
"""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 LevitImageProcessor




class  __lowerCamelCase  ( unittest.TestCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=7							,						UpperCAmelCase=3							,						UpperCAmelCase=18							,						UpperCAmelCase=30							,						UpperCAmelCase=400							,						UpperCAmelCase=True							,						UpperCAmelCase=None							,						UpperCAmelCase=True							,						UpperCAmelCase=None							,						UpperCAmelCase=True							,						UpperCAmelCase=[0.5, 0.5, 0.5]							,						UpperCAmelCase=[0.5, 0.5, 0.5]							,						):
							lowerCamelCase_					=  size if size is not None else {'''shortest_edge''': 18}
							lowerCamelCase_					=  crop_size if crop_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_center_crop
							lowerCamelCase_					=  crop_size
							lowerCamelCase_					=  do_normalize
							lowerCamelCase_					=  image_mean
							lowerCamelCase_					=  image_std





				def        UpperCAmelCase__						(				self   ):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}




@require_torch
@require_vision
class  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Tuple          =    LevitImageProcessor if is_vision_available() else None




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  LevitImageProcessingTester(self   )




				@property
				def        UpperCAmelCase__						(				self   ):
							return self.image_processor_tester.prepare_image_processor_dict()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''image_mean'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''image_std'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''do_normalize'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''do_resize'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''do_center_crop'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''size'''   )   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size							,						{'''shortest_edge''': 18}   )
							self.assertEqual(image_processor.crop_size							,						{'''height''': 18, '''width''': 18}   )

							lowerCamelCase_					=  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        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							# 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=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )							,						)

							# Test batched
							lowerCamelCase_					=  image_processing(UpperCAmelCase							,						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        UpperCAmelCase__						(				self   ):
							# 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=UpperCAmelCase							,						numpify=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )							,						)

							# Test batched
							lowerCamelCase_					=  image_processing(UpperCAmelCase							,						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        UpperCAmelCase__						(				self   ):
							# 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=UpperCAmelCase							,						torchify=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						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.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )							,						)

							# Test batched
							lowerCamelCase_					=  image_processing(UpperCAmelCase							,						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'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	Union[str, Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	List[Any]									=   0.01
				with locka.acquire():
								with pytest.raises(__A   ):
												a_		:	List[Any]									=   time.time()
												locka.acquire(__A   )
												assert time.time() - _start > timeout





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Tuple									=   '''a''' * 10_00 + '''.lock'''
				a_		:	List[Any]									=   FileLock(str(tmpdir / filename   )   )
				assert locka._lock_file.endswith('''.lock'''   )
				assert not locka._lock_file.endswith(__A   )
				assert len(os.path.basename(locka._lock_file   )   ) <= 2_55
				a_		:	Optional[int]									=   FileLock(tmpdir / filename   )
				with locka.acquire():
								with pytest.raises(__A   ):
												locka.acquire(0   )




