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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase="shi-labs/oneformer_demo"       )->       Tuple:



			"""simple docstring"""







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






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[Any]=7							,							__lowerCamelCase				:      Tuple=3							,							__lowerCamelCase				:      Optional[int]=30							,							__lowerCamelCase				:      Tuple=4_00							,							__lowerCamelCase				:      Union[str, Any]=None							,							__lowerCamelCase				:      int=True							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      List[Any]=[0.5, 0.5, 0.5]							,							__lowerCamelCase				:      Any=[0.5, 0.5, 0.5]							,							__lowerCamelCase				:      Optional[Any]=10							,							__lowerCamelCase				:      str=False							,							__lowerCamelCase				:      Optional[int]=2_55							,							__lowerCamelCase				:      str="shi-labs/oneformer_demo"							,							__lowerCamelCase				:      str="ade20k_panoptic.json"							,							__lowerCamelCase				:      Dict=10							,							):


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		{'''shortest_edge''': 32, '''longest_edge''': 13_33} if size is None else size
									lowercase							=		do_normalize
									lowercase							=		image_mean
									lowercase							=		image_std
									lowercase							=		class_info_file
									lowercase							=		prepare_metadata(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		num_text
									lowercase							=		repo_path

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

						def        __a		(	self				:      int   ):


									'''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        __a		(	self				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Any=False   ):


									'''simple docstring'''

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

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

									return expected_height, expected_width



						def        __a		(	self				:      str   ):


									'''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      __lowercase					(    lowercase_       ,     unittest.TestCase    ):
						lowercase					 =     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
						# only for test_image_processing_common.test_image_proc_to_json_string
						lowercase					 =     image_processing_class

						def        __a		(	self				:      List[str]   ):


									'''simple docstring'''

									lowercase							=		OneFormerImageProcessorTester(self   )

						@property
						def        __a		(	self				:      List[str]   ):


									'''simple docstring'''

									return self.image_processing_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Optional[int]   ):


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''image_mean'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''image_std'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_normalize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_resize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''size'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''ignore_index'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''class_info_file'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''num_text'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''repo_path'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''metadata'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_reduce_labels'''   )   )

						def        __a		(	self				:      int   ):


									'''simple docstring'''

									pass

						def        __a		(	self				:      str   ):


									'''simple docstring'''

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

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

									lowercase		,lowercase							=		self.image_processing_tester.get_expected_values(__lowerCamelCase   )

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

									# Test batched
									lowercase		,lowercase							=		self.image_processing_tester.get_expected_values(__lowerCamelCase							,							batched=__lowerCamelCase   )

									lowercase							=		image_processor(
									    __lowerCamelCase							,							['''semantic'''] * len(__lowerCamelCase   )							,							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        __a		(	self				:      Any   ):


									'''simple docstring'''

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

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

									lowercase		,lowercase							=		self.image_processing_tester.get_expected_values(__lowerCamelCase   )

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

									# Test batched
									lowercase		,lowercase							=		self.image_processing_tester.get_expected_values(__lowerCamelCase							,							batched=__lowerCamelCase   )

									lowercase							=		image_processor(
									    __lowerCamelCase							,							['''semantic'''] * len(__lowerCamelCase   )							,							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        __a		(	self				:      Union[str, Any]   ):


									'''simple docstring'''

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

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

									lowercase		,lowercase							=		self.image_processing_tester.get_expected_values(__lowerCamelCase   )

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

									# Test batched
									lowercase		,lowercase							=		self.image_processing_tester.get_expected_values(__lowerCamelCase							,							batched=__lowerCamelCase   )

									lowercase							=		image_processor(
									    __lowerCamelCase							,							['''semantic'''] * len(__lowerCamelCase   )							,							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        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]=False							,							__lowerCamelCase				:      List[str]=False							,							__lowerCamelCase				:      Union[str, Any]="np"   ):


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# prepare image and target
									lowercase							=		self.image_processing_tester.num_labels
									lowercase							=		None
									lowercase							=		None
									lowercase							=		prepare_image_inputs(self.image_processing_tester							,							equal_resolution=__lowerCamelCase   )
									if with_segmentation_maps:
												lowercase							=		num_labels
												if is_instance_map:
															lowercase							=		list(range(__lowerCamelCase   )   ) * 2
															lowercase							=		dict(enumerate(__lowerCamelCase   )   )
												lowercase							=		[
												    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							=		[Image.fromarray(__lowerCamelCase   ) for annotation in annotations]

									lowercase							=		image_processor(
									    __lowerCamelCase							,							['''semantic'''] * len(__lowerCamelCase   )							,							__lowerCamelCase							,							return_tensors='''pt'''							,							instance_id_to_semantic_id=__lowerCamelCase							,							pad_and_return_pixel_mask=__lowerCamelCase							,							)

									return inputs

						def        __a		(	self				:      str   ):


