


'''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 MobileViTImageProcessor




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=7							,      a_=3							,      a_=1_8							,      a_=3_0							,      a_=4_0_0							,      a_=True							,      a_=None							,      a_=True							,      a_=None							,      a_=True							,      )    ->  Optional[int]:
					lowercase						:		Optional[Any]   					=		size if size is not None else {"shortest_edge": 2_0}
					lowercase						:		Tuple   					=		crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
					lowercase						:		Optional[int]   					=		parent
					lowercase						:		Union[str, Any]   					=		batch_size
					lowercase						:		str   					=		num_channels
					lowercase						:		List[str]   					=		image_size
					lowercase						:		int   					=		min_resolution
					lowercase						:		List[Any]   					=		max_resolution
					lowercase						:		Union[str, Any]   					=		do_resize
					lowercase						:		Union[str, Any]   					=		size
					lowercase						:		str   					=		do_center_crop
					lowercase						:		Optional[int]   					=		crop_size
					lowercase						:		Optional[Any]   					=		do_flip_channel_order


				def 	a__		(     self							)    ->  List[str]:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_flip_channel_order": self.do_flip_channel_order,
					}




@require_torch
@require_vision
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				MobileViTImageProcessor if is_vision_available() else None

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		List[Any]   					=		MobileViTImageProcessingTester(self							)

				@property
				def 	a__		(     self							)    ->  Union[str, Any]:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(a_							,      "do_resize"							)							)
					self.assertTrue(hasattr(a_							,      "size"							)							)
					self.assertTrue(hasattr(a_							,      "do_center_crop"							)							)
					self.assertTrue(hasattr(a_							,      "center_crop"							)							)
					self.assertTrue(hasattr(a_							,      "do_flip_channel_order"							)							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Tuple   					=		self.image_processing_class.from_dict(self.image_processor_dict							)
					self.assertEqual(image_processor.size							,      {"shortest_edge": 2_0}							)
					self.assertEqual(image_processor.crop_size							,      {"height": 1_8, "width": 1_8}							)

					lowercase						:		str   					=		self.image_processing_class.from_dict(self.image_processor_dict							,      size=4_2							,      crop_size=8_4							)
					self.assertEqual(image_processor.size							,      {"shortest_edge": 4_2}							)
					self.assertEqual(image_processor.crop_size							,      {"height": 8_4, "width": 8_4}							)

				def 	a__		(     self							)    ->  Optional[Any]:
					pass

				def 	a__		(     self							)    ->  Dict:
					# Initialize image_processing
					lowercase						:		str   					=		self.image_processing_class(**self.image_processor_dict							)
					# create random PIL images
					lowercase						:		Tuple   					=		prepare_image_inputs(self.image_processor_tester							,      equal_resolution=a_							)
					for image in image_inputs:
						self.assertIsInstance(a_							,      Image.Image							)

					# Test not batched input
					lowercase						:		List[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
					lowercase						:		List[Any]   					=		image_processing(a_							,      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 	a__		(     self							)    ->  Tuple:
					# Initialize image_processing
					lowercase						:		Optional[Any]   					=		self.image_processing_class(**self.image_processor_dict							)
					# create random numpy tensors
					lowercase						:		Any   					=		prepare_image_inputs(self.image_processor_tester							,      equal_resolution=a_							,      numpify=a_							)
					for image in image_inputs:
						self.assertIsInstance(a_							,      np.ndarray							)

					# Test not batched input
					lowercase						:		Optional[int]   					=		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
					lowercase						:		Optional[Any]   					=		image_processing(a_							,      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 	a__		(     self							)    ->  Optional[int]:
					# Initialize image_processing
					lowercase						:		Optional[Any]   					=		self.image_processing_class(**self.image_processor_dict							)
					# create random PyTorch tensors
					lowercase						:		Dict   					=		prepare_image_inputs(self.image_processor_tester							,      equal_resolution=a_							,      torchify=a_							)
					for image in image_inputs:
						self.assertIsInstance(a_							,      torch.Tensor							)

					# Test not batched input
					lowercase						:		Tuple   					=		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
					lowercase						:		Tuple   					=		image_processing(a_							,      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


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


