase__      :			Tuple 						= prepare_img()
														lowerCamelCase__      :			Any 						= processor(images=_lowerCAmelCase    ,							return_tensors="""pt"""  ).to(_lowerCAmelCase  )

														with torch.no_grad():
																					lowerCamelCase__      :			List[str] 						= model(**_lowerCAmelCase  )

														lowerCamelCase__      :			str 						= torch.Size((1, model.config.num_labels, 512, 512)  )
														self.assertEqual(outputs.logits.shape    ,							_lowerCAmelCase  )

														lowerCamelCase__      :			Optional[int] 						= torch.tensor(
														    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]  ).to(_lowerCAmelCase  )
														self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,							_lowerCAmelCase    ,							atol=1e-4  )  )









'''simple docstring'''







import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A			:    int					  =logging.get_logger(__name__)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							print("""Loading config file..."""   )

							def flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase="."   ):
														lowerCamelCase__      :			Optional[int] 						= []
														for k, v in d.items():
																					lowerCamelCase__      :			Optional[int] 						= parent_key + sep + k if parent_key else k
																					if isinstance(UpperCamelCase       ,    collections.abc.MutableMapping   ):
																												items.extend(flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase       ,    sep=UpperCamelCase   ).items()   )
																					else:
																												items.append((new_key, v)   )
														return dict(UpperCamelCase   )

							lowerCamelCase__      :			Any 						= argparse.Namespace()
							with open(UpperCamelCase       ,    """r"""   ) as yaml_file:
														try:
																					lowerCamelCase__      :			int 						= yaml.load(UpperCamelCase       ,    Loader=yaml.FullLoader   )

																					lowerCamelCase__      :			Tuple 						= flatten_yaml_as_dict(UpperCamelCase   )
																					for k, v in flat_cfg.items():
																												setattr(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														except yaml.YAMLError as exc:
																					logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase       ,    str(UpperCamelCase   )   )   )
							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaConfig()

							lowerCamelCase__      :			str 						= False

							# dataset
							if task_name.startswith("""imagenet1k_"""   ):
														lowerCamelCase__      :			Optional[Any] 						= 1000
														if int(task_name.strip().split("""_"""   )[-1]   ) == 384:
																					lowerCamelCase__      :			int 						= 384
														else:
																					lowerCamelCase__      :			Optional[int] 						= 256
														lowerCamelCase__      :			str 						= """imagenet-1k-id2label.json"""
							elif task_name.startswith("""imagenet21k_to_1k_"""   ):
														lowerCamelCase__      :			Tuple 						= 21000
														if int(task_name.strip().split("""_"""   )[-1]   ) == 384:
																					lowerCamelCase__      :			str 						= 384
														else:
																					lowerCamelCase__      :			Any 						= 256
														lowerCamelCase__      :			int 						= """imagenet-22k-id2label.json"""
							elif task_name.startswith("""ade20k_"""   ):
														lowerCamelCase__      :			Dict 						= 151
														lowerCamelCase__      :			str 						= 512
														lowerCamelCase__      :			List[Any] 						= """ade20k-id2label.json"""
														lowerCamelCase__      :			Union[str, Any] 						= True
							elif task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			Tuple 						= 21
														lowerCamelCase__      :			Optional[int] 						= 512
														lowerCamelCase__      :			List[Any] 						= """pascal-voc-id2label.json"""
														lowerCamelCase__      :			Tuple 						= True

