


import inspect
import unittest

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

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


if is_torch_available():
     import torch

     from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
     from transformers.models.mobilevitva.modeling_mobilevitva import (
         MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
         make_divisible,
     )


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Any		     =					self.config_class(**self.inputs_dict		)
         self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,       '''width_multiplier'''		)		)






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE="swish"				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=0.2_5				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       )->Optional[int]:




         '''simple docstring'''





         A_      :				Dict		     =					parent
         A_      :				List[str]		     =					batch_size
         A_      :				Optional[Any]		     =					image_size
         A_      :				Optional[Any]		     =					patch_size
         A_      :				Tuple		     =					num_channels
         A_      :				Dict		     =					make_divisible(512 * width_multiplier				,       divisor=8		)
         A_      :				Any		     =					hidden_act
         A_      :				str		     =					conv_kernel_size
         A_      :				str		     =					output_stride
         A_      :				Union[str, Any]		     =					classifier_dropout_prob
         A_      :				Union[str, Any]		     =					use_labels
         A_      :				Optional[int]		     =					is_training
         A_      :				List[Any]		     =					num_labels
         A_      :				List[Any]		     =					initializer_range
         A_      :				Optional[Any]		     =					scope
         A_      :				Optional[Any]		     =					width_multiplier
         A_      :				Union[str, Any]		     =					ffn_dropout
         A_      :				List[str]		     =					attn_dropout



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





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

         A_      :				str		     =					None
         A_      :				Tuple		     =					None
         if self.use_labels:
           A_      :				Tuple		     =					ids_tensor([self.batch_size]				,       self.num_labels		)
           A_      :				List[Any]		     =					ids_tensor([self.batch_size, self.image_size, self.image_size]				,       self.num_labels		)

         A_      :				List[str]		     =					self.get_config()

         return config, pixel_values, labels, pixel_labels



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         return MobileViTVaConfig(
             image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_act=self.hidden_act				,       conv_kernel_size=self.conv_kernel_size				,       output_stride=self.output_stride				,       classifier_dropout_prob=self.classifier_dropout_prob				,       initializer_range=self.initializer_range				,       width_multiplier=self.width_multiplier				,       ffn_dropout=self.ffn_dropout_prob				,       attn_dropout=self.attn_dropout_prob				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->List[str]:




         '''simple docstring'''





         A_      :				Dict		     =					MobileViTVaModel(config=_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Optional[Any]		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(
             result.last_hidden_state.shape				,       (
                 self.batch_size,
                 self.last_hidden_size,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )				,       )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					self.num_labels
         A_      :				int		     =					MobileViTVaForImageClassification(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				Optional[int]		     =					model(_SCREAMING_SNAKE_CASE				,       labels=_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)		)



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




         '''simple docstring'''





         A_      :				Dict		     =					self.num_labels
         A_      :				Optional[Any]		     =					MobileViTVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE		)
         model.to(_SCREAMING_SNAKE_CASE		)
         model.eval()
         A_      :				List[Any]		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(
             result.logits.shape				,       (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )				,       )
         A_      :				Union[str, Any]		     =					model(_SCREAMING_SNAKE_CASE				,       labels=_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(
             result.logits.shape				,       (
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )				,       )







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




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.prepare_config_and_inputs()
         A_     ,       A_     ,       A_     ,       A_      :				Any		     =					config_and_inputs
         A_      :				List[str]		     =					{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       snake_case      =    (
           {
               "feature-extraction": MobileViTVaModel,
               "image-classification": MobileViTVaForImageClassification,
               "image-segmentation": MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       snake_case      =    False
       snake_case      =    False
       snake_case      =    False
       snake_case      =    False



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




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					MobileViTVaModelTester(self		)
         A_      :				Tuple		     =					MobileViTVaConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       has_text_modality=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         self.config_tester.run_common_tests()



       @unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''		)
       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         pass



       @unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''		)
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         pass



       @unittest.skip(reason='''MobileViTV2 does not output attentions'''		)
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         pass



       @require_torch_multi_gpu
       @unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''		)
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         pass



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




         '''simple docstring'''





         pass



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




         '''simple docstring'''





