






'''simple docstring'''







import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

			from transformers import MaskFormerSwinBackbone
			from transformers.models.maskformer import MaskFormerSwinModel

class _lowercase			:




							def __init__(							self:       Dict    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[Any]=13    ,							UpperCamelCase__:       Tuple=32    ,							UpperCamelCase__:       Optional[int]=2    ,							UpperCamelCase__:       int=3    ,							UpperCamelCase__:       Dict=16    ,							UpperCamelCase__:       str=[1, 2, 1]    ,							UpperCamelCase__:       Tuple=[2, 2, 4]    ,							UpperCamelCase__:       Dict=2    ,							UpperCamelCase__:       List[str]=2.0    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       List[str]=0.0    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       str=0.1    ,							UpperCamelCase__:       List[Any]="gelu"    ,							UpperCamelCase__:       Any=False    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Optional[int]=0.02    ,							UpperCamelCase__:       List[Any]=1e-5    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       str=None    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       Tuple=8    ,							UpperCamelCase__:       Optional[int]=["stage1", "stage2", "stage3"]    ,							UpperCamelCase__:       Dict=[1, 2, 3]    ,							):
														lowerCamelCase__      :			Any 						= parent
														lowerCamelCase__      :			Dict 						= batch_size
														lowerCamelCase__      :			List[str] 						= image_size
														lowerCamelCase__      :			Dict 						= patch_size
														lowerCamelCase__      :			Optional[int] 						= num_channels
														lowerCamelCase__      :			Any 						= embed_dim
														lowerCamelCase__      :			Optional[int] 						= depths
														lowerCamelCase__      :			Union[str, Any] 						= num_heads
														lowerCamelCase__      :			Optional[int] 						= window_size
														lowerCamelCase__      :			str 						= mlp_ratio
														lowerCamelCase__      :			List[Any] 						= qkv_bias
														lowerCamelCase__      :			Tuple 						= hidden_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Any 						= drop_path_rate
														lowerCamelCase__      :			Optional[int] 						= hidden_act
														lowerCamelCase__      :			Union[str, Any] 						= use_absolute_embeddings
														lowerCamelCase__      :			str 						= patch_norm
														lowerCamelCase__      :			List[str] 						= layer_norm_eps
														lowerCamelCase__      :			Any 						= initializer_range
														lowerCamelCase__      :			List[str] 						= is_training
														lowerCamelCase__      :			List[Any] 						= scope
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			List[Any] 						= type_sequence_label_size
														lowerCamelCase__      :			Dict 						= encoder_stride
														lowerCamelCase__      :			Union[str, Any] 						= out_features
														lowerCamelCase__      :			Union[str, Any] 						= out_indices




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

														lowerCamelCase__      :			Tuple 						= None
														if self.use_labels:
																					lowerCamelCase__      :			int 						= 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 MaskFormerSwinConfig(
														    image_size=self.image_size    ,							patch_size=self.patch_size    ,							num_channels=self.num_channels    ,							embed_dim=self.embed_dim    ,							depths=self.depths    ,							num_heads=self.num_heads    ,							window_size=self.window_size    ,							mlp_ratio=self.mlp_ratio    ,							qkv_bias=self.qkv_bias    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							drop_path_rate=self.drop_path_rate    ,							hidden_act=self.hidden_act    ,							use_absolute_embeddings=self.use_absolute_embeddings    ,							path_norm=self.patch_norm    ,							layer_norm_eps=self.layer_norm_eps    ,							initializer_range=self.initializer_range    ,							encoder_stride=self.encoder_stride    ,							out_features=self.out_features    ,							out_indices=self.out_indices    ,							)




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			int 						= MaskFormerSwinModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
														lowerCamelCase__      :			Optional[int] 						= int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

														self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, expected_seq_len, expected_dim)  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Union[str, Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MaskFormerSwinBackbone(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Dict 						= model(UpperCamelCase__  )

														# verify feature maps
														self.parent.assertEqual(len(result.feature_maps  )    ,							len(config.out_features  )  )
														self.parent.assertListEqual(list(result.feature_maps[0].shape  )    ,							[13, 16, 16, 16]  )

														# verify channels
														self.parent.assertEqual(len(model.channels  )    ,							len(config.out_features  )  )
														self.parent.assertListEqual(model.channels    ,							[16, 32, 64]  )