							# orig_config
							lowerCamelCase__      :			Optional[int] 						= load_orig_config_file(UpperCamelCase   )
							assert getattr(UpperCamelCase       ,    """model.classification.name"""       ,    -1   ) == "mobilevit_v2", "Invalid model"
							lowerCamelCase__      :			int 						= getattr(UpperCamelCase       ,    """model.classification.mitv2.width_multiplier"""       ,    1.0   )
							assert (
							    getattr(UpperCamelCase       ,    """model.classification.mitv2.attn_norm_layer"""       ,    -1   ) == "layer_norm_2d"
							), "Norm layers other than layer_norm_2d is not supported"
							lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.classification.activation.name"""       ,    """swish"""   )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														lowerCamelCase__      :			Any 						= getattr(UpperCamelCase       ,    """model.segmentation.output_stride"""       ,    16   )
														if "_deeplabv3" in task_name:
																					lowerCamelCase__      :			str 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_rates"""       ,    [12, 24, 36]   )
																					lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_out_channels"""       ,    512   )
																					lowerCamelCase__      :			List[Any] 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_dropout"""       ,    0.1   )

    # id2label
							lowerCamelCase__      :			Tuple 						= """huggingface/label-files"""
							lowerCamelCase__      :			Optional[Any] 						= json.load(open(hf_hub_download(UpperCamelCase       ,    UpperCamelCase       ,    repo_type="""dataset"""   )       ,    """r"""   )   )
							lowerCamelCase__      :			Union[str, Any] 						= {int(UpperCamelCase   ): v for k, v in idalabel.items()}
							lowerCamelCase__      :			int 						= idalabel
							lowerCamelCase__      :			Optional[Any] 						= {v: k for k, v in idalabel.items()}

							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Any:
							lowerCamelCase__      :			List[Any] 						= dct.pop(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= val





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Tuple:
							if base_model:
														lowerCamelCase__      :			Optional[int] 						= """"""
							else:
														lowerCamelCase__      :			Optional[Any] 						= """mobilevitv2."""

							lowerCamelCase__      :			List[str] 						= []
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					lowerCamelCase__      :			Optional[Any] 						= k[8:]
														else:
																					lowerCamelCase__      :			Optional[Any] 						= k

														if ".block." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".block."""       ,    """."""   )
														if ".conv." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace(""".conv."""       ,    """.convolution."""   )
														if ".norm." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".norm."""       ,    """.normalization."""   )

														if "conv_1." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace("""conv_1."""       ,    f'''{model_prefix}conv_stem.'''   )
														for i in [1, 2]:
																					if f'''layer_{i}.''' in k:
																												lowerCamelCase__      :			Optional[Any] 						= k_new.replace(f'''layer_{i}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.layer.'''   )
														if ".exp_1x1." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".exp_1x1."""       ,    """.expand_1x1."""   )
														if ".red_1x1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".red_1x1."""       ,    """.reduce_1x1."""   )

														for i in [3, 4, 5]:
																					if f'''layer_{i}.0.''' in k:
																												lowerCamelCase__      :			List[str] 						= k_new.replace(f'''layer_{i}.0.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''   )
																					if f'''layer_{i}.1.local_rep.0.''' in k:
																												lowerCamelCase__      :			Optional[Any] 						= k_new.replace(f'''layer_{i}.1.local_rep.0.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''   )
																					if f'''layer_{i}.1.local_rep.1.''' in k:
																												lowerCamelCase__      :			Dict 						= k_new.replace(f'''layer_{i}.1.local_rep.1.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''   )

														for i in [3, 4, 5]:
																					if i == 3:
																												lowerCamelCase__      :			int 						= [0, 1]
																					elif i == 4:
																												lowerCamelCase__      :			str 						= [0, 1, 2, 3]
																					elif i == 5:
																												lowerCamelCase__      :			Dict 						= [0, 1, 2]

																					for j in j_in:
																												if f'''layer_{i}.1.global_rep.{j}.''' in k:
																																			lowerCamelCase__      :			List[Any] 						= k_new.replace(
																																			    f'''layer_{i}.1.global_rep.{j}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''   )
																					if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
																												lowerCamelCase__      :			Optional[int] 						= k_new.replace(
																												    f'''layer_{i}.1.global_rep.{j+1}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''   )

																					if f'''layer_{i}.1.conv_proj.''' in k:
																												lowerCamelCase__      :			Optional[int] 						= k_new.replace(f'''layer_{i}.1.conv_proj.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''   )

