



'''simple docstring'''

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 __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=7     ,	A_=3     ,	A_=18     ,	A_=30     ,	A_=400     ,	A_=True     ,	A_=None     ,	A_=True     ,	A_=[0.5, 0.5, 0.5]     ,	A_=[0.5, 0.5, 0.5]     ,	)     ->     List[str]:
							lowerCAmelCase   =     size if size is not None else {"""height""": 18, """width""": 18}
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     min_resolution
							lowerCAmelCase   =     max_resolution
							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     size
							lowerCAmelCase   =     do_normalize
							lowerCAmelCase   =     image_mean
							lowerCAmelCase   =     image_std




						def     __snake_case   ( self				)     ->     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 __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Dict									=		DPTImageProcessor if is_vision_available() else None


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     DPTImageProcessingTester(self				)


						@property
						def     __snake_case   ( self				)     ->     Any:
							return self.image_processor_tester.prepare_image_processor_dict()


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     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     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size     ,	{"""height""": 18, """width""": 18}				)

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


						def     __snake_case   ( self				)     ->     List[Any]:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	Image.Image				)

							# Test not batched input
							lowerCAmelCase   =     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
							lowerCAmelCase   =     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     __snake_case   ( self				)     ->     str:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCAmelCase   =     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
							lowerCAmelCase   =     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
							lowerCAmelCase   =     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     __snake_case   ( self				)     ->     int:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCAmelCase   =     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
							lowerCAmelCase   =     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
							lowerCAmelCase   =     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'''

import pytest

import datasets


# Import fixture modules as plugins
UpperCAmelCase     		=   ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						Optional[int]:







	"""simple docstring"""
	# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
	for item in items:
		if any(marker in item.keywords for marker in ["""integration""", """unit"""]							):
			continue
		item.add_marker(pytest.mark.unit							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						Any:







	"""simple docstring"""
	config.addinivalue_line("""markers"""							,		"""torchaudio_latest: mark test to run with torchaudio>=0.12"""							)






@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE							)
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Dict							)      ->						str:







	"""simple docstring"""
	# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
	lowerCAmelCase   =     tmp_path_factory.getbasetemp() / """cache"""
	lowerCAmelCase   =     test_hf_cache_home / """datasets"""
	lowerCAmelCase   =     test_hf_cache_home / """metrics"""
	lowerCAmelCase   =     test_hf_cache_home / """modules"""
	monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase   =     test_hf_datasets_cache / """downloads"""
	monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""							,		str(_SCREAMING_SNAKE_CASE							)							)
	lowerCAmelCase   =     test_hf_datasets_cache / """downloads""" / """extracted"""
	monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""							,		str(_SCREAMING_SNAKE_CASE							)							)






@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE							,		scope="""session"""							)
def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	datasets.disable_progress_bar()






@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE							)
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							)      ->						Optional[int]:







	"""simple docstring"""
	# don't take tests into account when counting downloads
	monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""							,		_SCREAMING_SNAKE_CASE							)






@pytest.fixture
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						str:







	"""simple docstring"""
	# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
	# To be removed once SQLAlchemy 2.0 supported
	monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""							,		_SCREAMING_SNAKE_CASE							)