
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    a      ( a		,						a="shi-labs/oneformer_demo"	)     ->List[Any]:




			'''simple docstring'''


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





class     lowerCamelCase      (       unittest.TestCase					):







	def __init__(     self      :Optional[int]   ,     lowercase      :Optional[Any]   ,     lowercase      :Tuple=7   ,     lowercase      :Tuple=3   ,     lowercase      :Optional[Any]=3_0   ,     lowercase      :Optional[Any]=4_0_0   ,     lowercase      :Any=None   ,     lowercase      :Union[str, Any]=True   ,     lowercase      :Optional[int]=True   ,     lowercase      :str=[0.5, 0.5, 0.5]   ,     lowercase      :Optional[Any]=[0.5, 0.5, 0.5]   ,     lowercase      :Union[str, Any]=1_0   ,     lowercase      :Optional[Any]=False   ,     lowercase      :Any=2_5_5   ,     lowercase      :List[str]="shi-labs/oneformer_demo"   ,     lowercase      :str="ade20k_panoptic.json"   ,     lowercase      :str=1_0   ,     )		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= parent
				SCREAMING_SNAKE_CASE     						= batch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= min_resolution
				SCREAMING_SNAKE_CASE     						= max_resolution
				SCREAMING_SNAKE_CASE     						= do_resize
				SCREAMING_SNAKE_CASE     						= {'''shortest_edge''': 3_2, '''longest_edge''': 1_3_3_3} if size is None else size
				SCREAMING_SNAKE_CASE     						= do_normalize
				SCREAMING_SNAKE_CASE     						= image_mean
				SCREAMING_SNAKE_CASE     						= image_std
				SCREAMING_SNAKE_CASE     						= class_info_file
				SCREAMING_SNAKE_CASE     						= prepare_metadata(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= num_text
				SCREAMING_SNAKE_CASE     						= repo_path

				# for the post_process_functions
				SCREAMING_SNAKE_CASE     						= 2
				SCREAMING_SNAKE_CASE     						= 1_0
				SCREAMING_SNAKE_CASE     						= 1_0
				SCREAMING_SNAKE_CASE     						= 3
				SCREAMING_SNAKE_CASE     						= 4
				SCREAMING_SNAKE_CASE     						= num_labels
				SCREAMING_SNAKE_CASE     						= do_reduce_labels
				SCREAMING_SNAKE_CASE     						= ignore_index







	def  snake_case__     (     self      :Dict			)		->      Tuple:







				"""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      :List[Any]   ,     lowercase      :List[str]   ,     lowercase      :List[str]=False			)		->      Optional[Any]:







				"""simple docstring"""






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

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

				return expected_height, expected_width







	def  snake_case__     (     self      :str			)		->      List[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     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					List[Any]    		=	OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	UpperCamelCase_  :					Any    		=	image_processing_class







	def  snake_case__     (     self      :str			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= OneFormerImageProcessorTester(self			)







	@property
	def  snake_case__     (     self      :int			)		->      List[str]:







				"""simple docstring"""






				return self.image_processing_tester.prepare_image_processor_dict()







	def  snake_case__     (     self      :Dict			)		->      Dict:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				pass







	def  snake_case__     (     self      :Union[str, Any]			)		->      Dict:







				"""simple docstring"""






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

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

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

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

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

				SCREAMING_SNAKE_CASE     						= image_processor(
				    lowercase   ,     ['''semantic'''] * len(lowercase			)   ,     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      :Dict			)		->      Tuple:







				"""simple docstring"""






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

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

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

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

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

				SCREAMING_SNAKE_CASE     						= image_processor(
				    lowercase   ,     ['''semantic'''] * len(lowercase			)   ,     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      :Tuple			)		->      Optional[int]:







				"""simple docstring"""






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

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

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

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

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

				SCREAMING_SNAKE_CASE     						= image_processor(
				    lowercase   ,     ['''semantic'''] * len(lowercase			)   ,     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      :Any   ,     lowercase      :List[str]=False   ,     lowercase      :Any=False   ,     lowercase      :List[Any]="np"			)		->      int:







				"""simple docstring"""






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

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

				return inputs







	def  snake_case__     (     self      :Dict			)		->      Union[str, Any]:







				"""simple docstring"""






				pass







	def  snake_case__     (     self      :List[str]			)		->      str:







				"""simple docstring"""






				def common(lowercase      :Optional[int]=False   ,     lowercase      :int=None			):
							SCREAMING_SNAKE_CASE     						= self.comm_get_image_processor_inputs(
							    with_segmentation_maps=lowercase   ,     is_instance_map=lowercase   ,     segmentation_type=lowercase			)

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

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

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







	def  snake_case__     (     self      :Any			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= np.zeros((2_0, 5_0)			)
				SCREAMING_SNAKE_CASE     						= 1
				SCREAMING_SNAKE_CASE     						= 1
				SCREAMING_SNAKE_CASE     						= 1

				SCREAMING_SNAKE_CASE     						= binary_mask_to_rle(lowercase			)
				self.assertEqual(len(lowercase			)   ,     4			)
				self.assertEqual(rle[0]   ,     2_1			)
				self.assertEqual(rle[1]   ,     4_5			)







	def  snake_case__     (     self      :Optional[int]			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes   ,     max_seq_length=7_7   ,     task_seq_length=7_7   ,     class_info_file='''ade20k_panoptic.json'''   ,     num_text=self.image_processing_tester.num_text   ,     repo_path='''shi-labs/oneformer_demo'''   ,     )
				SCREAMING_SNAKE_CASE     						= self.image_processing_tester.get_fake_oneformer_outputs()

				SCREAMING_SNAKE_CASE     						= fature_extractor.post_process_semantic_segmentation(lowercase			)

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

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

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







	def  snake_case__     (     self      :List[str]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes   ,     max_seq_length=7_7   ,     task_seq_length=7_7   ,     class_info_file='''ade20k_panoptic.json'''   ,     num_text=self.image_processing_tester.num_text   ,     repo_path='''shi-labs/oneformer_demo'''   ,     )
				SCREAMING_SNAKE_CASE     						= self.image_processing_tester.get_fake_oneformer_outputs()
				SCREAMING_SNAKE_CASE     						= image_processor.post_process_instance_segmentation(lowercase   ,     threshold=0			)

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







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes   ,     max_seq_length=7_7   ,     task_seq_length=7_7   ,     class_info_file='''ade20k_panoptic.json'''   ,     num_text=self.image_processing_tester.num_text   ,     repo_path='''shi-labs/oneformer_demo'''   ,     )
				SCREAMING_SNAKE_CASE     						= self.image_processing_tester.get_fake_oneformer_outputs()
				SCREAMING_SNAKE_CASE     						= image_processor.post_process_panoptic_segmentation(lowercase   ,     threshold=0			)

				self.assertTrue(len(lowercase			) == 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''']			)   ,     lowercase			)
							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, is_vision_available


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure)