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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase=7		,     lowercase=3		,     lowercase=3_0		,     lowercase=4_0_0		,     lowercase=True		,     lowercase=None		,     lowercase=True		,     lowercase=[0.5, 0.5, 0.5]		,     lowercase=[0.5, 0.5, 0.5]		,     lowercase=True		,     lowercase=1 / 2_5_5		,     lowercase=True		,     ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
						A_		:    Union[str, Any]         =				parent
						A_		:    int         =				batch_size
						A_		:    List[str]         =				num_channels
						A_		:    Union[str, Any]         =				min_resolution
						A_		:    Optional[Any]         =				max_resolution
						A_		:    str         =				do_resize
						A_		:    int         =				size
						A_		:    Tuple         =				do_normalize
						A_		:    Dict         =				image_mean
						A_		:    Tuple         =				image_std
						A_		:    int         =				do_rescale
						A_		:    Optional[Any]         =				rescale_factor
						A_		:    List[str]         =				do_pad
				def   lowerCAmelCase_       (						self ):



						"""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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=False ):



						"""simple docstring"""





						if not batched:
								A_		:    Any         =				image_inputs[0]
								if isinstance(lowercase		,     Image.Image ):
										A_       ,      A_		:    Dict         =				image.size
								else:
										A_       ,      A_		:    str         =				image.shape[1], image.shape[2]
								if w < h:
										A_		:    List[Any]         =				int(self.size['shortest_edge'] * h / w )
										A_		:    Tuple         =				self.size['shortest_edge']
								elif w > h:
										A_		:    Optional[Any]         =				self.size['shortest_edge']
										A_		:    Dict         =				int(self.size['shortest_edge'] * w / h )
								else:
										A_		:    Optional[Any]         =				self.size['shortest_edge']
										A_		:    Optional[int]         =				self.size['shortest_edge']

						else:
								A_		:    List[Any]         =				[]
								for image in image_inputs:
										A_       ,      A_		:    List[str]         =				self.get_expected_values([image] )
										expected_values.append((expected_height, expected_width) )
								A_		:    Optional[int]         =				max(lowercase		,     key=lambda lowercase : item[0] )[0]
								A_		:    Optional[int]         =				max(lowercase		,     key=lambda lowercase : item[1] )[1]

						return expected_height, expected_width





@require_torch
@require_vision
class UpperCAmelCase						( __A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = ConditionalDetrImageProcessor if is_vision_available() else None
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				ConditionalDetrImageProcessingTester(self )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return self.image_processor_tester.prepare_image_processor_dict()
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(lowercase		,     'image_mean' ) )
						self.assertTrue(hasattr(lowercase		,     'image_std' ) )
						self.assertTrue(hasattr(lowercase		,     'do_normalize' ) )
						self.assertTrue(hasattr(lowercase		,     'do_resize' ) )
						self.assertTrue(hasattr(lowercase		,     'size' ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size		,     {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3} )
						self.assertEqual(image_processor.do_pad		,     lowercase )

						A_		:    int         =				self.image_processing_class.from_dict(
						    self.image_processor_dict		,     size=4_2		,     max_size=8_4		,     pad_and_return_pixel_mask=lowercase )
						self.assertEqual(image_processor.size		,     {'shortest_edge': 4_2, 'longest_edge': 8_4} )
						self.assertEqual(image_processor.do_pad		,     lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						pass
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						A_		:    Optional[int]         =				prepare_image_inputs(self.image_processor_tester		,     equal_resolution=lowercase )
						for image in image_inputs:
								self.assertIsInstance(lowercase		,     Image.Image )

						# Test not batched input
						A_		:    str         =				image_processing(image_inputs[0]		,     return_tensors='pt' ).pixel_values

						A_       ,      A_		:    List[str]         =				self.image_processor_tester.get_expected_values(lowercase )

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

						# Test batched
						A_       ,      A_		:    int         =				self.image_processor_tester.get_expected_values(lowercase		,     batched=lowercase )

						A_		:    Optional[Any]         =				image_processing(lowercase		,     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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						A_		:    List[str]         =				prepare_image_inputs(self.image_processor_tester		,     equal_resolution=lowercase		,     numpify=lowercase )
						for image in image_inputs:
								self.assertIsInstance(lowercase		,     np.ndarray )

