






"""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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_="shi-labs/oneformer_demo"):
					with open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r''') as f:
										a__          =  json.load(lowerCamelCase_)
					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(lowerCamelCase_))
					a__          =  thing_ids
					a__          =  class_names
					return metadata







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     Optional[int]				,		__A:     List[Any]				,		__A:     str=7				,		__A:     Any=3				,		__A:     Union[str, Any]=30				,		__A:     Union[str, Any]=400				,		__A:     Optional[Any]=None				,		__A:     Union[str, Any]=True				,		__A:     Optional[Any]=True				,		__A:     Union[str, Any]=[0.5, 0.5, 0.5]				,		__A:     Tuple=[0.5, 0.5, 0.5]				,		__A:     Tuple=10				,		__A:     int=False				,		__A:     Optional[int]=255				,		__A:     List[str]="shi-labs/oneformer_demo"				,		__A:     Optional[int]="ade20k_panoptic.json"				,		__A:     str=10				,		):

												'''simple docstring'''

												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(__A				,		__A    )
												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  lowercase		(   self:     Optional[Any]    ):

												'''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  lowercase		(   self:     int				,		__A:     List[Any]				,		__A:     List[str]=False    ):

												'''simple docstring'''

												if not batched:
																	a__          =  image_inputs[0]
																	if isinstance(__A				,		Image.Image    ):
																						a__       ,a__          =  image.size
																	else:
																						a__       ,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__       ,a__          =  self.get_expected_values([image]    )
																						expected_values.append((expected_height, expected_width)    )
																	a__          =  max(__A				,		key=lambda __A    : item[0]    )[0]
																	a__          =  max(__A				,		key=lambda __A    : item[1]    )[1]

												return expected_height, expected_width





							def  lowercase		(   self:     List[Any]    ):

												'''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        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
							# only for test_image_processing_common.test_image_proc_to_json_string
							_SCREAMING_SNAKE_CASE    =image_processing_class




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  OneFormerImageProcessorTester(self    )




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

												'''simple docstring'''

												return self.image_processing_tester.prepare_image_processor_dict()




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  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  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												# create random PIL images
												a__          =  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__          =  image_processor(image_inputs[0]				,		['''semantic''']				,		return_tensors='''pt'''    ).pixel_values

												a__       ,a__          =  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__          =  self.image_processing_tester.get_expected_values(__A				,		batched=__A    )

												a__          =  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  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												# create random numpy tensors
												a__          =  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__          =  image_processor(image_inputs[0]				,		['''semantic''']				,		return_tensors='''pt'''    ).pixel_values

												a__       ,a__          =  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__          =  self.image_processing_tester.get_expected_values(__A				,		batched=__A    )

												a__          =  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  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												# create random PyTorch tensors
												a__          =  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__          =  image_processor(image_inputs[0]				,		['''semantic''']				,		return_tensors='''pt'''    ).pixel_values

												a__       ,a__          =  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__          =  self.image_processing_tester.get_expected_values(__A				,		batched=__A    )

												a__          =  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  lowercase		(   self:     Optional[Any]				,		__A:     Optional[Any]=False				,		__A:     Optional[int]=False				,		__A:     int="np"    ):

												'''simple docstring'''

												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=__A    )
												if with_segmentation_maps:
																	a__          =  num_labels
																	if is_instance_map:
																						a__          =  list(range(__A    )    ) * 2
																						a__          =  dict(enumerate(__A    )    )
																	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(__A    ) for annotation in annotations]

												a__          =  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  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												pass




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

												'''simple docstring'''

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

																	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(__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  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

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

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												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(__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__          =  [(1, 4) for i in range(self.image_processing_tester.batch_size    )]
												a__          =  fature_extractor.post_process_semantic_segmentation(__A				,		target_sizes=__A    )

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




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												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(__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  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												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(__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"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


