
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_video_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import VideoMAEImageProcessor



class 			UpperCamelCase_			(						unittest.TestCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase=7     ,						UpperCamelCase=3     ,						UpperCamelCase=10     ,						UpperCamelCase=18     ,						UpperCamelCase=30     ,						UpperCamelCase=4_00     ,						UpperCamelCase=True     ,						UpperCamelCase=None     ,						UpperCamelCase=True     ,						UpperCamelCase=[0.5, 0.5, 0.5]     ,						UpperCamelCase=[0.5, 0.5, 0.5]     ,						UpperCamelCase=None     ,						) ->	str:
										UpperCamelCase__    :		Optional[int]     =				size if size is not None else {'shortest_edge': 18}
										UpperCamelCase__    :		Optional[int]     =				crop_size if crop_size is not None else {'height': 18, 'width': 18}

										UpperCamelCase__    :		int     =				parent
										UpperCamelCase__    :		Any     =				batch_size
										UpperCamelCase__    :		Dict     =				num_channels
										UpperCamelCase__    :		Any     =				num_frames
										UpperCamelCase__    :		Dict     =				image_size
										UpperCamelCase__    :		Dict     =				min_resolution
										UpperCamelCase__    :		Optional[Any]     =				max_resolution
										UpperCamelCase__    :		int     =				do_resize
										UpperCamelCase__    :		Optional[int]     =				size
										UpperCamelCase__    :		Tuple     =				do_normalize
										UpperCamelCase__    :		str     =				image_mean
										UpperCamelCase__    :		str     =				image_std
										UpperCamelCase__    :		List[Any]     =				crop_size






							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,
										    "crop_size": self.crop_size,
										}



@require_torch
@require_vision
class 			UpperCamelCase_			(						__UpperCamelCase	,     unittest.TestCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							VideoMAEImageProcessor if is_vision_available() else None
							def        lowerCAmelCase__ (				self) ->	Any:
										UpperCamelCase__    :		Tuple     =				VideoMAEImageProcessingTester(self)
							@property
							def        lowerCAmelCase__ (				self) ->	Tuple:
										return self.image_processor_tester.prepare_image_processor_dict()
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										UpperCamelCase__    :		Optional[Any]     =				self.image_processing_class(**self.image_processor_dict)
										self.assertTrue(hasattr(UpperCamelCase     ,						'image_mean'))
										self.assertTrue(hasattr(UpperCamelCase     ,						'image_std'))
										self.assertTrue(hasattr(UpperCamelCase     ,						'do_normalize'))
										self.assertTrue(hasattr(UpperCamelCase     ,						'do_resize'))
										self.assertTrue(hasattr(UpperCamelCase     ,						'do_center_crop'))
										self.assertTrue(hasattr(UpperCamelCase     ,						'size'))
							def        lowerCAmelCase__ (				self) ->	str:
										UpperCamelCase__    :		Dict     =				self.image_processing_class.from_dict(self.image_processor_dict)
										self.assertEqual(image_processor.size     ,						{'shortest_edge': 18})
										self.assertEqual(image_processor.crop_size     ,						{'height': 18, 'width': 18})

										UpperCamelCase__    :		Tuple     =				self.image_processing_class.from_dict(self.image_processor_dict     ,						size=42     ,						crop_size=84)
										self.assertEqual(image_processor.size     ,						{'shortest_edge': 42})
										self.assertEqual(image_processor.crop_size     ,						{'height': 84, 'width': 84})
							def        lowerCAmelCase__ (				self) ->	List[str]:
										pass
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										# Initialize image_processing
										UpperCamelCase__    :		Optional[int]     =				self.image_processing_class(**self.image_processor_dict)
										# create random PIL videos
										UpperCamelCase__    :		int     =				prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=UpperCamelCase)
										for video in video_inputs:
													self.assertIsInstance(UpperCamelCase     ,						UpperCamelCase)
													self.assertIsInstance(video[0]     ,						Image.Image)

										# Test not batched input
										UpperCamelCase__    :		Union[str, Any]     =				image_processing(video_inputs[0]     ,						return_tensors='pt').pixel_values
										self.assertEqual(
										    encoded_videos.shape     ,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )     ,						)

										# Test batched
										UpperCamelCase__    :		Dict     =				image_processing(UpperCamelCase     ,						return_tensors='pt').pixel_values
										self.assertEqual(
										    encoded_videos.shape     ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )     ,						)
							def        lowerCAmelCase__ (				self) ->	int:
										# Initialize image_processing
										UpperCamelCase__    :		Dict     =				self.image_processing_class(**self.image_processor_dict)
										# create random numpy tensors
										UpperCamelCase__    :		Any     =				prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=UpperCamelCase     ,						numpify=UpperCamelCase)
										for video in video_inputs:
													self.assertIsInstance(UpperCamelCase     ,						UpperCamelCase)
													self.assertIsInstance(video[0]     ,						np.ndarray)

										# Test not batched input
										UpperCamelCase__    :		Dict     =				image_processing(video_inputs[0]     ,						return_tensors='pt').pixel_values
										self.assertEqual(
										    encoded_videos.shape     ,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )     ,						)

										# Test batched
										UpperCamelCase__    :		Union[str, Any]     =				image_processing(UpperCamelCase     ,						return_tensors='pt').pixel_values
										self.assertEqual(
										    encoded_videos.shape     ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )     ,						)






							def        lowerCAmelCase__ (				self) ->	Dict:
										# Initialize image_processing
										UpperCamelCase__    :		int     =				self.image_processing_class(**self.image_processor_dict)
										# create random PyTorch tensors
										UpperCamelCase__    :		Any     =				prepare_video_inputs(self.image_processor_tester     ,						equal_resolution=UpperCamelCase     ,						torchify=UpperCamelCase)
										for video in video_inputs:
													self.assertIsInstance(UpperCamelCase     ,						UpperCamelCase)
													self.assertIsInstance(video[0]     ,						torch.Tensor)

										# Test not batched input
										UpperCamelCase__    :		Tuple     =				image_processing(video_inputs[0]     ,						return_tensors='pt').pixel_values
										self.assertEqual(
										    encoded_videos.shape     ,						(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )     ,						)

										# Test batched
										UpperCamelCase__    :		List[Any]     =				image_processing(UpperCamelCase     ,						return_tensors='pt').pixel_values
										self.assertEqual(
										    encoded_videos.shape     ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )     ,						)





from typing import TYPE_CHECKING

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


UpperCAmelCase__				:  str       					=				{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__				:  List[Any]       					=				[
							    '''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

							UpperCAmelCase__				:  List[str]       					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



