
'''simple docstring'''



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
												from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
												from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
						from PIL import Image


def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:List[Any]="shi-labs/oneformer_demo"		):
						with open(hf_hub_download(__lowerCamelCase			,     __lowerCamelCase			,     repo_type="""dataset"""		)			,     """r"""		) as f:
												_lowerCAmelCase  						=			json.load(__lowerCamelCase		)
						_lowerCAmelCase  						=			{}
						_lowerCAmelCase  						=			[]
						_lowerCAmelCase  						=			[]
						for key, info in class_info.items():
												_lowerCAmelCase  						=			info["""name"""]
												class_names.append(info["""name"""]		)
												if info["isthing"]:
																		thing_ids.append(int(__lowerCamelCase		)		)
						_lowerCAmelCase  						=			thing_ids
						_lowerCAmelCase  						=			class_names
						return metadata




class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=7   ,       _lowercase=3   ,       _lowercase=30   ,       _lowercase=400   ,       _lowercase=None   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=[0.5, 0.5, 0.5]   ,       _lowercase=[0.5, 0.5, 0.5]   ,       _lowercase=10   ,       _lowercase=False   ,       _lowercase=255   ,       _lowercase="shi-labs/oneformer_demo"   ,       _lowercase="ade20k_panoptic.json"   ,       _lowercase=10   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			min_resolution
												_lowerCAmelCase  						=			max_resolution
												_lowerCAmelCase  						=			do_resize
												_lowerCAmelCase  						=			{"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
												_lowerCAmelCase  						=			do_normalize
												_lowerCAmelCase  						=			image_mean
												_lowerCAmelCase  						=			image_std
												_lowerCAmelCase  						=			class_info_file
												_lowerCAmelCase  						=			prepare_metadata(_lowercase   ,       _lowercase				)
												_lowerCAmelCase  						=			num_text
												_lowerCAmelCase  						=			repo_path

												# for the post_process_functions
												_lowerCAmelCase  						=			2
												_lowerCAmelCase  						=			10
												_lowerCAmelCase  						=			10
												_lowerCAmelCase  						=			3
												_lowerCAmelCase  						=			4
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			do_reduce_labels
												_lowerCAmelCase  						=			ignore_index




						def  _lowercase     ( 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,
												    "num_labels": self.num_labels,
												    "do_reduce_labels": self.do_reduce_labels,
												    "ignore_index": self.ignore_index,
												    "class_info_file": self.class_info_file,
												    "metadata": self.metadata,
												    "num_text": self.num_text,
												}




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=False				):

												"""simple docstring"""



												if not batched:
																		_lowerCAmelCase  						=			image_inputs[0]
																		if isinstance(_lowercase   ,       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(_lowercase   ,       key=lambda _lowercase				: item[0]				)[0]
																		_lowerCAmelCase  						=			max(_lowercase   ,       key=lambda _lowercase				: item[1]				)[1]

												return expected_height, expected_width




						def  _lowercase     ( self				):

												"""simple docstring"""



												return OneFormerForUniversalSegmentationOutput(
												    # +1 for null class
												    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)				)   ,       masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)				)   ,       )






@require_torch
@require_vision
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Tuple									=       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
						# only for test_image_processing_common.test_image_proc_to_json_string
						_lowercase		:				Optional[Any]									=       image_processing_class




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			OneFormerImageProcessorTester(self				)




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.image_processing_tester.prepare_image_processor_dict()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			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"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """ignore_index"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """class_info_file"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """num_text"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """repo_path"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """metadata"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """do_reduce_labels"""				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												_lowerCAmelCase  						=			prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=_lowercase				)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,       Image.Image				)

												# Test not batched input
												_lowerCAmelCase  						=			image_processor(image_inputs[0]   ,       ["""semantic"""]   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.image_processing_tester.get_expected_values(_lowercase				)

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

												# Test batched
												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.image_processing_tester.get_expected_values(_lowercase   ,       batched=_lowercase				)

