



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 ViTImageProcessor
class lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase=7   ,       lowerCamelCase=3   ,       lowerCamelCase=18   ,       lowerCamelCase=30   ,       lowerCamelCase=400   ,       lowerCamelCase=True   ,       lowerCamelCase=None   ,       lowerCamelCase=True   ,       lowerCamelCase=[0.5, 0.5, 0.5]   ,       lowerCamelCase=[0.5, 0.5, 0.5]   ,       ):



				'''simple docstring'''


				a__					=  size if size is not None else {"""height""": 18, """width""": 18}
				a__					=  parent
				a__					=  batch_size
				a__					=  num_channels
				a__					=  image_size
				a__					=  min_resolution
				a__					=  max_resolution
				a__					=  do_resize
				a__					=  size
				a__					=  do_normalize
				a__					=  image_mean
				a__					=  image_std







	def 				_A			(	self       ):



				'''simple docstring'''


				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}


@require_torch
@require_vision
class lowerCAmelCase_       (   A_ ,unittest.TestCase			):







	'''simple docstring'''




	A_       :     Optional[int]									=		ViTImageProcessor if is_vision_available() else None

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  ViTImageProcessingTester(self       )

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.image_processing_class(**self.image_processor_dict       )
				self.assertTrue(hasattr(lowerCamelCase   ,       """image_mean"""       )       )
				self.assertTrue(hasattr(lowerCamelCase   ,       """image_std"""       )       )
				self.assertTrue(hasattr(lowerCamelCase   ,       """do_normalize"""       )       )
				self.assertTrue(hasattr(lowerCamelCase   ,       """do_resize"""       )       )
				self.assertTrue(hasattr(lowerCamelCase   ,       """size"""       )       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.image_processing_class.from_dict(self.image_processor_dict       )
				self.assertEqual(image_processor.size   ,       {"""height""": 18, """width""": 18}       )

				a__					=  self.image_processing_class.from_dict(self.image_processor_dict   ,       size=42       )
				self.assertEqual(image_processor.size   ,       {"""height""": 42, """width""": 42}       )

	def 				_A			(	self       ):



				'''simple docstring'''


				pass

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.image_processing_class(**self.image_processor_dict       )
				# create random PIL images
				a__					=  prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowerCamelCase       )
				for image in image_inputs:
							self.assertIsInstance(lowerCamelCase   ,       Image.Image       )

				# Test not batched input
				a__					=  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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )   ,       )

				# Test batched
				a__					=  image_processing(lowerCamelCase   ,       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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )   ,       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.image_processing_class(**self.image_processor_dict       )
				# create random numpy tensors
				a__					=  prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowerCamelCase   ,       numpify=lowerCamelCase       )
				for image in image_inputs:
							self.assertIsInstance(lowerCamelCase   ,       np.ndarray       )

				# Test not batched input
				a__					=  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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )   ,       )

				# Test batched
				a__					=  image_processing(lowerCamelCase   ,       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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )   ,       )







	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.image_processing_class(**self.image_processor_dict       )
				# create random PyTorch tensors
				a__					=  prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=lowerCamelCase   ,       torchify=lowerCamelCase       )
				for image in image_inputs:
							self.assertIsInstance(lowerCamelCase   ,       torch.Tensor       )

				# Test not batched input
				a__					=  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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )   ,       )

				# Test batched
				a__					=  image_processing(lowerCamelCase   ,       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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )   ,       )






from .prime_check import Test

Test()

