



'''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  (					_lowercase	,				_lowercase="shi-labs/oneformer_demo"		)      ->			int:
							with open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		) as f:
														UpperCAmelCase     :		Optional[Any]							     =		json.load(_lowercase		)
							UpperCAmelCase     :		Tuple							     =		{}
							UpperCAmelCase     :		Union[str, Any]							     =		[]
							UpperCAmelCase     :		int							     =		[]
							for key, info in class_info.items():
														UpperCAmelCase     :		Union[str, Any]							     =		info["""name"""]
														class_names.append(info["""name"""]		)
														if info["isthing"]:
																					thing_ids.append(int(_lowercase		)		)
							UpperCAmelCase     :		Dict							     =		thing_ids
							UpperCAmelCase     :		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		,		)			->			Dict:
								UpperCAmelCase     :		int							     =		parent
								UpperCAmelCase     :		Optional[int]							     =		batch_size
								UpperCAmelCase     :		List[Any]							     =		num_channels
								UpperCAmelCase     :		Optional[Any]							     =		min_resolution
								UpperCAmelCase     :		int							     =		max_resolution
								UpperCAmelCase     :		List[str]							     =		do_resize
								UpperCAmelCase     :		Dict							     =		{"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
								UpperCAmelCase     :		Tuple							     =		do_normalize
								UpperCAmelCase     :		Any							     =		image_mean
								UpperCAmelCase     :		Any							     =		image_std
								UpperCAmelCase     :		Any							     =		class_info_file
								UpperCAmelCase     :		Any							     =		prepare_metadata(A		,		A    )
								UpperCAmelCase     :		Tuple							     =		num_text
								UpperCAmelCase     :		Dict							     =		repo_path

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





	def  _lowercase(  self    )			->			Dict:
								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		,		A		,		A=False    )			->			Tuple:
								if not batched:
															UpperCAmelCase     :		Optional[Any]							     =		image_inputs[0]
															if isinstance(A		,		Image.Image    ):
																						UpperCAmelCase     ,     UpperCAmelCase     :		Dict							     =		image.size
															else:
																						UpperCAmelCase     ,     UpperCAmelCase     :		Dict							     =		image.shape[1], image.shape[2]
															if w < h:
																						UpperCAmelCase     :		Tuple							     =		int(self.size["""shortest_edge"""] * h / w    )
																						UpperCAmelCase     :		Union[str, Any]							     =		self.size["""shortest_edge"""]
															elif w > h:
																						UpperCAmelCase     :		Union[str, Any]							     =		self.size["""shortest_edge"""]
																						UpperCAmelCase     :		Union[str, Any]							     =		int(self.size["""shortest_edge"""] * w / h    )
															else:
																						UpperCAmelCase     :		int							     =		self.size["""shortest_edge"""]
																						UpperCAmelCase     :		Dict							     =		self.size["""shortest_edge"""]

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

								return expected_height, expected_width






	def  _lowercase(  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 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	lowercase          = image_processing_class





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Tuple							     =		OneFormerImageProcessorTester(self    )





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





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		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  _lowercase(  self    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			Optional[int]:
								# Initialize image_processor
								UpperCAmelCase     :		Optional[int]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								UpperCAmelCase     :		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
								UpperCAmelCase     :		List[Any]							     =		image_processor(image_inputs[0]		,		["""semantic"""]		,		return_tensors="""pt"""    ).pixel_values

								UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		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
								UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		self.image_processing_tester.get_expected_values(A		,		batched=A    )

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

								UpperCAmelCase     ,     UpperCAmelCase     :		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
								UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		self.image_processing_tester.get_expected_values(A		,		batched=A    )

								UpperCAmelCase     :		Optional[int]							     =		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    )			->			List[Any]:
								# Initialize image_processor
								UpperCAmelCase     :		List[str]							     =		self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								UpperCAmelCase     :		Tuple							     =		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
								UpperCAmelCase     :		Any							     =		image_processor(image_inputs[0]		,		["""semantic"""]		,		return_tensors="""pt"""    ).pixel_values

								UpperCAmelCase     ,     UpperCAmelCase     :		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
								UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		self.image_processing_tester.get_expected_values(A		,		batched=A    )

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

								UpperCAmelCase     :		str							     =		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    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			Dict:
								def common(A=False		,		A=None    ):
															UpperCAmelCase     :		List[str]							     =		self.comm_get_image_processor_inputs(
															    with_segmentation_maps=A		,		is_instance_map=A		,		segmentation_type=A    )

															UpperCAmelCase     :		List[str]							     =		inputs["""mask_labels"""]
															UpperCAmelCase     :		Optional[Any]							     =		inputs["""class_labels"""]
															UpperCAmelCase     :		int							     =		inputs["""pixel_values"""]
															UpperCAmelCase     :		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  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		Dict							     =		np.zeros((20, 50)    )
								UpperCAmelCase     :		Union[str, Any]							     =		1
								UpperCAmelCase     :		Tuple							     =		1
								UpperCAmelCase     :		Any							     =		1

								UpperCAmelCase     :		Union[str, Any]							     =		binary_mask_to_rle(A    )
								self.assertEqual(len(A    )		,		4    )
								self.assertEqual(rle[0]		,		21    )
								self.assertEqual(rle[1]		,		45    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		List[str]							     =		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"""		,		)
								UpperCAmelCase     :		int							     =		self.image_processing_tester.get_fake_oneformer_outputs()

								UpperCAmelCase     :		int							     =		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,
								    )		,		)

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

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





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		Optional[int]							     =		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"""		,		)
								UpperCAmelCase     :		str							     =		self.image_processing_tester.get_fake_oneformer_outputs()
								UpperCAmelCase     :		List[str]							     =		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    )			->			Optional[int]:
								UpperCAmelCase     :		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"""		,		)
								UpperCAmelCase     :		Union[str, Any]							     =		self.image_processing_tester.get_fake_oneformer_outputs()
								UpperCAmelCase     :		List[Any]							     =		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   :			Optional[int]					  =    {
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a   :			Tuple					  =    [
							    """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GitForCausalLM""",
							    """GitModel""",
							    """GitPreTrainedModel""",
							    """GitVisionModel""",
							]

if TYPE_CHECKING:
							from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
							from .processing_git import GitProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_git import (
														    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GitForCausalLM,
														    GitModel,
														    GitPreTrainedModel,
														    GitVisionModel,
														)

else:
							import sys

							a   :			Tuple					  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