														if "pre_norm_attn.0." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.0."""       ,    """layernorm_before."""   )
														if "pre_norm_attn.1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.1."""       ,    """attention."""   )
														if "pre_norm_ffn.0." in k:
																					lowerCamelCase__      :			Optional[Any] 						= k_new.replace("""pre_norm_ffn.0."""       ,    """layernorm_after."""   )
														if "pre_norm_ffn.1." in k:
																					lowerCamelCase__      :			Union[str, Any] 						= k_new.replace("""pre_norm_ffn.1."""       ,    """ffn.conv1."""   )
														if "pre_norm_ffn.3." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace("""pre_norm_ffn.3."""       ,    """ffn.conv2."""   )

														if "classifier.1." in k:
																					lowerCamelCase__      :			Tuple 						= k_new.replace("""classifier.1."""       ,    """classifier."""   )

														if "seg_head." in k:
																					lowerCamelCase__      :			Optional[int] 						= k_new.replace("""seg_head."""       ,    """segmentation_head."""   )
														if ".aspp_layer." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_layer."""       ,    """."""   )
														if ".aspp_pool." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_pool."""       ,    """."""   )

														rename_keys.append((k, k_new)   )
							return rename_keys





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							lowerCamelCase__      :			Union[str, Any] 						= []
							for k in state_dict.keys():
														if k.startswith("""seg_head.aux_head."""   ):
																					keys_to_ignore.append(UpperCamelCase   )
							for k in keys_to_ignore:
														state_dict.pop(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Dict:
							lowerCamelCase__      :			Union[str, Any] 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							lowerCamelCase__      :			Tuple 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							return im





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			str 						= get_mobilevitva_config(UpperCamelCase       ,    UpperCamelCase   )

							# load original state_dict
							lowerCamelCase__      :			List[str] 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )

							# load huggingface model
							if task_name.startswith("""ade20k_"""   ) or task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Tuple 						= False
							else:
														lowerCamelCase__      :			int 						= MobileViTVaForImageClassification(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Optional[Any] 						= False

							# remove and rename some keys of load the original model
							lowerCamelCase__      :			Tuple 						= checkpoint
							remove_unused_keys(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, Any] 						= create_rename_keys(UpperCamelCase       ,    base_model=UpperCamelCase   )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							# load modified state_dict
							model.load_state_dict(UpperCamelCase   )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							lowerCamelCase__      :			int 						= MobileViTImageProcessor(crop_size=config.image_size       ,    size=config.image_size + 32   )
							lowerCamelCase__      :			Dict 						= image_processor(images=prepare_img()       ,    return_tensors="""pt"""   )
							lowerCamelCase__      :			str 						= model(**UpperCamelCase   )

							# verify classification model
							if task_name.startswith("""imagenet"""   ):
														lowerCamelCase__      :			Dict 						= outputs.logits
														lowerCamelCase__      :			Optional[Any] 						= logits.argmax(-1   ).item()
														print("""Predicted class:"""       ,    model.config.idalabel[predicted_class_idx]   )
														if task_name.startswith("""imagenet1k_256"""   ) and config.width_multiplier == 1.0:
																					# expected_logits for base variant
																					lowerCamelCase__      :			Optional[Any] 						= torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]   )
																					assert torch.allclose(logits[0, :3]       ,    UpperCamelCase       ,    atol=1E-4   )

							Path(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''   )
							model.save_pretrained(UpperCamelCase   )
							print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
							image_processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
			_A			:    Optional[int]					  =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''',
			    default='''imagenet1k_256''',
			    type=str,
			    help=(
			        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
			        '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
			    ),
			    choices=[
			        '''imagenet1k_256''',
			        '''imagenet1k_384''',
			        '''imagenet21k_to_1k_256''',
			        '''imagenet21k_to_1k_384''',
			        '''ade20k_deeplabv3''',
			        '''voc_deeplabv3''',
			    ],
			)

			parser.add_argument(
			    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
			)
			parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_A			:    Dict					  =parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)