         A_     ,       A_      :				Union[str, Any]		     =					self.model_tester.prepare_config_and_inputs_for_common()

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

           A_      :				str		     =					['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				int		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)



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




         '''simple docstring'''





         def check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				Tuple		     =					model_class(_SCREAMING_SNAKE_CASE		)
           model.to(_SCREAMING_SNAKE_CASE		)
           model.eval()

           with torch.no_grad():
             A_      :				Optional[int]		     =					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

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

           A_      :				str		     =					5
           self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       _SCREAMING_SNAKE_CASE		)

           # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           A_      :				Optional[int]		     =					2
           for i in range(len(_SCREAMING_SNAKE_CASE		)		):
             self.assertListEqual(
                 list(hidden_states[i].shape[-2:]		)				,       [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]				,       )
             divisor *= 2

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

         A_     ,       A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           A_      :				int		     =					True
           check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

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

           check_hidden_states_output(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



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




         '''simple docstring'''





         A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)



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




         '''simple docstring'''





         A_      :				int		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE		)







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




         '''simple docstring'''





         for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A_      :				Dict		     =					MobileViTVaModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




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







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




       """simple docstring"""



       @cached_property
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         return (
             MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''		)
             if is_vision_available()
             else None
         )



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




         '''simple docstring'''





         A_      :				Tuple		     =					MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''		).to(
             _SCREAMING_SNAKE_CASE		)

         A_      :				int		     =					self.default_image_processor
         A_      :				Optional[Any]		     =					prepare_img()
         A_      :				Optional[Any]		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

         # forward pass
         with torch.no_grad():
           A_      :				List[str]		     =					model(**_SCREAMING_SNAKE_CASE		)

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

         A_      :				Tuple		     =					torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]		).to(_SCREAMING_SNAKE_CASE		)

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



       @slow
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				List[Any]		     =					MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''		)
         A_      :				int		     =					model.to(_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''		)

         A_      :				Dict		     =					prepare_img()
         A_      :				Union[str, Any]		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

         # forward pass
         with torch.no_grad():
           A_      :				Any		     =					model(**_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					outputs.logits

         # verify the logits
         A_      :				int		     =					torch.Size((1, 21, 32, 32)		)
         self.assertEqual(logits.shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					torch.tensor(
             [
                 [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
                 [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
                 [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
             ]				,       device=_SCREAMING_SNAKE_CASE				,       )

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







       @slow
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''		)
         A_      :				Any		     =					model.to(_SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''		)

         A_      :				Optional[int]		     =					prepare_img()
         A_      :				List[str]		     =					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

         # forward pass
         with torch.no_grad():
           A_      :				Optional[int]		     =					model(**_SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					outputs.logits.detach().cpu()

         A_      :				List[str]		     =					image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE				,       target_sizes=[(50, 60)]		)
         A_      :				Tuple		     =					torch.Size((50, 60)		)
         self.assertEqual(segmentation[0].shape				,       _SCREAMING_SNAKE_CASE		)

         A_      :				int		     =					image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					torch.Size((32, 32)		)
         self.assertEqual(segmentation[0].shape				,       _SCREAMING_SNAKE_CASE		)








import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=sys.maxsize		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					'''bilinear'''
         A_      :				Optional[Any]		     =					max_size
         A_      :				Optional[Any]		     =					short_edge_length







       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					[]
         for img in imgs:
           A_     ,       A_      :				List[str]		     =					img.shape[:2]
           # later: provide list and randomly choose index for resize
           A_      :				List[Any]		     =					np.random.randint(self.short_edge_length[0]				,       self.short_edge_length[1] + 1		)
           if size == 0:
             return img
           A_      :				int		     =					size * 1.0 / min(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
           if h < w:
             A_     ,       A_      :				Tuple		     =					size, scale * w
           else:
             A_     ,       A_      :				List[str]		     =					scale * h, size
           if max(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) > self.max_size:
             A_      :				List[Any]		     =					self.max_size * 1.0 / max(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
             A_      :				Any		     =					newh * scale
             A_      :				List[str]		     =					neww * scale
           A_      :				List[Any]		     =					int(neww + 0.5		)
           A_      :				Tuple		     =					int(newh + 0.5		)

