'''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  snake_case_		(    __SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Tuple="shi-labs/oneformer_demo"							):



						"""simple docstring"""





						with open(hf_hub_download(lowerCAmelCase__				,				lowerCAmelCase__				,				repo_type='''dataset'''							)				,				'''r'''							) as f:
												lowercase_     :					Optional[Any]													=	json.load(lowerCAmelCase__							)
						lowercase_     :					Any													=	{}
						lowercase_     :					Dict													=	[]
						lowercase_     :					Tuple													=	[]
						for key, info in class_info.items():
												lowercase_     :					Optional[Any]													=	info['''name''']
												class_names.append(info['''name''']							)
												if info["isthing"]:
																		thing_ids.append(int(lowerCAmelCase__							)							)
						lowercase_     :					Optional[int]													=	thing_ids
						lowercase_     :					Optional[Any]													=	class_names
						return metadata






class 			lowerCAmelCase__       (  unittest.TestCase					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=7    ,							__SCREAMING_SNAKE_CASE=3    ,							__SCREAMING_SNAKE_CASE=30    ,							__SCREAMING_SNAKE_CASE=4_00    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,							__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,							__SCREAMING_SNAKE_CASE=10    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=2_55    ,							__SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"    ,							__SCREAMING_SNAKE_CASE="ade20k_panoptic.json"    ,							__SCREAMING_SNAKE_CASE=10    ,							):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	parent
											lowercase_     :					List[Any]													=	batch_size
											lowercase_     :					Any													=	num_channels
											lowercase_     :					Tuple													=	min_resolution
											lowercase_     :					int													=	max_resolution
											lowercase_     :					Optional[Any]													=	do_resize
											lowercase_     :					Tuple													=	{'''shortest_edge''': 32, '''longest_edge''': 13_33} if size is None else size
											lowercase_     :					Dict													=	do_normalize
											lowercase_     :					Optional[int]													=	image_mean
											lowercase_     :					Dict													=	image_std
											lowercase_     :					Tuple													=	class_info_file
											lowercase_     :					int													=	prepare_metadata(snake_case_    ,							snake_case_		)
											lowercase_     :					Optional[Any]													=	num_text
											lowercase_     :					Any													=	repo_path

											# for the post_process_functions
											lowercase_     :					Any													=	2
											lowercase_     :					Union[str, Any]													=	10
											lowercase_     :					Optional[Any]													=	10
											lowercase_     :					str													=	3
											lowercase_     :					str													=	4
											lowercase_     :					Optional[Any]													=	num_labels
											lowercase_     :					Union[str, Any]													=	do_reduce_labels
											lowercase_     :					Optional[Any]													=	ignore_index






					def      _snake_case   (       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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=False		):


											"""simple docstring"""





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

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

											return expected_height, expected_width






					def      _snake_case   (       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 			lowerCAmelCase__       (  __lowercase   ,						unittest.TestCase					):
					lowerCAmelCase_					       =					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
					# only for test_image_processing_common.test_image_proc_to_json_string
					lowerCAmelCase_					       =					image_processing_class






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	OneFormerImageProcessorTester(self		)






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return self.image_processing_tester.prepare_image_processor_dict()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	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'''		)		)
											self.assertTrue(hasattr(snake_case_    ,							'''ignore_index'''		)		)
											self.assertTrue(hasattr(snake_case_    ,							'''class_info_file'''		)		)
											self.assertTrue(hasattr(snake_case_    ,							'''num_text'''		)		)
											self.assertTrue(hasattr(snake_case_    ,							'''repo_path'''		)		)
											self.assertTrue(hasattr(snake_case_    ,							'''metadata'''		)		)
											self.assertTrue(hasattr(snake_case_    ,							'''do_reduce_labels'''		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											lowercase_     :					List[str]													=	prepare_image_inputs(self.image_processing_tester    ,							equal_resolution=snake_case_		)
											for image in image_inputs:
																	self.assertIsInstance(snake_case_    ,							Image.Image		)

											# Test not batched input
											lowercase_     :					Any													=	image_processor(image_inputs[0]    ,							['''semantic''']    ,							return_tensors='''pt'''		).pixel_values

											lowercase_     :					int													=	self.image_processing_tester.get_expected_values(snake_case_		)

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

											# Test batched
											lowercase_     :					Union[str, Any]													=	self.image_processing_tester.get_expected_values(snake_case_    ,							batched=snake_case_		)

											lowercase_     :					str													=	image_processor(
											    snake_case_    ,							['''semantic'''] * len(snake_case_		)    ,							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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.image_processing_class(**self.image_processor_dict		)
											# create random numpy tensors
											lowercase_     :					int													=	prepare_image_inputs(self.image_processing_tester    ,							equal_resolution=snake_case_    ,							numpify=snake_case_		)
											for image in image_inputs:
																	self.assertIsInstance(snake_case_    ,							np.ndarray		)

											# Test not batched input
											lowercase_     :					Tuple													=	image_processor(image_inputs[0]    ,							['''semantic''']    ,							return_tensors='''pt'''		).pixel_values

											lowercase_     :					str													=	self.image_processing_tester.get_expected_values(snake_case_		)

