
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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						Tuple="shi-labs/oneformer_demo"       ) ->       Dict:

				"""simple docstring"""







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







class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=7				,       UpperCamelCase__=3				,       UpperCamelCase__=30				,       UpperCamelCase__=400				,       UpperCamelCase__=None				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=[0.5, 0.5, 0.5]				,       UpperCamelCase__=[0.5, 0.5, 0.5]				,       UpperCamelCase__=10				,       UpperCamelCase__=False				,       UpperCamelCase__=255				,       UpperCamelCase__="shi-labs/oneformer_demo"				,       UpperCamelCase__="ade20k_panoptic.json"				,       UpperCamelCase__=10				,       ):
					A__  :	Any         =							parent
					A__  :	Union[str, Any]         =							batch_size
					A__  :	Any         =							num_channels
					A__  :	Optional[int]         =							min_resolution
					A__  :	Optional[int]         =							max_resolution
					A__  :	Optional[int]         =							do_resize
					A__  :	List[Any]         =							{'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
					A__  :	List[str]         =							do_normalize
					A__  :	Any         =							image_mean
					A__  :	Union[str, Any]         =							image_std
					A__  :	int         =							class_info_file
					A__  :	Optional[Any]         =							prepare_metadata(UpperCamelCase__				,       UpperCamelCase__		)
					A__  :	Optional[int]         =							num_text
					A__  :	Optional[int]         =							repo_path

					# for the post_process_functions
					A__  :	Dict         =							2
					A__  :	Any         =							10
					A__  :	Tuple         =							10
					A__  :	List[Any]         =							3
					A__  :	Dict         =							4
					A__  :	int         =							num_labels
					A__  :	Union[str, Any]         =							do_reduce_labels
					A__  :	List[str]         =							ignore_index





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

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

					return expected_height, expected_width





	def   __snake_case				(			self		):
					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'''
	_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		):
					A__  :	int         =							OneFormerImageProcessorTester(self		)





	@property
	def   __snake_case				(			self		):
					return self.image_processing_tester.prepare_image_processor_dict()





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.image_processing_class(**self.image_processor_dict		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_mean'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_std'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_normalize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_resize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''size'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''ignore_index'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''class_info_file'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''num_text'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''repo_path'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''metadata'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_reduce_labels'''		)		)





	def   __snake_case				(			self		):
					pass





	def   __snake_case				(			self		):
					# Initialize image_processor
					A__  :	Tuple         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PIL images
					A__  :	Dict         =							prepare_image_inputs(self.image_processing_tester				,       equal_resolution=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       Image.Image		)

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

					A__       ,							A__  :	Any         =							self.image_processing_tester.get_expected_values(UpperCamelCase__		)

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

					# Test batched
					A__       ,							A__  :	str         =							self.image_processing_tester.get_expected_values(UpperCamelCase__				,       batched=UpperCamelCase__		)

					A__  :	Optional[Any]         =							image_processor(
					    UpperCamelCase__				,       ['''semantic'''] * len(UpperCamelCase__		)				,       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		):
					# Initialize image_processor
					A__  :	Optional[Any]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random numpy tensors
					A__  :	Dict         =							prepare_image_inputs(self.image_processing_tester				,       equal_resolution=UpperCamelCase__				,       numpify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       np.ndarray		)

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

					A__       ,							A__  :	Optional[Any]         =							self.image_processing_tester.get_expected_values(UpperCamelCase__		)

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

					# Test batched
					A__       ,							A__  :	List[str]         =							self.image_processing_tester.get_expected_values(UpperCamelCase__				,       batched=UpperCamelCase__		)

					A__  :	Optional[Any]         =							image_processor(
					    UpperCamelCase__				,       ['''semantic'''] * len(UpperCamelCase__		)				,       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		):
					# Initialize image_processor
					A__  :	str         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PyTorch tensors
					A__  :	Union[str, Any]         =							prepare_image_inputs(self.image_processing_tester				,       equal_resolution=UpperCamelCase__				,       torchify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       torch.Tensor		)

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

					A__       ,							A__  :	Union[str, Any]         =							self.image_processing_tester.get_expected_values(UpperCamelCase__		)

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

					# Test batched
					A__       ,							A__  :	int         =							self.image_processing_tester.get_expected_values(UpperCamelCase__				,       batched=UpperCamelCase__		)

