



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 OwlViTImageProcessor

class 			_a     (   unittest.TestCase):



			def __init__( self       :							Optional[int]      ,  _lowercase       :							int      ,  _lowercase       :							Union[str, Any]=7      ,  _lowercase       :							Union[str, Any]=3      ,  _lowercase       :							int=18      ,  _lowercase       :							Any=30      ,  _lowercase       :							str=400      ,  _lowercase       :							str=True      ,  _lowercase       :							Dict=None      ,  _lowercase       :							int=True      ,  _lowercase       :							Union[str, Any]=None      ,  _lowercase       :							str=True      ,  _lowercase       :							Union[str, Any]=[0.48145466, 0.4578275, 0.40821073]      ,  _lowercase       :							str=[0.26862954, 0.26130258, 0.27577711]      ,  _lowercase       :							Optional[int]=True      ,  )       ->				Optional[int]:
									snake_case      : str							=   parent
									snake_case      : Any							=   batch_size
									snake_case      : Optional[int]							=   num_channels
									snake_case      : Any							=   image_size
									snake_case      : Optional[Any]							=   min_resolution
									snake_case      : int							=   max_resolution
									snake_case      : List[Any]							=   do_resize
									snake_case      : Dict							=   size if size is not None else {"height": 18, "width": 18}
									snake_case      : str							=   do_center_crop
									snake_case      : List[str]							=   crop_size if crop_size is not None else {"height": 18, "width": 18}
									snake_case      : str							=   do_normalize
									snake_case      : str							=   image_mean
									snake_case      : Optional[Any]							=   image_std
									snake_case      : str							=   do_convert_rgb



			def 							__lowercase				( self       :							Tuple		)       ->				Union[str, Any]:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									}



@require_torch
@require_vision
class 			_a     (   SCREAMING_SNAKE_CASE__    ,  unittest.TestCase):
			__magic_name__    				=    OwlViTImageProcessor if is_vision_available() else None



			def 							__lowercase				( self       :							Any		)       ->				List[Any]:
									snake_case      : Dict							=   OwlViTImageProcessingTester(self		)



			@property
			def 							__lowercase				( self       :							Optional[int]		)       ->				List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()



			def 							__lowercase				( self       :							List[Any]		)       ->				Union[str, Any]:
									snake_case      : List[Any]							=   self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_lowercase      ,  "do_resize"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "size"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_center_crop"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "center_crop"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_normalize"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "image_mean"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "image_std"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_convert_rgb"		)		)



			def 							__lowercase				( self       :							Any		)       ->				Any:
									snake_case      : List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict		)
									self.assertEqual(image_processor.size      ,  {"height": 18, "width": 18}		)
									self.assertEqual(image_processor.crop_size      ,  {"height": 18, "width": 18}		)

									snake_case      : int							=   self.image_processing_class.from_dict(self.image_processor_dict      ,  size=42      ,  crop_size=84		)
									self.assertEqual(image_processor.size      ,  {"height": 42, "width": 42}		)
									self.assertEqual(image_processor.crop_size      ,  {"height": 84, "width": 84}		)



			def 							__lowercase				( self       :							Any		)       ->				Optional[int]:
									# Initialize image_processing
									snake_case      : List[Any]							=   self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									snake_case      : Tuple							=   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      : Dict							=   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )

									# Test batched
									snake_case      : Optional[Any]							=   image_processing(_lowercase      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )



			def 							__lowercase				( self       :							Optional[Any]		)       ->				Dict:
									# Initialize image_processing
									snake_case      : Optional[int]							=   self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									snake_case      : int							=   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      : str							=   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )

									# Test batched
									snake_case      : str							=   image_processing(_lowercase      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )



			def 							__lowercase				( self       :							List[Any]		)       ->				List[str]:
									# Initialize image_processing
									snake_case      : Any							=   self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									snake_case      : Dict							=   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      : Union[str, Any]							=   image_processing(image_inputs[0]      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )

									# Test batched
									snake_case      : Any							=   image_processing(_lowercase      ,  return_tensors="pt"		).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,  (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )      ,  )








"""simple docstring"""







from typing import TYPE_CHECKING

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


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


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    '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

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