												_lowerCAmelCase  						=			image_processor(
												    _lowercase   ,       ["""semantic"""] * len(_lowercase				)   ,       return_tensors="""pt"""				).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,       (
												        self.image_processing_tester.batch_size,
												        self.image_processing_tester.num_channels,
												        expected_height,
												        expected_width,
												    )   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												_lowerCAmelCase  						=			prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=_lowercase   ,       numpify=_lowercase				)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,       np.ndarray				)

												# Test not batched input
												_lowerCAmelCase  						=			image_processor(image_inputs[0]   ,       ["""semantic"""]   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.image_processing_tester.get_expected_values(_lowercase				)

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

												# Test batched
												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.image_processing_tester.get_expected_values(_lowercase   ,       batched=_lowercase				)

												_lowerCAmelCase  						=			image_processor(
												    _lowercase   ,       ["""semantic"""] * len(_lowercase				)   ,       return_tensors="""pt"""				).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,       (
												        self.image_processing_tester.batch_size,
												        self.image_processing_tester.num_channels,
												        expected_height,
												        expected_width,
												    )   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												_lowerCAmelCase  						=			prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=_lowercase   ,       torchify=_lowercase				)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,       torch.Tensor				)

												# Test not batched input
												_lowerCAmelCase  						=			image_processor(image_inputs[0]   ,       ["""semantic"""]   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.image_processing_tester.get_expected_values(_lowercase				)

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

												# Test batched
												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.image_processing_tester.get_expected_values(_lowercase   ,       batched=_lowercase				)

												_lowerCAmelCase  						=			image_processor(
												    _lowercase   ,       ["""semantic"""] * len(_lowercase				)   ,       return_tensors="""pt"""				).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,       (
												        self.image_processing_tester.batch_size,
												        self.image_processing_tester.num_channels,
												        expected_height,
												        expected_width,
												    )   ,       )




						def  _lowercase     ( self   ,       _lowercase=False   ,       _lowercase=False   ,       _lowercase="np"				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# prepare image and target
												_lowerCAmelCase  						=			self.image_processing_tester.num_labels
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			prepare_image_inputs(self.image_processing_tester   ,       equal_resolution=_lowercase				)
												if with_segmentation_maps:
																		_lowerCAmelCase  						=			num_labels
																		if is_instance_map:
																								_lowerCAmelCase  						=			list(range(_lowercase				)				) * 2
																								_lowerCAmelCase  						=			dict(enumerate(_lowercase				)				)
																		_lowerCAmelCase  						=			[
																		    np.random.randint(0   ,       high * 2   ,       (img.size[1], img.size[0])				).astype(np.uinta				) for img in image_inputs
																		]
																		if segmentation_type == "pil":
																								_lowerCAmelCase  						=			[Image.fromarray(_lowercase				) for annotation in annotations]

												_lowerCAmelCase  						=			image_processor(
												    _lowercase   ,       ["""semantic"""] * len(_lowercase				)   ,       _lowercase   ,       return_tensors="""pt"""   ,       instance_id_to_semantic_id=_lowercase   ,       pad_and_return_pixel_mask=_lowercase   ,       )

												return inputs




						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												def common(_lowercase=False   ,       _lowercase=None				):
																		_lowerCAmelCase  						=			self.comm_get_image_processor_inputs(
																		    with_segmentation_maps=_lowercase   ,       is_instance_map=_lowercase   ,       segmentation_type=_lowercase				)

																		_lowerCAmelCase  						=			inputs["""mask_labels"""]
																		_lowerCAmelCase  						=			inputs["""class_labels"""]
																		_lowerCAmelCase  						=			inputs["""pixel_values"""]
																		_lowerCAmelCase  						=			inputs["""text_inputs"""]

																		# check the batch_size
																		for mask_label, class_label, text_input in zip(_lowercase   ,       _lowercase   ,       _lowercase				):
																								self.assertEqual(mask_label.shape[0]   ,       class_label.shape[0]				)
																								# this ensure padding has happened
																								self.assertEqual(mask_label.shape[1:]   ,       pixel_values.shape[2:]				)
																								self.assertEqual(len(_lowercase				)   ,       self.image_processing_tester.num_text				)

												common()
												common(is_instance_map=_lowercase				)
												common(is_instance_map=_lowercase   ,       segmentation_type="""pil"""				)
												common(is_instance_map=_lowercase   ,       segmentation_type="""pil"""				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			np.zeros((20, 50)				)
												_lowerCAmelCase  						=			1
												_lowerCAmelCase  						=			1
												_lowerCAmelCase  						=			1