           if img.dtype == np.uinta:
             A_      :				List[str]		     =					Image.fromarray(_SCREAMING_SNAKE_CASE		)
             A_      :				Optional[Any]		     =					pil_image.resize((neww, newh)				,       PILImageResampling.BILINEAR		)
             A_      :				Dict		     =					np.asarray(_SCREAMING_SNAKE_CASE		)
           else:
             A_      :				Any		     =					img.permute(2				,       0				,       1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
             A_      :				List[str]		     =					nn.functional.interpolate(
                 _SCREAMING_SNAKE_CASE				,       (newh, neww)				,       mode=self.interp_method				,       align_corners=_SCREAMING_SNAKE_CASE		).squeeze(0		)
           img_augs.append(_SCREAMING_SNAKE_CASE		)

         return img_augs






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Tuple		     =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,       cfg.INPUT.MAX_SIZE_TEST		)
         A_      :				Union[str, Any]		     =					cfg.INPUT.FORMAT
         A_      :				int		     =					cfg.SIZE_DIVISIBILITY
         A_      :				Tuple		     =					cfg.PAD_VALUE
         A_      :				List[Any]		     =					cfg.INPUT.MAX_SIZE_TEST
         A_      :				List[str]		     =					cfg.MODEL.DEVICE
         A_      :				Dict		     =					torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)				,       1				,       1		)
         A_      :				List[Any]		     =					torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)				,       1				,       1		)
         A_      :				List[Any]		     =					lambda _SCREAMING_SNAKE_CASE		: (x - self.pixel_mean) / self.pixel_std



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




         '''simple docstring'''





         A_      :				Any		     =					tuple(max(_SCREAMING_SNAKE_CASE		) for s in zip(*[img.shape for img in images]		)		)
         A_      :				List[Any]		     =					[im.shape[-2:] for im in images]
         A_      :				Any		     =					[
             nn.functional.pad(
                 _SCREAMING_SNAKE_CASE				,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,       value=self.pad_value				,       )
             for size, im in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         ]

         return torch.stack(_SCREAMING_SNAKE_CASE		), torch.tensor(_SCREAMING_SNAKE_CASE		)







       def __call__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->Dict:




         '''simple docstring'''





         with torch.no_grad():
           if not isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
             A_      :				Dict		     =					[images]
           if single_image:
             assert len(_SCREAMING_SNAKE_CASE		) == 1
           for i in range(len(_SCREAMING_SNAKE_CASE		)		):
             if isinstance(images[i]				,       torch.Tensor		):
               images.insert(_SCREAMING_SNAKE_CASE				,       images.pop(_SCREAMING_SNAKE_CASE		).to(self.device		).float()		)
             elif not isinstance(images[i]				,       torch.Tensor		):
               images.insert(
                   _SCREAMING_SNAKE_CASE				,       torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE		)				,       input_format=self.input_format		)		)
                   .to(self.device		)
                   .float()				,       )
            # resize smallest edge
           A_      :				List[str]		     =					torch.tensor([im.shape[:2] for im in images]		)
           A_      :				Union[str, Any]		     =					self.aug(_SCREAMING_SNAKE_CASE		)
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           A_      :				List[str]		     =					[self.normalizer(_SCREAMING_SNAKE_CASE		) for x in images]
           # now pad them to do the following operations
           A_     ,       A_      :				Any		     =					self.pad(_SCREAMING_SNAKE_CASE		)
           # Normalize

           if self.size_divisibility > 0:
             raise NotImplementedError()
           # pad
           A_      :				str		     =					torch.true_divide(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
           if single_image:
             return images[0], sizes[0], scales_yx[0]
           else:
             return images, sizes, scales_yx




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  assert torch.isfinite(SCREAMING_SNAKE_CASE       ).all(), "Box tensor contains infinite or NaN!"
  A_     ,       A_      :				int		     =					box_size
  tensor[:, 0].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )
  tensor[:, 1].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )
  tensor[:, 2].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )
  tensor[:, 3].clamp_(min=0 ,			max=SCREAMING_SNAKE_CASE       )




