






"""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 ViTImageProcessor







class 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : Any					,A_       : int=7					,A_       : Tuple=3					,A_       : Optional[Any]=18					,A_       : List[str]=30					,A_       : Union[str, Any]=400					,A_       : Any=True					,A_       : List[str]=None					,A_       : Any=True					,A_       : List[str]=[0.5, 0.5, 0.5]					,A_       : Dict=[0.5, 0.5, 0.5]					,)       ->				Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				List[str]:
							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_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      ViTImageProcessor if is_vision_available() else None


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[str]:
							A  				=				ViTImageProcessingTester(self )


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


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Any:
							A  				=				self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(A_					,'image_mean' ) )
							self.assertTrue(hasattr(A_					,'image_std' ) )
							self.assertTrue(hasattr(A_					,'do_normalize' ) )
							self.assertTrue(hasattr(A_					,'do_resize' ) )
							self.assertTrue(hasattr(A_					,'size' ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							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   _SCREAMING_SNAKE_CASE (	self       : int )       ->				int:
							pass


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int] )       ->				Optional[Any]:
							# Initialize image_processing
							A  				=				self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							A  				=				prepare_image_inputs(self.image_processor_tester					,equal_resolution=A_ )
							for image in image_inputs:
								self.assertIsInstance(A_					,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(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.size['height'],
							        self.image_processor_tester.size['width'],
							    )					,)


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				Union[str, Any]:
							# Initialize image_processing
							A  				=				self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							A  				=				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
							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(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.size['height'],
							        self.image_processor_tester.size['width'],
							    )					,)




						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[Any]:
							# Initialize image_processing
							A  				=				self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							A  				=				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
							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(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.size['height'],
							        self.image_processor_tester.size['width'],
							    )					,)

"""simple docstring"""


from typing import TYPE_CHECKING

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


_lowercase											=			{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			['''ViltFeatureExtractor''']
		_lowercase											=			['''ViltImageProcessor''']
		_lowercase											=			['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		_lowercase											=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)