												_lowerCAmelCase  						=			binary_mask_to_rle(_lowercase				)
												self.assertEqual(len(_lowercase				)   ,       4				)
												self.assertEqual(rle[0]   ,       21				)
												self.assertEqual(rle[1]   ,       45				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(
												    num_labels=self.image_processing_tester.num_classes   ,       max_seq_length=77   ,       task_seq_length=77   ,       class_info_file="""ade20k_panoptic.json"""   ,       num_text=self.image_processing_tester.num_text   ,       repo_path="""shi-labs/oneformer_demo"""   ,       )
												_lowerCAmelCase  						=			self.image_processing_tester.get_fake_oneformer_outputs()

												_lowerCAmelCase  						=			fature_extractor.post_process_semantic_segmentation(_lowercase				)

												self.assertEqual(len(_lowercase				)   ,       self.image_processing_tester.batch_size				)
												self.assertEqual(
												    segmentation[0].shape   ,       (
												        self.image_processing_tester.height,
												        self.image_processing_tester.width,
												    )   ,       )

												_lowerCAmelCase  						=			[(1, 4) for i in range(self.image_processing_tester.batch_size				)]
												_lowerCAmelCase  						=			fature_extractor.post_process_semantic_segmentation(_lowercase   ,       target_sizes=_lowercase				)

												self.assertEqual(segmentation[0].shape   ,       target_sizes[0]				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(
												    num_labels=self.image_processing_tester.num_classes   ,       max_seq_length=77   ,       task_seq_length=77   ,       class_info_file="""ade20k_panoptic.json"""   ,       num_text=self.image_processing_tester.num_text   ,       repo_path="""shi-labs/oneformer_demo"""   ,       )
												_lowerCAmelCase  						=			self.image_processing_tester.get_fake_oneformer_outputs()
												_lowerCAmelCase  						=			image_processor.post_process_instance_segmentation(_lowercase   ,       threshold=0				)

												self.assertTrue(len(_lowercase				) == self.image_processing_tester.batch_size				)
												for el in segmentation:
																		self.assertTrue("""segmentation""" in el				)
																		self.assertTrue("""segments_info""" in el				)
																		self.assertEqual(type(el["""segments_info"""]				)   ,       _lowercase				)
																		self.assertEqual(
																		    el["""segmentation"""].shape   ,       (self.image_processing_tester.height, self.image_processing_tester.width)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(
												    num_labels=self.image_processing_tester.num_classes   ,       max_seq_length=77   ,       task_seq_length=77   ,       class_info_file="""ade20k_panoptic.json"""   ,       num_text=self.image_processing_tester.num_text   ,       repo_path="""shi-labs/oneformer_demo"""   ,       )
												_lowerCAmelCase  						=			self.image_processing_tester.get_fake_oneformer_outputs()
												_lowerCAmelCase  						=			image_processor.post_process_panoptic_segmentation(_lowercase   ,       threshold=0				)

												self.assertTrue(len(_lowercase				) == self.image_processing_tester.batch_size				)
												for el in segmentation:
																		self.assertTrue("""segmentation""" in el				)
																		self.assertTrue("""segments_info""" in el				)
																		self.assertEqual(type(el["""segments_info"""]				)   ,       _lowercase				)
																		self.assertEqual(
																		    el["""segmentation"""].shape   ,       (self.image_processing_tester.height, self.image_processing_tester.width)				)


'''simple docstring'''



# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    =		"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
						raise ImportWarning(
						    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
						    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    =		concatenate_datasets
_lowercase    =		DownloadConfig
_lowercase    =		DownloadManager
_lowercase    =		DownloadMode
_lowercase    =		DownloadConfig
_lowercase    =		DownloadMode
_lowercase    =		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
