
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 DetaImageProcessor

class       a		(   unittest.TestCase					):







							def __init__(						self  :  Dict      ,				snake_case__  :  List[str]      ,				snake_case__  :  Optional[Any]=7      ,				snake_case__  :  Dict=3      ,				snake_case__  :  Tuple=30      ,				snake_case__  :  Any=400      ,				snake_case__  :  Any=True      ,				snake_case__  :  str=None      ,				snake_case__  :  List[Any]=True      ,				snake_case__  :  Tuple=[0.5, 0.5, 0.5]      ,				snake_case__  :  Tuple=[0.5, 0.5, 0.5]      ,				snake_case__  :  Dict=True      ,				snake_case__  :  Optional[Any]=1 / 255      ,				snake_case__  :  Optional[int]=True      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							batch_size
								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							min_resolution
								__lowerCAmelCase         =							max_resolution
								__lowerCAmelCase         =							do_resize
								__lowerCAmelCase         =							size
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							image_mean
								__lowerCAmelCase         =							image_std
								__lowerCAmelCase         =							do_rescale
								__lowerCAmelCase         =							rescale_factor
								__lowerCAmelCase         =							do_pad







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								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  :  int      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any=False ):
								"""simple docstring"""







								if not batched:
									__lowerCAmelCase         =							image_inputs[0]
									if isinstance(snake_case__      ,				Image.Image ):
										__lowerCAmelCase   ,						__lowerCAmelCase         =							image.size
									else:
										__lowerCAmelCase   ,						__lowerCAmelCase         =							image.shape[1], image.shape[2]
									if w < h:
										__lowerCAmelCase         =							int(self.size["shortest_edge"] * h / w )
										__lowerCAmelCase         =							self.size["shortest_edge"]
									elif w > h:
										__lowerCAmelCase         =							self.size["shortest_edge"]
										__lowerCAmelCase         =							int(self.size["shortest_edge"] * w / h )
									else:
										__lowerCAmelCase         =							self.size["shortest_edge"]
										__lowerCAmelCase         =							self.size["shortest_edge"]

								else:
									__lowerCAmelCase         =							[]
									for image in image_inputs:
										__lowerCAmelCase   ,						__lowerCAmelCase         =							self.get_expected_values([image] )
										expected_values.append((expected_height, expected_width) )
									__lowerCAmelCase         =							max(snake_case__      ,				key=lambda snake_case__ : item[0] )[0]
									__lowerCAmelCase         =							max(snake_case__      ,				key=lambda snake_case__ : item[1] )[1]

								return expected_height, expected_width

@require_torch
@require_vision
class       a		(   __UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						Dict   							=  DetaImageProcessor if is_vision_available() else None







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DetaImageProcessingTester(self )







							@property
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								return self.image_processor_tester.prepare_image_processor_dict()







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							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__      ,				"do_rescale" ) )
								self.assertTrue(hasattr(snake_case__      ,				"do_pad" ) )
								self.assertTrue(hasattr(snake_case__      ,				"size" ) )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class.from_dict(self.image_processor_dict )
								self.assertEqual(image_processor.size      ,				{"shortest_edge": 18, "longest_edge": 1_333} )
								self.assertEqual(image_processor.do_pad      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								# create random PIL images
								__lowerCAmelCase         =							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         =							image_processing(image_inputs[0]      ,				return_tensors="pt" ).pixel_values

								__lowerCAmelCase   ,						__lowerCAmelCase         =							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         =							self.image_processor_tester.get_expected_values(snake_case__      ,				batched=snake_case__ )

								__lowerCAmelCase         =							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  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								# create random numpy tensors
								__lowerCAmelCase         =							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         =							image_processing(image_inputs[0]      ,				return_tensors="pt" ).pixel_values

								__lowerCAmelCase   ,						__lowerCAmelCase         =							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         =							image_processing(snake_case__      ,				return_tensors="pt" ).pixel_values

								__lowerCAmelCase   ,						__lowerCAmelCase         =							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  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								__lowerCAmelCase         =							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         =							image_processing(image_inputs[0]      ,				return_tensors="pt" ).pixel_values

								__lowerCAmelCase   ,						__lowerCAmelCase         =							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         =							image_processing(snake_case__      ,				return_tensors="pt" ).pixel_values

								__lowerCAmelCase   ,						__lowerCAmelCase         =							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  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,				"r" ) as f:
									__lowerCAmelCase         =							json.loads(f.read() )

								__lowerCAmelCase         =							{"image_id": 39_769, "annotations": target}

								# encode them
								__lowerCAmelCase         =							DetaImageProcessor()
								__lowerCAmelCase         =							image_processing(images=snake_case__      ,				annotations=snake_case__      ,				return_tensors="pt" )

								# verify pixel values
								__lowerCAmelCase         =							torch.Size([1, 3, 800, 1_066] )
								self.assertEqual(encoding["pixel_values"].shape      ,				snake_case__ )