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

											# Test batched
											lowercase_     :					Optional[int]													=	self.image_processing_tester.get_expected_values(snake_case_    ,							batched=snake_case_		)

											lowercase_     :					List[Any]													=	image_processor(
											    snake_case_    ,							['''semantic'''] * len(snake_case_		)    ,							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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											lowercase_     :					Optional[Any]													=	prepare_image_inputs(self.image_processing_tester    ,							equal_resolution=snake_case_    ,							torchify=snake_case_		)
											for image in image_inputs:
																	self.assertIsInstance(snake_case_    ,							torch.Tensor		)

											# Test not batched input
											lowercase_     :					int													=	image_processor(image_inputs[0]    ,							['''semantic''']    ,							return_tensors='''pt'''		).pixel_values

											lowercase_     :					int													=	self.image_processing_tester.get_expected_values(snake_case_		)

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

											# Test batched
											lowercase_     :					str													=	self.image_processing_tester.get_expected_values(snake_case_    ,							batched=snake_case_		)

											lowercase_     :					int													=	image_processor(
											    snake_case_    ,							['''semantic'''] * len(snake_case_		)    ,							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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE="np"		):


											"""simple docstring"""





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

											lowercase_     :					Tuple													=	image_processor(
											    snake_case_    ,							['''semantic'''] * len(snake_case_		)    ,							snake_case_    ,							return_tensors='''pt'''    ,							instance_id_to_semantic_id=snake_case_    ,							pad_and_return_pixel_mask=snake_case_    ,							)

											return inputs






					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					def      _snake_case   (       self		):


											"""simple docstring"""





											def common(__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=None		):
																	lowercase_     :					int													=	self.comm_get_image_processor_inputs(
																	    with_segmentation_maps=snake_case_    ,							is_instance_map=snake_case_    ,							segmentation_type=snake_case_		)

																	lowercase_     :					int													=	inputs['''mask_labels''']
																	lowercase_     :					Optional[Any]													=	inputs['''class_labels''']
																	lowercase_     :					List[Any]													=	inputs['''pixel_values''']
																	lowercase_     :					Optional[Any]													=	inputs['''text_inputs''']

																	# check the batch_size
																	for mask_label, class_label, text_input in zip(snake_case_    ,							snake_case_    ,							snake_case_		):
																							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(snake_case_		)    ,							self.image_processing_tester.num_text		)

											common()
											common(is_instance_map=snake_case_		)
											common(is_instance_map=snake_case_    ,							segmentation_type='''pil'''		)
											common(is_instance_map=snake_case_    ,							segmentation_type='''pil'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	np.zeros((20, 50)		)
											lowercase_     :					Tuple													=	1
											lowercase_     :					int													=	1
											lowercase_     :					List[Any]													=	1

											lowercase_     :					Dict													=	binary_mask_to_rle(snake_case_		)
											self.assertEqual(len(snake_case_		)    ,							4		)
											self.assertEqual(rle[0]    ,							21		)
											self.assertEqual(rle[1]    ,							45		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	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'''    ,							)
											lowercase_     :					str													=	self.image_processing_tester.get_fake_oneformer_outputs()

											lowercase_     :					Optional[int]													=	fature_extractor.post_process_semantic_segmentation(snake_case_		)

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

											lowercase_     :					List[str]													=	[(1, 4) for i in range(self.image_processing_tester.batch_size		)]
											lowercase_     :					str													=	fature_extractor.post_process_semantic_segmentation(snake_case_    ,							target_sizes=snake_case_		)

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	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'''    ,							)
											lowercase_     :					Optional[Any]													=	self.image_processing_tester.get_fake_oneformer_outputs()
											lowercase_     :					List[Any]													=	image_processor.post_process_instance_segmentation(snake_case_    ,							threshold=0		)

											self.assertTrue(len(snake_case_		) == 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''']		)    ,							snake_case_		)
																	self.assertEqual(
																	    el['''segmentation'''].shape    ,							(self.image_processing_tester.height, self.image_processing_tester.width)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	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'''    ,							)
											lowercase_     :					List[str]													=	self.image_processing_tester.get_fake_oneformer_outputs()
											lowercase_     :					List[Any]													=	image_processor.post_process_panoptic_segmentation(snake_case_    ,							threshold=0		)

											self.assertTrue(len(snake_case_		) == 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''']		)    ,							snake_case_		)
																	self.assertEqual(
																	    el['''segmentation'''].shape    ,							(self.image_processing_tester.height, self.image_processing_tester.width)		)





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  List[Any]   							=	{
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  int   							=	[
							    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "XCLIPModel",
							    "XCLIPPreTrainedModel",
							    "XCLIPTextModel",
							    "XCLIPVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_x_clip import (
							    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XCLIPConfig,
							    XCLIPTextConfig,
							    XCLIPVisionConfig,
							)
							from .processing_x_clip import XCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_x_clip import (
														    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XCLIPModel,
														    XCLIPPreTrainedModel,
														    XCLIPTextModel,
														    XCLIPVisionModel,
														)

else:
							import sys

							_lowercase			:  List[str]   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