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

					A__  :	Optional[int]         =							image_processor(
					    UpperCamelCase__				,       ['''semantic'''] * len(UpperCamelCase__		)				,       UpperCamelCase__				,       return_tensors='''pt'''				,       instance_id_to_semantic_id=UpperCamelCase__				,       pad_and_return_pixel_mask=UpperCamelCase__				,       )

					return inputs





	def   __snake_case				(			self		):
					pass





	def   __snake_case				(			self		):
					def common(UpperCamelCase__=False				,       UpperCamelCase__=None		):
									A__  :	List[Any]         =							self.comm_get_image_processor_inputs(
									    with_segmentation_maps=UpperCamelCase__				,       is_instance_map=UpperCamelCase__				,       segmentation_type=UpperCamelCase__		)

									A__  :	Optional[int]         =							inputs['''mask_labels''']
									A__  :	Union[str, Any]         =							inputs['''class_labels''']
									A__  :	Dict         =							inputs['''pixel_values''']
									A__  :	Union[str, Any]         =							inputs['''text_inputs''']

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

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





	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							np.zeros((20, 50)		)
					A__  :	Any         =							1
					A__  :	str         =							1
					A__  :	Dict         =							1

					A__  :	Tuple         =							binary_mask_to_rle(UpperCamelCase__		)
					self.assertEqual(len(UpperCamelCase__		)				,       4		)
					self.assertEqual(rle[0]				,       21		)
					self.assertEqual(rle[1]				,       45		)





	def   __snake_case				(			self		):
					A__  :	Union[str, 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'''				,       )
					A__  :	List[Any]         =							self.image_processing_tester.get_fake_oneformer_outputs()

					A__  :	Union[str, Any]         =							fature_extractor.post_process_semantic_segmentation(UpperCamelCase__		)

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

					A__  :	Dict         =							[(1, 4) for i in range(self.image_processing_tester.batch_size		)]
					A__  :	Optional[int]         =							fature_extractor.post_process_semantic_segmentation(UpperCamelCase__				,       target_sizes=UpperCamelCase__		)

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





	def   __snake_case				(			self		):
					A__  :	Union[str, 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'''				,       )
					A__  :	int         =							self.image_processing_tester.get_fake_oneformer_outputs()
					A__  :	str         =							image_processor.post_process_instance_segmentation(UpperCamelCase__				,       threshold=0		)

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





	def   __snake_case				(			self		):
					A__  :	Tuple         =							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'''				,       )
					A__  :	Tuple         =							self.image_processing_tester.get_fake_oneformer_outputs()
					A__  :	int         =							image_processor.post_process_panoptic_segmentation(UpperCamelCase__				,       threshold=0		)

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

import numpy as np

_SCREAMING_SNAKE_CASE    :      Any          =			[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self		):
					A__  :	List[Any]         =							np.array(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__  :	Any         =							np.where(letter == self.SQUARE		)
					A__  :	int         =							np.concatenate([indexa + 1, indexa + 1]		)
					return indexes





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							self.SQUARE[indexa - 1, indexa - 1]
					return letter





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							message.lower()
					A__  :	str         =							message.replace(''' '''				,       ''''''		)
					A__  :	Union[str, Any]         =							message.replace('''j'''				,       '''i'''		)

					A__  :	List[Any]         =							np.empty((2, len(UpperCamelCase__		))		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	Any         =							self.letter_to_numbers(message[letter_index]		)

									A__  :	Optional[Any]         =							numbers[0]
									A__  :	List[str]         =							numbers[1]

					A__  :	List[str]         =							first_step.reshape(2 * len(UpperCamelCase__		)		)
					A__  :	List[Any]         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Dict         =							int(second_step[numbers_index * 2]		)
									A__  :	List[str]         =							int(second_step[(numbers_index * 2) + 1]		)
									A__  :	Dict         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							encoded_message + letter

					return encoded_message





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							message.lower()
					message.replace(''' '''				,       ''''''		)
					A__  :	List[Any]         =							np.empty(2 * len(UpperCamelCase__		)		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	List[str]         =							self.letter_to_numbers(message[letter_index]		)
									A__  :	Dict         =							numbers[0]
									A__  :	int         =							numbers[1]

					A__  :	Optional[Any]         =							first_step.reshape((2, len(UpperCamelCase__		))		)
					A__  :	int         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Tuple         =							int(second_step[0, numbers_index]		)
									A__  :	Dict         =							int(second_step[1, numbers_index]		)
									A__  :	List[str]         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							decoded_message + letter

					return decoded_message