






"""simple docstring"""



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor







class        lowercase__      (							unittest.TestCase       ):


			def __init__(				self					:  Dict       ,	snake_case__					:  List[Any]       ,	snake_case__					:  List[str]=7       ,	snake_case__					:  Optional[Any]=3       ,	snake_case__					:  Dict=30       ,	snake_case__					:  Dict=400       ,	snake_case__					:  List[str]=True       ,	snake_case__					:  Union[str, Any]=None       ,	snake_case__					:  str=True       ,	snake_case__					:  str=[0.5, 0.5, 0.5]       ,	snake_case__					:  int=[0.5, 0.5, 0.5]       ,	snake_case__					:  int=True       ,	snake_case__					:  Optional[int]=1 / 255       ,	snake_case__					:  List[Any]=True       ,	):
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										lowerCamelCase_	:							List[str]									=size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
										lowerCamelCase_	:							List[str]									=parent
										lowerCamelCase_	:							Any									=batch_size
										lowerCamelCase_	:							Any									=num_channels
										lowerCamelCase_	:							List[Any]									=min_resolution
										lowerCamelCase_	:							Any									=max_resolution
										lowerCamelCase_	:							str									=do_resize
										lowerCamelCase_	:							Union[str, Any]									=size
										lowerCamelCase_	:							Dict									=do_normalize
										lowerCamelCase_	:							Dict									=image_mean
										lowerCamelCase_	:							Any									=image_std
										lowerCamelCase_	:							List[Any]									=do_rescale
										lowerCamelCase_	:							Tuple									=rescale_factor
										lowerCamelCase_	:							Dict									=do_pad


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  str       ,	snake_case__					:  Any=False ):
										if not batched:
																	lowerCamelCase_	:							str									=image_inputs[0]
																	if isinstance(snake_case__       ,	Image.Image ):
																								lowerCamelCase_							,						lowerCamelCase_	:							Dict									=image.size
																	else:
																								lowerCamelCase_							,						lowerCamelCase_	:							int									=image.shape[1], image.shape[2]
																	if w < h:
																								lowerCamelCase_	:							Union[str, Any]									=int(self.size["shortest_edge"] * h / w )
																								lowerCamelCase_	:							Dict									=self.size["shortest_edge"]
																	elif w > h:
																								lowerCamelCase_	:							int									=self.size["shortest_edge"]
																								lowerCamelCase_	:							Dict									=int(self.size["shortest_edge"] * w / h )
																	else:
																								lowerCamelCase_	:							Optional[int]									=self.size["shortest_edge"]
																								lowerCamelCase_	:							Tuple									=self.size["shortest_edge"]

										else:
																	lowerCamelCase_	:							Dict									=[]
																	for image in image_inputs:
																								lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=self.get_expected_values([image] )
																								expected_values.append((expected_height, expected_width) )
																	lowerCamelCase_	:							Tuple									=max(snake_case__       ,	key=lambda snake_case__ : item[0] )[0]
																	lowerCamelCase_	:							Tuple									=max(snake_case__       ,	key=lambda snake_case__ : item[1] )[1]

										return expected_height, expected_width







@require_torch
@require_vision
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[Any]             =			ConditionalDetrImageProcessor if is_vision_available() else None


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							Any									=ConditionalDetrImageProcessingTester(self )


			@property
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Optional[int]									=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(snake_case__       ,	"image_mean" ) )
										self.assertTrue(hasattr(snake_case__       ,	"image_std" ) )
										self.assertTrue(hasattr(snake_case__       ,	"do_normalize" ) )
										self.assertTrue(hasattr(snake_case__       ,	"do_resize" ) )
										self.assertTrue(hasattr(snake_case__       ,	"size" ) )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Optional[int]									=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size       ,	{"shortest_edge": 18, "longest_edge": 1333} )
										self.assertEqual(image_processor.do_pad       ,	snake_case__ )

										lowerCamelCase_	:							List[Any]									=self.image_processing_class.from_dict(
										    self.image_processor_dict       ,	size=42       ,	max_size=84       ,	pad_and_return_pixel_mask=snake_case__ )
										self.assertEqual(image_processor.size       ,	{"shortest_edge": 42, "longest_edge": 84} )
										self.assertEqual(image_processor.do_pad       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										pass


