





'''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 			lowerCamelCase					(						unittest.TestCase  ):


					def __init__(					self	:  List[str]       ,     __snake_case	:  List[str]       ,     __snake_case	:  str=7       ,     __snake_case	:  Tuple=3       ,     __snake_case	:  Dict=18       ,     __snake_case	:  List[Any]=30       ,     __snake_case	:  Optional[Any]=4_00       ,     __snake_case	:  List[str]=True       ,     __snake_case	:  Tuple=None       ,     __snake_case	:  Dict=True       ,     __snake_case	:  Optional[int]=None       ,     __snake_case	:  List[str]=True       ,     ):





											'''simple docstring'''





											_snake_case:   Dict  					=		size if size is not None else {'shortest_edge': 20}
											_snake_case:   str  					=		crop_size if crop_size is not None else {'height': 18, 'width': 18}
											_snake_case:   str  					=		parent
											_snake_case:   List[str]  					=		batch_size
											_snake_case:   Tuple  					=		num_channels
											_snake_case:   str  					=		image_size
											_snake_case:   Optional[Any]  					=		min_resolution
											_snake_case:   int  					=		max_resolution
											_snake_case:   Union[str, Any]  					=		do_resize
											_snake_case:   str  					=		size
											_snake_case:   str  					=		do_center_crop
											_snake_case:   str  					=		crop_size
											_snake_case:   List[str]  					=		do_flip_channel_order




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											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 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    MobileViTImageProcessor if is_vision_available() else None


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   int  					=		MobileViTImageProcessingTester(self       )


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											return self.image_processor_tester.prepare_image_processor_dict()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(__snake_case       ,     'do_resize'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'size'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_center_crop'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'center_crop'       )       )
											self.assertTrue(hasattr(__snake_case       ,     'do_flip_channel_order'       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.image_processing_class.from_dict(self.image_processor_dict       )
											self.assertEqual(image_processor.size       ,     {'shortest_edge': 20}       )
											self.assertEqual(image_processor.crop_size       ,     {'height': 18, 'width': 18}       )

											_snake_case:   Optional[int]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											_snake_case:   Tuple  					=		prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=__snake_case       )
											for image in image_inputs:
																	self.assertIsInstance(__snake_case       ,     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:   List[str]  					=		image_processing(__snake_case       ,     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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





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

											# 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:   Dict  					=		image_processing(__snake_case       ,     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    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





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

											# 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:   Union[str, Any]  					=		image_processing(__snake_case       ,     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_tf_available,
    is_torch_available,
    is_vision_available,
)


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		A   : Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