														# verify ValueError
														with self.parent.assertRaises(UpperCamelCase__  ):
																					lowerCamelCase__      :			Dict 						= ["""stem"""]
																					lowerCamelCase__      :			Union[str, Any] 						= MaskFormerSwinBackbone(config=UpperCamelCase__  )




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



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (
							        MaskFormerSwinModel,
							        MaskFormerSwinBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							a          = {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
							a          = False
							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			str 						= MaskFormerSwinModelTester(self  )
														lowerCamelCase__      :			List[str] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							embed_dim=37  )




							@require_torch_multi_gpu
							@unittest.skip(
							    reason=(
							        """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
							        """ `nn.DataParallel`"""
							    )  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




							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:       Tuple  ):
														return




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			List[Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_backbone(*UpperCamelCase__  )




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




							@unittest.skip("""Swin does not support feedforward chunking"""  )
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														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] 						= model_class(UpperCamelCase__  )
																					self.assertIsInstance(model.get_input_embeddings()    ,							(nn.Module)  )
																					lowerCamelCase__      :			str 						= model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__    ,							nn.Linear  )  )




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

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

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




							@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""  )
							def 	lowerCamelCase_       (							self:       Any  ):
														pass




							@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""  )
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			List[str] 						= model_class(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()

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

														lowerCamelCase__      :			Union[str, Any] 						= outputs.hidden_states

														lowerCamelCase__      :			Union[str, Any] 						= getattr(
														    self.model_tester    ,							"""expected_num_hidden_layers"""    ,							len(self.model_tester.depths  ) + 1  )
														self.assertEqual(len(UpperCamelCase__  )    ,							UpperCamelCase__  )

														# Swin has a different seq_length
														lowerCamelCase__      :			int 						= (
														    config.patch_size
														    if isinstance(config.patch_size    ,							collections.abc.Iterable  )
														    else (config.patch_size, config.patch_size)
														)

														lowerCamelCase__      :			Union[str, Any] 						= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]  )    ,							[num_patches, self.model_tester.embed_dim]    ,							)




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

														lowerCamelCase__      :			Optional[int] 						= (
														    self.model_tester.image_size
														    if isinstance(self.model_tester.image_size    ,							collections.abc.Iterable  )
														    else (self.model_tester.image_size, self.model_tester.image_size)
														)

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Any 						= True
																					self.check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

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

																					self.check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__      :			Tuple 						= 3

														lowerCamelCase__      :			Optional[int] 						= (
														    self.model_tester.image_size
														    if isinstance(self.model_tester.image_size    ,							collections.abc.Iterable  )
														    else (self.model_tester.image_size, self.model_tester.image_size)
														)
														lowerCamelCase__      :			Optional[int] 						= (
														    config.patch_size
														    if isinstance(config.patch_size    ,							collections.abc.Iterable  )
														    else (config.patch_size, config.patch_size)
														)

														lowerCamelCase__      :			List[Any] 						= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
														lowerCamelCase__      :			int 						= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Dict 						= True
																					self.check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							(padded_height, padded_width)  )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__      :			Dict 						= True
																					self.check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							(padded_height, padded_width)  )




							@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""  )
							def 	lowerCamelCase_       (							self:       List[str]  ):
														pass




							@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""  )
							def 	lowerCamelCase_       (							self:       Any  ):
														pass




							@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""  )
							def 	lowerCamelCase_       (							self:       Any  ):
														pass




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

														def set_nan_tensor_to_zero(UpperCamelCase__:       int  ):
																					lowerCamelCase__      :			str 						= 0
																					return t

														def check_equivalence(UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[str]={}  ):
																					with torch.no_grad():
																												lowerCamelCase__      :			List[Any] 						= model(**UpperCamelCase__    ,							return_dict=UpperCamelCase__    ,							**UpperCamelCase__  )
																												lowerCamelCase__      :			Any 						= model(**UpperCamelCase__    ,							return_dict=UpperCamelCase__    ,							**UpperCamelCase__  ).to_tuple()