									'''simple docstring'''

									pass

						def        __a		(	self				:      List[Any]   ):


									'''simple docstring'''

									def common(__lowerCamelCase				:      List[Any]=False							,							__lowerCamelCase				:      Union[str, Any]=None   ):
												lowercase							=		self.comm_get_image_processor_inputs(
												    with_segmentation_maps=__lowerCamelCase							,							is_instance_map=__lowerCamelCase							,							segmentation_type=__lowerCamelCase   )

												lowercase							=		inputs['''mask_labels''']
												lowercase							=		inputs['''class_labels''']
												lowercase							=		inputs['''pixel_values''']
												lowercase							=		inputs['''text_inputs''']

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

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

						def        __a		(	self				:      Tuple   ):


									'''simple docstring'''

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

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

						def        __a		(	self				:      Union[str, Any]   ):


									'''simple docstring'''

									lowercase							=		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							=		self.image_processing_tester.get_fake_oneformer_outputs()

									lowercase							=		fature_extractor.post_process_semantic_segmentation(__lowerCamelCase   )

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

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

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

						def        __a		(	self				:      Union[str, Any]   ):


									'''simple docstring'''

									lowercase							=		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							=		self.image_processing_tester.get_fake_oneformer_outputs()
									lowercase							=		image_processor.post_process_instance_segmentation(__lowerCamelCase							,							threshold=0   )

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



						def        __a		(	self				:      int   ):


									'''simple docstring'''

									lowercase							=		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							=		self.image_processing_tester.get_fake_oneformer_outputs()
									lowercase							=		image_processor.post_process_panoptic_segmentation(__lowerCamelCase							,							threshold=0   )

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






from __future__ import annotations

from collections import deque






class      __lowercase					:

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      list[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									self.adlist.append(
									    {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}   )

									for keyword in keywords:
												self.add_keyword(__lowerCamelCase   )
									self.set_fail_transitions()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      str   )							->		int | None:


									'''simple docstring'''

									for state in self.adlist[current_state]["next_states"]:
												if char == self.adlist[state]["value"]:
															return state
									return None

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      str   )							->		None:


									'''simple docstring'''

									lowercase							=		0
									for character in keyword:
												lowercase							=		self.find_next_state(__lowerCamelCase							,							__lowerCamelCase   )
												if next_state is None:
															self.adlist.append(
															    {
															        '''value''': character,
															        '''next_states''': [],
															        '''fail_state''': 0,
															        '''output''': [],
															    }   )
															self.adlist[current_state]["next_states"].append(len(self.adlist   ) - 1   )
															lowercase							=		len(self.adlist   ) - 1
												else:
															lowercase							=		next_state
									self.adlist[current_state]["output"].append(__lowerCamelCase   )

						def        __a		(	self				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		deque()
									for node in self.adlist[0]["next_states"]:
												q.append(__lowerCamelCase   )
												lowercase							=		0
									while q:
												lowercase							=		q.popleft()
												for child in self.adlist[r]["next_states"]:
															q.append(__lowerCamelCase   )
															lowercase							=		self.adlist[r]['''fail_state''']
															while (
															    self.find_next_state(__lowerCamelCase							,							self.adlist[child]['''value''']   ) is None
															    and state != 0
															):
																		lowercase							=		self.adlist[state]['''fail_state''']
															lowercase							=		self.find_next_state(
															    __lowerCamelCase							,							self.adlist[child]['''value''']   )
															if self.adlist[child]["fail_state"] is None:
																		lowercase							=		0
															lowercase							=		(
															    self.adlist[child]['''output''']
															    + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
															)



						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str   )							->		dict[str, list[int]]:


									'''simple docstring'''

									lowercase							=		{}  # returns a dict with keywords and list of its occurrences
									lowercase							=		0
									for i in range(len(__lowerCamelCase   )   ):
												while (
												    self.find_next_state(__lowerCamelCase							,							string[i]   ) is None
												    and current_state != 0
												):
															lowercase							=		self.adlist[current_state]['''fail_state''']
												lowercase							=		self.find_next_state(__lowerCamelCase							,							string[i]   )
												if next_state is None:
															lowercase							=		0
												else:
															lowercase							=		next_state
															for key in self.adlist[current_state]["output"]:
																		if key not in result:
																					lowercase							=		[]
																		result[key].append(i - len(__lowerCamelCase   ) + 1   )
									return result


if __name__ == "__main__":
						import doctest

						doctest.testmod()