								__lowerCAmelCase         =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,				snake_case__      ,				atol=1E-4 ) )

								# verify area
								__lowerCAmelCase         =							torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,				snake_case__ ) )
								# verify boxes
								__lowerCAmelCase         =							torch.Size([6, 4] )
								self.assertEqual(encoding["labels"][0]["boxes"].shape      ,				snake_case__ )
								__lowerCAmelCase         =							torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,				snake_case__      ,				atol=1E-3 ) )
								# verify image_id
								__lowerCAmelCase         =							torch.tensor([39_769] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,				snake_case__ ) )
								# verify is_crowd
								__lowerCAmelCase         =							torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,				snake_case__ ) )
								# verify class_labels
								__lowerCAmelCase         =							torch.tensor([75, 75, 63, 65, 17, 17] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,				snake_case__ ) )
								# verify orig_size
								__lowerCAmelCase         =							torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,				snake_case__ ) )
								# verify size
								__lowerCAmelCase         =							torch.tensor([800, 1_066] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,				snake_case__ ) )







							@slow
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,				"r" ) as f:
									__lowerCAmelCase         =							json.loads(f.read() )

								__lowerCAmelCase         =							{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

								__lowerCAmelCase         =							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

								# encode them
								__lowerCAmelCase         =							DetaImageProcessor(format="coco_panoptic" )
								__lowerCAmelCase         =							image_processing(images=snake_case__      ,				annotations=snake_case__      ,				masks_path=snake_case__      ,				return_tensors="pt" )

								# verify pixel values
								__lowerCAmelCase         =							torch.Size([1, 3, 800, 1_066] )
								self.assertEqual(encoding["pixel_values"].shape      ,				snake_case__ )

								__lowerCAmelCase         =							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,				snake_case__      ,				atol=1E-4 ) )

								# verify area
								__lowerCAmelCase         =							torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,				snake_case__ ) )
								# verify boxes
								__lowerCAmelCase         =							torch.Size([6, 4] )
								self.assertEqual(encoding["labels"][0]["boxes"].shape      ,				snake_case__ )
								__lowerCAmelCase         =							torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,				snake_case__      ,				atol=1E-3 ) )
								# verify image_id
								__lowerCAmelCase         =							torch.tensor([39_769] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,				snake_case__ ) )
								# verify is_crowd
								__lowerCAmelCase         =							torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,				snake_case__ ) )
								# verify class_labels
								__lowerCAmelCase         =							torch.tensor([17, 17, 63, 75, 75, 93] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,				snake_case__ ) )
								# verify masks
								__lowerCAmelCase         =							822_873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,				snake_case__ )
								# verify orig_size
								__lowerCAmelCase         =							torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,				snake_case__ ) )
								# verify size
								__lowerCAmelCase         =							torch.tensor([800, 1_066] )
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,				snake_case__ ) )








import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCamelCase_   	=	pytest.mark.integration






@pytest.mark.parametrize("path"			,							["paws", "csv"]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	inspect_dataset(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							path + ".py"
	assert script_name in os.listdir(UpperCamelCase  )
	assert "__pycache__" not in os.listdir(UpperCamelCase  )






@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"  )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"  )
@pytest.mark.parametrize("path"			,							["accuracy"]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


	inspect_metric(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							path + ".py"
	assert script_name in os.listdir(UpperCamelCase  )
	assert "__pycache__" not in os.listdir(UpperCamelCase  )






@pytest.mark.parametrize(
    "path, config_name, expected_splits"			,							[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     int			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_config_info(UpperCamelCase			,							config_name=UpperCamelCase  )
	assert info.config_name == config_name
	assert list(info.splits.keys()  ) == expected_splits






@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,							[
        ("paws", None, ValueError),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Any			,							UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	with pytest.raises(UpperCamelCase  ):
		get_dataset_config_info(UpperCamelCase			,							config_name=UpperCamelCase  )






@pytest.mark.parametrize(
    "path, expected"			,							[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_config_names(UpperCamelCase  )
	assert expected in config_names






@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"			,							[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_infos(UpperCamelCase  )
	assert list(infos.keys()  ) == expected_configs
	__lowerCAmelCase         =							expected_configs[0]
	assert expected_config in infos
	__lowerCAmelCase         =							infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()  ) == expected_splits_in_first_config






@pytest.mark.parametrize(
    "path, expected_config, expected_splits"			,							[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     int			,							UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_infos(UpperCamelCase  )
	assert expected_config in infos
	__lowerCAmelCase         =							infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()  ) == expected_splits







@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,							[
        ("paws", None, ValueError),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	with pytest.raises(UpperCamelCase  ):
		get_dataset_split_names(UpperCamelCase			,							config_name=UpperCamelCase  )






