

'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViltImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def __init__(       self,						lowercase_,						lowercase_=7,						lowercase_=3,						lowercase_=18,						lowercase_=30,						lowercase_=400,						lowercase_=True,						lowercase_=None,						lowercase_=2,						lowercase_=True,						lowercase_=[0.5, 0.5, 0.5],						lowercase_=[0.5, 0.5, 0.5],						)    ->							Optional[Any]:
					snake_case							=					size if size is not None else {'shortest_edge': 30}
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					num_channels
					snake_case							=					image_size
					snake_case							=					min_resolution
					snake_case							=					max_resolution
					snake_case							=					do_resize
					snake_case							=					size
					snake_case							=					size_divisor
					snake_case							=					do_normalize
					snake_case							=					image_mean
					snake_case							=					image_std

				def 					_lowerCamelCase    (       self )    ->							Any:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_=False )    ->							Optional[int]:
					if not batched:
						snake_case							=					self.size['shortest_edge']
						snake_case							=					image_inputs[0]
						if isinstance(lowercase_,						Image.Image ):
							snake_case			,			snake_case							=					image.size
						else:
							snake_case			,			snake_case							=					image.shape[1], image.shape[2]
						snake_case							=					size / min(lowercase_,						lowercase_ )
						if h < w:
							snake_case			,			snake_case							=					size, scale * w
						else:
							snake_case			,			snake_case							=					scale * h, size

						snake_case							=					int((1333 / 800) * size )
						if max(lowercase_,						lowercase_ ) > max_size:
							snake_case							=					max_size / max(lowercase_,						lowercase_ )
							snake_case							=					newh * scale
							snake_case							=					neww * scale

						snake_case			,			snake_case							=					int(newh + 0.5 ), int(neww + 0.5 )
						snake_case			,			snake_case							=					(
						    newh // self.size_divisor * self.size_divisor,
						    neww // self.size_divisor * self.size_divisor,
						)

					else:
						snake_case							=					[]
						for image in image_inputs:
							snake_case			,			snake_case							=					self.get_expected_values([image] )
							expected_values.append((expected_height, expected_width) )
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[0] )[0]
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[1] )[1]

					return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase   (    __lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = ViltImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					ViltImageProcessingTester(self )

				@property
				def 					_lowerCamelCase    (       self )    ->							int:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowercase_,						'image_mean' ) )
					self.assertTrue(hasattr(lowercase_,						'image_std' ) )
					self.assertTrue(hasattr(lowercase_,						'do_normalize' ) )
					self.assertTrue(hasattr(lowercase_,						'do_resize' ) )
					self.assertTrue(hasattr(lowercase_,						'size' ) )
					self.assertTrue(hasattr(lowercase_,						'size_divisor' ) )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size,						{'shortest_edge': 30} )

					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict,						size=42 )
					self.assertEqual(image_processor.size,						{'shortest_edge': 42} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					pass

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						Image.Image )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						numpify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						np.ndarray )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					# Initialize image_processing
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					snake_case							=					prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowercase_,						torchify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						torch.Tensor )

					# Test not batched input
					snake_case							=					image_processing(image_inputs[0],						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowercase  		=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileViTV2ForImageClassification""",
					    """MobileViTV2ForSemanticSegmentation""",
					    """MobileViTV2Model""",
					    """MobileViTV2PreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileViTVaConfig,
					    MobileViTVaOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilevitva import (
										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileViTVaForImageClassification,
										    MobileViTVaForSemanticSegmentation,
										    MobileViTVaModel,
										    MobileViTVaPreTrainedModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





