






import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def 						A__			(				__lowerCAmelCase		:			Any						):  # picklable for multiprocessing
						return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						A__			(				):
						with parallel_backend("""spark"""						):
												assert ParallelBackendConfig.backend_name == "spark"

						lowerCamelCase__         =  [1, 2, 3]
						with pytest.raises(__lowerCAmelCase						):
												with parallel_backend("""unsupported backend"""						):
																		map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=2						)

						with pytest.raises(__lowerCAmelCase						):
												with parallel_backend("""unsupported backend"""						):
																		map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=-1						)





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""   ,    [2, -1]						)
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  [1, 2]
						lowerCamelCase__         =  {"""a""": 1, """b""": 2}
						lowerCamelCase__         =  {"""a""": [1, 2], """b""": [3, 4]}
						lowerCamelCase__         =  {"""a""": {"""1""": 1}, """b""": 2}
						lowerCamelCase__         =  {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
						lowerCamelCase__         =  [2, 3]
						lowerCamelCase__         =  {"""a""": 2, """b""": 3}
						lowerCamelCase__         =  {"""a""": [2, 3], """b""": [4, 5]}
						lowerCamelCase__         =  {"""a""": {"""1""": 2}, """b""": 3}
						lowerCamelCase__         =  {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

						with parallel_backend("""spark"""						):
												assert map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=__lowerCAmelCase						) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=__lowerCAmelCase						) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=__lowerCAmelCase						) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=__lowerCAmelCase						) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase   ,    __lowerCAmelCase   ,    num_proc=__lowerCAmelCase						) == expected_map_nested_sa









'''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 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=7   ,_lowerCAmelCase=3   ,_lowerCAmelCase=18   ,_lowerCAmelCase=30   ,_lowerCAmelCase=4_00   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,_lowerCAmelCase=[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 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					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(_lowerCAmelCase   ,"""image_mean"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_std"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_normalize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_resize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_center_crop"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""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=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,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(_lowerCAmelCase   ,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=_lowerCAmelCase   ,numpify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,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(_lowerCAmelCase   ,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=_lowerCAmelCase   ,torchify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,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(_lowerCAmelCase   ,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"""],
								    )   ,)

