


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





class 							__UpperCamelCase				(		unittest.TestCase					):




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

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  parent
						__a			:       Tuple					    =  batch_size
						__a			:       List[Any]					    =  num_channels
						__a			:       Tuple					    =  min_resolution
						__a			:       Union[str, Any]					    =  max_resolution
						__a			:       List[str]					    =  do_resize
						__a			:       Union[str, Any]					    =  {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
						__a			:       Optional[Any]					    =  do_normalize
						__a			:       Dict					    =  image_mean
						__a			:       Union[str, Any]					    =  image_std
						__a			:       Union[str, Any]					    =  class_info_file
						__a			:       int					    =  prepare_metadata(__a						,				__a							)
						__a			:       List[Any]					    =  num_text
						__a			:       List[Any]					    =  repo_path

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




	def       __UpperCAmelCase							(   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       __UpperCAmelCase							(   self						,				__a						,				__a=False							):

						'''simple docstring'''





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

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

						return expected_height, expected_width





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  OneFormerImageProcessorTester(self							)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.image_processing_tester.prepare_image_processor_dict()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__a						,				'image_mean'							)							)
						self.assertTrue(hasattr(__a						,				'image_std'							)							)
						self.assertTrue(hasattr(__a						,				'do_normalize'							)							)
						self.assertTrue(hasattr(__a						,				'do_resize'							)							)
						self.assertTrue(hasattr(__a						,				'size'							)							)
						self.assertTrue(hasattr(__a						,				'ignore_index'							)							)
						self.assertTrue(hasattr(__a						,				'class_info_file'							)							)
						self.assertTrue(hasattr(__a						,				'num_text'							)							)
						self.assertTrue(hasattr(__a						,				'repo_path'							)							)
						self.assertTrue(hasattr(__a						,				'metadata'							)							)
						self.assertTrue(hasattr(__a						,				'do_reduce_labels'							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

						__a    ,				__a			:       List[str]					    =  self.image_processing_tester.get_expected_values(__a							)

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

						# Test batched
						__a    ,				__a			:       Optional[Any]					    =  self.image_processing_tester.get_expected_values(__a						,				batched=__a							)

						__a			:       List[str]					    =  image_processor(
						    __a						,				['semantic'] * len(__a							)						,				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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

						__a    ,				__a			:       Tuple					    =  self.image_processing_tester.get_expected_values(__a							)

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

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

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

						'''simple docstring'''





						__a			:       int					    =  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=__a						,				torchify=__a							)
						for image in image_inputs:
											self.assertIsInstance(__a						,				torch.Tensor							)

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

						__a    ,				__a			:       int					    =  self.image_processing_tester.get_expected_values(__a							)

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

						# Test batched
						__a    ,				__a			:       List[Any]					    =  self.image_processing_tester.get_expected_values(__a						,				batched=__a							)

						__a			:       str					    =  image_processor(
						    __a						,				['semantic'] * len(__a							)						,				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       __UpperCAmelCase							(   self						,				__a=False						,				__a=False						,				__a="np"							):

						'''simple docstring'''





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

						__a			:       Optional[Any]					    =  image_processor(
						    __a						,				['semantic'] * len(__a							)						,				__a						,				return_tensors='pt'						,				instance_id_to_semantic_id=__a						,				pad_and_return_pixel_mask=__a						,				)

						return inputs




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						pass




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

											__a			:       Optional[Any]					    =  inputs['mask_labels']
											__a			:       Optional[Any]					    =  inputs['class_labels']
											__a			:       Optional[Any]					    =  inputs['pixel_values']
											__a			:       Optional[Any]					    =  inputs['text_inputs']

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

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  np.zeros((20, 50)							)
						__a			:       List[Any]					    =  1
						__a			:       List[str]					    =  1
						__a			:       Optional[Any]					    =  1

						__a			:       List[str]					    =  binary_mask_to_rle(__a							)
						self.assertEqual(len(__a							)						,				4							)
						self.assertEqual(rle[0]						,				21							)
						self.assertEqual(rle[1]						,				45							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__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			:       Union[str, Any]					    =  self.image_processing_tester.get_fake_oneformer_outputs()

						__a			:       List[Any]					    =  fature_extractor.post_process_semantic_segmentation(__a							)

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

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

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[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[str]					    =  self.image_processing_tester.get_fake_oneformer_outputs()
						__a			:       Union[str, Any]					    =  image_processor.post_process_instance_segmentation(__a						,				threshold=0							)

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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__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[str]					    =  self.image_processing_tester.get_fake_oneformer_outputs()
						__a			:       Optional[int]					    =  image_processor.post_process_panoptic_segmentation(__a						,				threshold=0							)

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







'''simple docstring'''



import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Any					    =  test_file.split(os.path.sep   )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
										    F"""{test_file} instead."""   )
					__a			:       Tuple					    =  components[-1]
					if not test_fn.endswith('py'   ):
										raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
					if not test_fn.startswith('test_modeling_'   ):
										raise ValueError(
										    F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										if attr.endswith('ModelTester'   ):
															tester_classes.append(getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										__a			:       Optional[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      'all_model_classes'						,      []   )
										if len(_SCREAMING_SNAKE_CASE   ) > 0:
															test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes   )

					# sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  None
					if hasattr(_SCREAMING_SNAKE_CASE						,      'model_tester'   ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       int					    =  []
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       Any					    =  get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   )
										if tester_class is not None:
															tester_classes.append(_SCREAMING_SNAKE_CASE   )

    # sort with class names
					return sorted(_SCREAMING_SNAKE_CASE						,      key=lambda _SCREAMING_SNAKE_CASE   : x.__name__   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  {
					    model_class: get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_test_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    model_class: get_tester_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) for model_class in model_classes
					}
					return model_to_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return o.__name__
					elif isinstance(_SCREAMING_SNAKE_CASE						,      (list, tuple)   ):
										return [to_json(_SCREAMING_SNAKE_CASE   ) for x in o]
					elif isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										return {to_json(_SCREAMING_SNAKE_CASE   ): to_json(_SCREAMING_SNAKE_CASE   ) for k, v in o.items()}
					else:
										return o



