
"""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 (						snake_case__      :   Optional[int]		,					snake_case__      :   Dict="shi-labs/oneformer_demo"   ):


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







class  lowerCAmelCase_ (					unittest.TestCase			):



						'''simple docstring'''


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

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


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

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

							return expected_height, expected_width




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Union[str, Any]:
							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_ (					_snake_case						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			str					=			OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
						# only for test_image_processing_common.test_image_proc_to_json_string
						_lowerCamelCase:			Union[str, Any]					=			image_processing_class


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[int]:
							A        =      OneFormerImageProcessorTester(self					)


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Dict:
							return self.image_processing_tester.prepare_image_processor_dict()


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      int:
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Tuple:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      List[str]:
							# Initialize image_processor
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							A        =      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        =      image_processor(image_inputs[0]     ,['semantic']     ,return_tensors='pt'					).pixel_values

							A        =      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        =      self.image_processing_tester.get_expected_values(_UpperCamelCase     ,batched=_UpperCamelCase					)

							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Optional[Any]:
							# Initialize image_processor
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							A        =      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        =      image_processor(image_inputs[0]     ,['semantic']     ,return_tensors='pt'					).pixel_values

							A        =      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        =      self.image_processing_tester.get_expected_values(_UpperCamelCase     ,batched=_UpperCamelCase					)

							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[str]:
							# Initialize image_processor
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							A        =      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        =      image_processor(image_inputs[0]     ,['semantic']     ,return_tensors='pt'					).pixel_values

							A        =      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        =      self.image_processing_tester.get_expected_values(_UpperCamelCase     ,batched=_UpperCamelCase					)

							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					Union[str, Any]=False     ,A_   :					List[Any]=False     ,A_   :					str="np"					)   ->      Dict:
							A        =      self.image_processing_class(**self.image_processor_dict					)
							# prepare image and target
							A        =      self.image_processing_tester.num_labels
							A        =      None
							A        =      None
							A        =      prepare_image_inputs(self.image_processing_tester     ,equal_resolution=_UpperCamelCase					)
							if with_segmentation_maps:
								A        =      num_labels
								if is_instance_map:
									A        =      list(range(_UpperCamelCase					)					) * 2
									A        =      dict(enumerate(_UpperCamelCase					)					)
								A        =      [
								    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        =      [Image.fromarray(_UpperCamelCase					) for annotation in annotations]

							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Any:
							pass


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      List[str]:
							def common(A_   :					str=False     ,A_   :					Optional[Any]=None					):
								A        =      self.comm_get_image_processor_inputs(
								    with_segmentation_maps=_UpperCamelCase     ,is_instance_map=_UpperCamelCase     ,segmentation_type=_UpperCamelCase					)

								A        =      inputs['mask_labels']
								A        =      inputs['class_labels']
								A        =      inputs['pixel_values']
								A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Optional[Any]:
							A        =      np.zeros((20, 50)					)
							A        =      1
							A        =      1
							A        =      1

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      int:
							A        =      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        =      self.image_processing_tester.get_fake_oneformer_outputs()

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

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Union[str, Any]:
							A        =      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        =      self.image_processing_tester.get_fake_oneformer_outputs()
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      List[str]:
							A        =      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        =      self.image_processing_tester.get_fake_oneformer_outputs()
							A        =      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)					)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