																												def recursive_check(UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Optional[Any]  ):
																																			if isinstance(UpperCamelCase__    ,							(List, Tuple)  ):
																																										for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__    ,							UpperCamelCase__  ):
																																																	recursive_check(UpperCamelCase__    ,							UpperCamelCase__  )
																																			elif isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																										for tuple_iterable_value, dict_iterable_value in zip(
																																										    tuple_object.values()    ,							dict_object.values()  ):
																																																	recursive_check(UpperCamelCase__    ,							UpperCamelCase__  )
																																			elif tuple_object is None:
																																										return
																																			else:
																																										self.assertTrue(
																																										    torch.allclose(
																																										        set_nan_tensor_to_zero(UpperCamelCase__  )    ,							set_nan_tensor_to_zero(UpperCamelCase__  )    ,							atol=1e-5  )    ,							msg=(
																																										        """Tuple and dict output are not equal. Difference:"""
																																										        F''' {torch.max(torch.abs(tuple_object - dict_object  )  )}. Tuple has `nan`:'''
																																										        F''' {torch.isnan(UpperCamelCase__  ).any()} and `inf`: {torch.isinf(UpperCamelCase__  )}. Dict has'''
																																										        F''' `nan`: {torch.isnan(UpperCamelCase__  ).any()} and `inf`: {torch.isinf(UpperCamelCase__  )}.'''
																																										    )    ,							)

																												recursive_check(UpperCamelCase__    ,							UpperCamelCase__  )

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Tuple 						= model_class(UpperCamelCase__  )
																					model.to(UpperCamelCase__  )
																					model.eval()

																					lowerCamelCase__      :			Optional[int] 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			int 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )
																					check_equivalence(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

																					lowerCamelCase__      :			str 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__    ,							return_labels=UpperCamelCase__  )
																					lowerCamelCase__      :			Optional[int] 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__    ,							return_labels=UpperCamelCase__  )
																					check_equivalence(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

																					lowerCamelCase__      :			Union[str, Any] 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			str 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )
																					check_equivalence(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							{"""output_hidden_states""": True}  )

																					lowerCamelCase__      :			List[str] 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__    ,							return_labels=UpperCamelCase__  )
																					lowerCamelCase__      :			int 						= self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__    ,							return_labels=UpperCamelCase__  )
																					check_equivalence(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							{"""output_hidden_states""": True}  )



@require_torch
class _lowercase			(       unittest.TestCase       ,   _lowercase	):
							a          = (MaskFormerSwinBackbone,) if is_torch_available() else ()
							a          = MaskFormerSwinConfig




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			str 						= MaskFormerSwinModelTester(self  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__						,	lowerCamelCase__      :			List[Any] 						= self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__      :			Dict 						= inputs_dict["""pixel_values"""].shape[0]

														for backbone_class in self.all_model_classes:
																					lowerCamelCase__      :			Union[str, Any] 						= backbone_class(UpperCamelCase__  )
																					backbone.to(UpperCamelCase__  )
																					backbone.eval()

																					lowerCamelCase__      :			str 						= backbone(**UpperCamelCase__  )

																					# Test default outputs and verify feature maps
																					self.assertIsInstance(outputs.feature_maps    ,							UpperCamelCase__  )
																					self.assertTrue(len(outputs.feature_maps  ) == len(backbone.channels  )  )
																					for feature_map, n_channels in zip(outputs.feature_maps    ,							backbone.channels  ):
																												self.assertTrue(feature_map.shape[:2]    ,							(batch_size, n_channels)  )
																					self.assertIsNone(outputs.hidden_states  )
																					self.assertIsNone(outputs.attentions  )

																					# Test output_hidden_states=True
																					lowerCamelCase__      :			List[str] 						= backbone(**UpperCamelCase__    ,							output_hidden_states=UpperCamelCase__  )
																					self.assertIsNotNone(outputs.hidden_states  )
																					self.assertTrue(len(outputs.hidden_states  )    ,							len(backbone.stage_names  )  )
																					# We skip the stem layer
																					for hidden_states, n_channels in zip(outputs.hidden_states[1:]    ,							backbone.channels  ):
																												for hidden_state in hidden_states:
																																			# Hidden states are in the format (batch_size, (height * width), n_channels)
																																			lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= hidden_state.shape
																																			self.assertTrue((h_batch_size, h_n_channels)    ,							(batch_size, n_channels)  )

            # Test output_attentions=True
																					if self.has_attentions:
																												lowerCamelCase__      :			List[Any] 						= backbone(**UpperCamelCase__    ,							output_attentions=UpperCamelCase__  )
																												self.assertIsNotNone(outputs.attentions  )









'''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
			)

