





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






class 				__snake_case     (					unittest.TestCase							):





	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      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   num_channels
		snake_case__	       =   min_resolution
		snake_case__	       =   max_resolution
		snake_case__	       =   do_resize
		snake_case__	       =   {'shortest_edge': 32, 'longest_edge': 1333} if size is None else size
		snake_case__	       =   do_normalize
		snake_case__	       =   image_mean
		snake_case__	       =   image_std
		snake_case__	       =   class_info_file
		snake_case__	       =   prepare_metadata(UpperCamelCase_      ,       UpperCamelCase_		)
		snake_case__	       =   num_text
		snake_case__	       =   repo_path

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





	def   _snake_case   (		self		)      ->		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=False		)      ->		List[str]:
		if not batched:
			snake_case__	       =   image_inputs[0]
			if isinstance(UpperCamelCase_      ,       Image.Image		):
				snake_case__       ,  snake_case__	       =   image.size
			else:
				snake_case__       ,  snake_case__	       =   image.shape[1], image.shape[2]
			if w < h:
				snake_case__	       =   int(self.size['shortest_edge'] * h / w		)
				snake_case__	       =   self.size['shortest_edge']
			elif w > h:
				snake_case__	       =   self.size['shortest_edge']
				snake_case__	       =   int(self.size['shortest_edge'] * w / h		)
			else:
				snake_case__	       =   self.size['shortest_edge']
				snake_case__	       =   self.size['shortest_edge']

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

		return expected_height, expected_width





	def   _snake_case   (		self		)      ->		Tuple:
		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 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__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		)      ->		Optional[Any]:
		snake_case__	       =   OneFormerImageProcessorTester(self		)





	@property
	def   _snake_case   (		self		)      ->		str:
		return self.image_processing_tester.prepare_image_processor_dict()





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   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		)      ->		Any:
		pass





	def   _snake_case   (		self		)      ->		Optional[Any]:
		# Initialize image_processor
		snake_case__	       =   self.image_processing_class(**self.image_processor_dict		)
		# create random PIL images
		snake_case__	       =   prepare_image_inputs(self.image_processing_tester      ,       equal_resolution=UpperCamelCase_		)
		for image in image_inputs:
			self.assertIsInstance(UpperCamelCase_      ,       Image.Image		)

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

		snake_case__       ,  snake_case__	       =   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
		snake_case__       ,  snake_case__	       =   self.image_processing_tester.get_expected_values(UpperCamelCase_      ,       batched=UpperCamelCase_		)

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

		snake_case__       ,  snake_case__	       =   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
		snake_case__       ,  snake_case__	       =   self.image_processing_tester.get_expected_values(UpperCamelCase_      ,       batched=UpperCamelCase_		)

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

		snake_case__       ,  snake_case__	       =   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
		snake_case__       ,  snake_case__	       =   self.image_processing_tester.get_expected_values(UpperCamelCase_      ,       batched=UpperCamelCase_		)

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

		snake_case__	       =   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		)      ->		Union[str, Any]:
		pass





	def   _snake_case   (		self		)      ->		List[Any]:
		def common(UpperCamelCase_=False      ,       UpperCamelCase_=None		):
			snake_case__	       =   self.comm_get_image_processor_inputs(
			    with_segmentation_maps=UpperCamelCase_      ,       is_instance_map=UpperCamelCase_      ,       segmentation_type=UpperCamelCase_		)

			snake_case__	       =   inputs['mask_labels']
			snake_case__	       =   inputs['class_labels']
			snake_case__	       =   inputs['pixel_values']
			snake_case__	       =   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		)      ->		Optional[Any]:
		snake_case__	       =   np.zeros((20, 50)		)
		snake_case__	       =   1
		snake_case__	       =   1
		snake_case__	       =   1

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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   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'      ,       )
		snake_case__	       =   self.image_processing_tester.get_fake_oneformer_outputs()

		snake_case__	       =   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,
		    )      ,       )

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

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





	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   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'      ,       )
		snake_case__	       =   self.image_processing_tester.get_fake_oneformer_outputs()
		snake_case__	       =   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		)      ->		Dict:
		snake_case__	       =   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'      ,       )
		snake_case__	       =   self.image_processing_tester.get_fake_oneformer_outputs()
		snake_case__	       =   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)		)










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



