
import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def __init__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=7       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3       ,							SCREAMING_SNAKE_CASE_  :			Any=1_8       ,							SCREAMING_SNAKE_CASE_  :			Tuple=3_0       ,							SCREAMING_SNAKE_CASE_  :			int=4_0_0       ,							SCREAMING_SNAKE_CASE_  :			Dict=True       ,							SCREAMING_SNAKE_CASE_  :			str=None       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[0.5, 0.5, 0.5]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=[0.5, 0.5, 0.5]       ,							)       ->     Optional[Any]:
					lowercase_       =					size if size is not None else {'''height''': 1_8, '''width''': 1_8}
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					num_channels
					lowercase_       =					image_size
					lowercase_       =					min_resolution
					lowercase_       =					max_resolution
					lowercase_       =					do_resize
					lowercase_       =					size
					lowercase_       =					do_normalize
					lowercase_       =					image_mean
					lowercase_       =					image_std




	def 						_lowercase  (       self  :			Tuple       )       ->     List[Any]:
					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  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	DPTImageProcessor if is_vision_available() else None
	def 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					DPTImageProcessingTester(self       )
	@property
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					return self.image_processor_tester.prepare_image_processor_dict()
	def 						_lowercase  (       self  :			int       )       ->     Dict:
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,							'''image_mean'''       )       )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,							'''image_std'''       )       )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,							'''do_normalize'''       )       )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,							'''do_resize'''       )       )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,							'''size'''       )       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[int]:
					lowercase_       =					self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size       ,							{'''height''': 1_8, '''width''': 1_8}       )

					lowercase_       =					self.image_processing_class.from_dict(self.image_processor_dict       ,							size=4_2       )
					self.assertEqual(image_processor.size       ,							{'''height''': 4_2, '''width''': 4_2}       )
	def 						_lowercase  (       self  :			Tuple       )       ->     List[Any]:
					# Initialize image_processing
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					lowercase_       =					prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=SCREAMING_SNAKE_CASE_       )
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,							Image.Image       )

					# Test not batched input
					lowercase_       =					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
					lowercase_       =					image_processing(SCREAMING_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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)
	def 						_lowercase  (       self  :			str       )       ->     Optional[int]:
					# Initialize image_processing
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					lowercase_       =					prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=SCREAMING_SNAKE_CASE_       ,							numpify=SCREAMING_SNAKE_CASE_       )
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,							np.ndarray       )

					# Test not batched input
					lowercase_       =					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
					lowercase_       =					image_processing(SCREAMING_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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)




	def 						_lowercase  (       self  :			List[str]       )       ->     str:
					# Initialize image_processing
					lowercase_       =					self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					lowercase_       =					prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=SCREAMING_SNAKE_CASE_       ,							torchify=SCREAMING_SNAKE_CASE_       )
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,							torch.Tensor       )

					# Test not batched input
					lowercase_       =					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
					lowercase_       =					image_processing(SCREAMING_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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )       ,							)






from typing import TYPE_CHECKING

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


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    '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  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