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										# Initialize image_processing
										lowerCamelCase_	:							Optional[int]									=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										lowerCamelCase_	:							int									=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
										lowerCamelCase_	:							List[Any]									=image_processing(image_inputs[0]       ,	return_tensors="pt" ).pixel_values

										lowerCamelCase_							,						lowerCamelCase_	:							Tuple									=self.image_processor_tester.get_expected_values(snake_case__ )

										self.assertEqual(
										    encoded_images.shape       ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,	)

										# Test batched
										lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=self.image_processor_tester.get_expected_values(snake_case__       ,	batched=snake_case__ )

										lowerCamelCase_	:							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,
										        expected_height,
										        expected_width,
										    )       ,	)


			def      UpperCAmelCase__						(				self					:  List[str] ):
										# Initialize image_processing
										lowerCamelCase_	:							Optional[Any]									=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										lowerCamelCase_	:							List[Any]									=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
										lowerCamelCase_	:							str									=image_processing(image_inputs[0]       ,	return_tensors="pt" ).pixel_values

										lowerCamelCase_							,						lowerCamelCase_	:							List[Any]									=self.image_processor_tester.get_expected_values(snake_case__ )

										self.assertEqual(
										    encoded_images.shape       ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,	)

										# Test batched
										lowerCamelCase_	:							Union[str, Any]									=image_processing(snake_case__       ,	return_tensors="pt" ).pixel_values

										lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=self.image_processor_tester.get_expected_values(snake_case__       ,	batched=snake_case__ )

										self.assertEqual(
										    encoded_images.shape       ,	(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,	)


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										# Initialize image_processing
										lowerCamelCase_	:							str									=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										lowerCamelCase_	:							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
										lowerCamelCase_	:							Dict									=image_processing(image_inputs[0]       ,	return_tensors="pt" ).pixel_values

										lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=self.image_processor_tester.get_expected_values(snake_case__ )

										self.assertEqual(
										    encoded_images.shape       ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,	)

										# Test batched
										lowerCamelCase_	:							List[Any]									=image_processing(snake_case__       ,	return_tensors="pt" ).pixel_values

										lowerCamelCase_							,						lowerCamelCase_	:							int									=self.image_processor_tester.get_expected_values(snake_case__       ,	batched=snake_case__ )

										self.assertEqual(
										    encoded_images.shape       ,	(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,	)


			@slow
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										# prepare image and target
										lowerCamelCase_	:							List[str]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,	"r" ) as f:
																	lowerCamelCase_	:							Optional[Any]									=json.loads(f.read() )

										lowerCamelCase_	:							str									={"image_id": 3_9769, "annotations": target}

										# encode them
										lowerCamelCase_	:							Any									=ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
										lowerCamelCase_	:							Tuple									=image_processing(images=snake_case__       ,	annotations=snake_case__       ,	return_tensors="pt" )

										# verify pixel values
										lowerCamelCase_	:							str									=torch.Size([1, 3, 800, 1066] )
										self.assertEqual(encoding["pixel_values"].shape       ,	snake_case__ )

										lowerCamelCase_	:							List[Any]									=torch.tensor([0.2_796, 0.3_138, 0.3_481] )
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,	snake_case__       ,	atol=1E-4 ) )

