



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



    """simple docstring"""






    with open(hf_hub_download(A__				,					A__				,					repo_type="""dataset"""		)				,					"""r"""		) as f:
        A			:	List[str]										=       json.load(A__		)
    A			:	List[Any]										=       {}
    A			:	Union[str, Any]										=       []
    A			:	Tuple										=       []
    for key, info in class_info.items():
        A			:	List[Any]										=       info["""name"""]
        class_names.append(info["""name"""]		)
        if info["isthing"]:
            thing_ids.append(int(A__		)		)
    A			:	List[Any]										=       thing_ids
    A			:	Tuple										=       class_names
    return metadata






class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=7,		lowerCamelCase__=3,		lowerCamelCase__=30,		lowerCamelCase__=400,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=[0.5, 0.5, 0.5],		lowerCamelCase__=[0.5, 0.5, 0.5],		lowerCamelCase__=10,		lowerCamelCase__=False,		lowerCamelCase__=255,		lowerCamelCase__="shi-labs/oneformer_demo",		lowerCamelCase__="ade20k_panoptic.json",		lowerCamelCase__=10,		):
        A			:	List[Any]										=       parent
        A			:	str										=       batch_size
        A			:	int										=       num_channels
        A			:	Optional[Any]										=       min_resolution
        A			:	int										=       max_resolution
        A			:	Optional[Any]										=       do_resize
        A			:	Optional[Any]										=       {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
        A			:	Any										=       do_normalize
        A			:	Any										=       image_mean
        A			:	List[Any]										=       image_std
        A			:	Any										=       class_info_file
        A			:	List[Any]										=       prepare_metadata(UpperCamelCase_,		UpperCamelCase_		)
        A			:	Dict										=       num_text
        A			:	Tuple										=       repo_path

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






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

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

        return expected_height, expected_width






    def       _lowerCAmelCase (	self		):
        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__					(		__lowerCamelCase			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[int] =				OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
    # only for test_image_processing_common.test_image_proc_to_json_string
    __lowerCamelCase      :			Any =				image_processing_class






    def       _lowerCAmelCase (	self		):
        A			:	int										=       OneFormerImageProcessorTester(self		)






    @property
    def       _lowerCAmelCase (	self		):
        return self.image_processing_tester.prepare_image_processor_dict()






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       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       _lowerCAmelCase (	self		):
        pass






    def       _lowerCAmelCase (	self		):
        # Initialize image_processor
        A			:	Optional[int]										=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A			:	Dict										=       prepare_image_inputs(self.image_processing_tester,		equal_resolution=UpperCamelCase_		)
        for image in image_inputs:
            self.assertIsInstance(UpperCamelCase_,		Image.Image		)

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

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

        A			:	Dict										=       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       _lowerCAmelCase (	self		):
        # Initialize image_processor
        A			:	int										=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A			:	List[str]										=       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
        A			:	Any										=       image_processor(image_inputs[0],		["""semantic"""],		return_tensors="""pt"""		).pixel_values

        A					,							A			:	Tuple										=       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
        A					,							A			:	Tuple										=       self.image_processing_tester.get_expected_values(UpperCamelCase_,		batched=UpperCamelCase_		)

        A			:	Any										=       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       _lowerCAmelCase (	self		):
        # Initialize image_processor
        A			:	Tuple										=       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=UpperCamelCase_,		torchify=UpperCamelCase_		)
        for image in image_inputs:
            self.assertIsInstance(UpperCamelCase_,		torch.Tensor		)

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

        A					,							A			:	Optional[int]										=       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
        A					,							A			:	List[Any]										=       self.image_processing_tester.get_expected_values(UpperCamelCase_,		batched=UpperCamelCase_		)

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

        A			:	Union[str, Any]										=       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       _lowerCAmelCase (	self		):
        pass






    def       _lowerCAmelCase (	self		):
        def common(lowerCamelCase__=False,		lowerCamelCase__=None		):
            A			:	Optional[Any]										=       self.comm_get_image_processor_inputs(
                with_segmentation_maps=UpperCamelCase_,		is_instance_map=UpperCamelCase_,		segmentation_type=UpperCamelCase_		)

            A			:	Tuple										=       inputs["""mask_labels"""]
            A			:	Optional[int]										=       inputs["""class_labels"""]
            A			:	Dict										=       inputs["""pixel_values"""]
            A			:	Optional[Any]										=       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       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       np.zeros((20, 50)		)
        A			:	Tuple										=       1
        A			:	Optional[int]										=       1
        A			:	Any										=       1

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






    def       _lowerCAmelCase (	self		):
        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			:	int										=       self.image_processing_tester.get_fake_oneformer_outputs()

        A			:	Any										=       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,
            ),		)

        A			:	List[Any]										=       [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
        A			:	Tuple										=       fature_extractor.post_process_semantic_segmentation(UpperCamelCase_,		target_sizes=UpperCamelCase_		)

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






    def       _lowerCAmelCase (	self		):
        A			:	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""",		)
        A			:	Dict										=       self.image_processing_tester.get_fake_oneformer_outputs()
        A			:	Optional[int]										=       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       _lowerCAmelCase (	self		):
        A			:	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""",		)
        A			:	List[Any]										=       self.image_processing_tester.get_fake_oneformer_outputs()
        A			:	int										=       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)		)







import os
from pathlib import Path




def      __UpperCamelCase     (  )	->  Any:



    """simple docstring"""






    from torch.utils.cpp_extension import load

    A			:	Any										=       Path(_lowerCAmelCase		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
    A			:	int										=       [
        root / filename
        for filename in [
            """vision.cpp""",
            os.path.join("""cpu"""				,					"""ms_deform_attn_cpu.cpp"""		),
            os.path.join("""cuda"""				,					"""ms_deform_attn_cuda.cu"""		),
        ]
    ]

    load(
        """MultiScaleDeformableAttention"""				,					_lowerCAmelCase				,					with_cuda=_lowerCAmelCase				,					extra_include_paths=[str(_lowerCAmelCase		)]				,					extra_cflags=["""-DWITH_CUDA=1"""]				,					extra_cuda_cflags=[
            """-DCUDA_HAS_FP16=1""",
            """-D__CUDA_NO_HALF_OPERATORS__""",
            """-D__CUDA_NO_HALF_CONVERSIONS__""",
            """-D__CUDA_NO_HALF2_OPERATORS__""",
        ]				,					)

    import MultiScaleDeformableAttention as MSDA

    return MSDA