						# Test not batched input
						A_		:    Union[str, Any]         =				image_processing(image_inputs[0]		,     return_tensors='pt' ).pixel_values

						A_       ,      A_		:    Any         =				self.image_processor_tester.get_expected_values(lowercase )

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

						# Test batched
						A_		:    Union[str, Any]         =				image_processing(lowercase		,     return_tensors='pt' ).pixel_values

						A_       ,      A_		:    Optional[int]         =				self.image_processor_tester.get_expected_values(lowercase		,     batched=lowercase )

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



						"""simple docstring"""





						A_		:    Tuple         =				self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						A_		:    Optional[int]         =				prepare_image_inputs(self.image_processor_tester		,     equal_resolution=lowercase		,     torchify=lowercase )
						for image in image_inputs:
								self.assertIsInstance(lowercase		,     torch.Tensor )

						# Test not batched input
						A_		:    Dict         =				image_processing(image_inputs[0]		,     return_tensors='pt' ).pixel_values

						A_       ,      A_		:    Optional[int]         =				self.image_processor_tester.get_expected_values(lowercase )

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

						# Test batched
						A_		:    List[Any]         =				image_processing(lowercase		,     return_tensors='pt' ).pixel_values

						A_       ,      A_		:    int         =				self.image_processor_tester.get_expected_values(lowercase		,     batched=lowercase )

						self.assertEqual(
						    encoded_images.shape		,     (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )		,     )
				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,     'r' ) as f:
								A_		:    Optional[Any]         =				json.loads(f.read() )

						A_		:    List[str]         =				{'image_id': 3_9_7_6_9, 'annotations': target}

						# encode them
						A_		:    Any         =				ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
						A_		:    int         =				image_processing(images=lowercase		,     annotations=lowercase		,     return_tensors='pt' )

						# verify pixel values
						A_		:    Optional[int]         =				torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding['pixel_values'].shape		,     lowercase )

						A_		:    int         =				torch.tensor([0.2796, 0.3138, 0.3481] )
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,     lowercase		,     atol=1E-4 ) )

						# verify area
						A_		:    Any         =				torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,     lowercase ) )
						# verify boxes
						A_		:    Any         =				torch.Size([6, 4] )
						self.assertEqual(encoding['labels'][0]['boxes'].shape		,     lowercase )
						A_		:    Tuple         =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,     lowercase		,     atol=1E-3 ) )
						# verify image_id
						A_		:    Any         =				torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,     lowercase ) )
						# verify is_crowd
						A_		:    str         =				torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,     lowercase ) )
						# verify class_labels
						A_		:    Any         =				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,     lowercase ) )
						# verify orig_size
						A_		:    Any         =				torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,     lowercase ) )
						# verify size
						A_		:    Dict         =				torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,     lowercase ) )







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,     'r' ) as f:
								A_		:    Optional[int]         =				json.loads(f.read() )

						A_		:    List[str]         =				{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

						A_		:    int         =				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

						# encode them
						A_		:    Tuple         =				ConditionalDetrImageProcessor(format='coco_panoptic' )
						A_		:    List[str]         =				image_processing(images=lowercase		,     annotations=lowercase		,     masks_path=lowercase		,     return_tensors='pt' )

						# verify pixel values
						A_		:    Any         =				torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding['pixel_values'].shape		,     lowercase )

						A_		:    List[Any]         =				torch.tensor([0.2796, 0.3138, 0.3481] )
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,     lowercase		,     atol=1E-4 ) )

						# verify area
						A_		:    Union[str, Any]         =				torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,     lowercase ) )
						# verify boxes
						A_		:    List[Any]         =				torch.Size([6, 4] )
						self.assertEqual(encoding['labels'][0]['boxes'].shape		,     lowercase )
						A_		:    int         =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,     lowercase		,     atol=1E-3 ) )
						# verify image_id
						A_		:    str         =				torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,     lowercase ) )
						# verify is_crowd
						A_		:    Tuple         =				torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,     lowercase ) )
						# verify class_labels
						A_		:    Tuple         =				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,     lowercase ) )
						# verify masks
						A_		:    List[Any]         =				8_2_2_8_7_3
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,     lowercase )
						# verify orig_size
						A_		:    Union[str, Any]         =				torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,     lowercase ) )
						# verify size
						A_		:    str         =				torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,     lowercase ) )







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