										# verify area
										lowerCamelCase_	:							Dict									=torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,	snake_case__ ) )
										# verify boxes
										lowerCamelCase_	:							Any									=torch.Size([6, 4] )
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,	snake_case__ )
										lowerCamelCase_	:							List[str]									=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,	snake_case__       ,	atol=1E-3 ) )
										# verify image_id
										lowerCamelCase_	:							List[str]									=torch.tensor([3_9769] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,	snake_case__ ) )
										# verify is_crowd
										lowerCamelCase_	:							List[str]									=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,	snake_case__ ) )
										# verify class_labels
										lowerCamelCase_	:							Any									=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,	snake_case__ ) )
										# verify orig_size
										lowerCamelCase_	:							Union[str, Any]									=torch.tensor([480, 640] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,	snake_case__ ) )
										# verify size
										lowerCamelCase_	:							Optional[Any]									=torch.tensor([800, 1066] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,	snake_case__ ) )


			@slow
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										# prepare image, target and masks_path
										lowerCamelCase_	:							List[str]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,	"r" ) as f:
																	lowerCamelCase_	:							Union[str, Any]									=json.loads(f.read() )

										lowerCamelCase_	:							int									={"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

										lowerCamelCase_	:							Optional[int]									=pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

										# encode them
										lowerCamelCase_	:							List[Any]									=ConditionalDetrImageProcessor(format="coco_panoptic" )
										lowerCamelCase_	:							Dict									=image_processing(images=snake_case__       ,	annotations=snake_case__       ,	masks_path=snake_case__       ,	return_tensors="pt" )

										# verify pixel values
										lowerCamelCase_	:							Any									=torch.Size([1, 3, 800, 1066] )
										self.assertEqual(encoding["pixel_values"].shape       ,	snake_case__ )

										lowerCamelCase_	:							Optional[int]									=torch.tensor([0.2_796, 0.3_138, 0.3_481] )
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,	snake_case__       ,	atol=1E-4 ) )

										# verify area
										lowerCamelCase_	:							Dict									=torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,	snake_case__ ) )
										# verify boxes
										lowerCamelCase_	:							int									=torch.Size([6, 4] )
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,	snake_case__ )
										lowerCamelCase_	:							Optional[Any]									=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,	snake_case__       ,	atol=1E-3 ) )
										# verify image_id
										lowerCamelCase_	:							Tuple									=torch.tensor([3_9769] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,	snake_case__ ) )
										# verify is_crowd
										lowerCamelCase_	:							List[str]									=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,	snake_case__ ) )
										# verify class_labels
										lowerCamelCase_	:							int									=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,	snake_case__ ) )
										# verify masks
										lowerCamelCase_	:							Tuple									=82_2873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,	snake_case__ )
										# verify orig_size
										lowerCamelCase_	:							List[str]									=torch.tensor([480, 640] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,	snake_case__ ) )
										# verify size
										lowerCamelCase_	:							List[Any]									=torch.tensor([800, 1066] )
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,	snake_case__ ) )











"""simple docstring"""



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 			_snake_case    (					lowerCamelCase__     :   Any  )    ->     Union[str, Any]:  # picklable for multiprocessing
							return i + 1

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 			_snake_case    (					)    ->     List[Any]:
							with parallel_backend("spark"  ):
														assert ParallelBackendConfig.backend_name == "spark"

							lowerCamelCase_	:							Optional[Any]									=[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 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     Optional[Any]:
							lowerCamelCase_	:							str									=[1, 2]
							lowerCamelCase_	:							List[str]									={"a": 1, "b": 2}
							lowerCamelCase_	:							List[str]									={"a": [1, 2], "b": [3, 4]}
							lowerCamelCase_	:							Optional[int]									={"a": {"1": 1}, "b": 2}
							lowerCamelCase_	:							int									={"a": 1, "b": 2, "c": 3, "d": 4}
							lowerCamelCase_	:							Optional[int]									=[2, 3]
							lowerCamelCase_	:							List[Any]									={"a": 2, "b": 3}
							lowerCamelCase_	:							int									={"a": [2, 3], "b": [4, 5]}
							lowerCamelCase_	:							str									={"a": {"1": 2}, "b": 3}
							lowerCamelCase_	:							Dict									={"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



