import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

    if is_vision_available():
        from transformers import MaskFormerImageProcessor

if is_vision_available():
    from PIL import Image


class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=2			,			__lowercase=True			,			__lowercase=False			,			__lowercase=10			,			__lowercase=3			,			__lowercase=32 * 4			,			__lowercase=32 * 6			,			__lowercase=4			,			__lowercase=32			,			)	->     Dict:
        __UpperCamelCase					:Union[str, Any]        =		parent
        __UpperCamelCase					:Tuple        =		batch_size
        __UpperCamelCase					:List[Any]        =		is_training
        __UpperCamelCase					:Dict        =		use_auxiliary_loss
        __UpperCamelCase					:Any        =		num_queries
        __UpperCamelCase					:List[str]        =		num_channels
        __UpperCamelCase					:Any        =		min_size
        __UpperCamelCase					:Optional[Any]        =		max_size
        __UpperCamelCase					:List[Any]        =		num_labels
        __UpperCamelCase					:int        =		mask_feature_size







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Optional[Any]        =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
            _a)

        __UpperCamelCase					:int        =		torch.ones([self.batch_size, self.min_size, self.max_size]			,			device=_a)

        __UpperCamelCase					:int        =		(
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,			device=_a) > 0.5
        ).float()
        __UpperCamelCase					:str        =		(torch.rand((self.batch_size, self.num_labels)			,			device=_a) > 0.5).long()

        __UpperCamelCase					:List[Any]        =		self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels







    def       UpperCamelCase__       (     self)	->     int:
        return MaskFormerConfig.from_backbone_and_decoder_configs(
            backbone_config=SwinConfig(
                depths=[1, 1, 1, 1]			,			)			,			decoder_config=DetrConfig(
                decoder_ffn_dim=128			,			num_queries=self.num_queries			,			decoder_attention_heads=2			,			d_model=self.mask_feature_size			,			)			,			mask_feature_size=self.mask_feature_size			,			fpn_feature_size=self.mask_feature_size			,			num_channels=self.num_channels			,			num_labels=self.num_labels			,			)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Tuple        =		self.prepare_config_and_inputs()
        __UpperCamelCase					:Optional[Any]        =		{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
        return config, inputs_dict







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     int:
        __UpperCamelCase					:Union[str, Any]        =		output.encoder_hidden_states
        __UpperCamelCase					:str        =		output.pixel_decoder_hidden_states
        __UpperCamelCase					:Any        =		output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(_a)			,			len(config.backbone_config.depths))
        self.parent.assertTrue(len(_a)			,			len(config.backbone_config.depths))
        self.parent.assertTrue(len(_a)			,			config.decoder_config.decoder_layers)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase=False)	->     Any:
        with torch.no_grad():
            __UpperCamelCase					:Union[str, Any]        =		MaskFormerModel(config=_a)
            model.to(_a)
            model.eval()

            __UpperCamelCase					:List[str]        =		model(pixel_values=_a			,			pixel_mask=_a)
            __UpperCamelCase					:Any        =		model(_a			,			output_hidden_states=_a)
        # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
        # encoder and pixel decoder
        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape			,			(self.batch_size, self.num_queries, self.mask_feature_size)			,			)
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
        self.parent.assertTrue(output.encoder_last_hidden_state is not None)

        if output_hidden_states:
            self.check_output_hidden_state(_a			,			_a)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     Optional[int]:
        __UpperCamelCase					:List[str]        =		MaskFormerForInstanceSegmentation(config=_a)
        model.to(_a)
        model.eval()

        def comm_check_on_output(__lowercase):
            # let's still check that all the required stuff is there
            self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
            self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
            self.parent.assertTrue(result.encoder_last_hidden_state is not None)
            # okay, now we need to check the logits shape
            # due to the encoder compression, masks have a //4 spatial size
            self.parent.assertEqual(
                result.masks_queries_logits.shape			,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)			,			)
            # + 1 for null class
            self.parent.assertEqual(
                result.class_queries_logits.shape			,			(self.batch_size, self.num_queries, self.num_labels + 1))

        with torch.no_grad():
            __UpperCamelCase					:Optional[int]        =		model(pixel_values=_a			,			pixel_mask=_a)
            __UpperCamelCase					:Optional[Any]        =		model(_a)

            comm_check_on_output(_a)

            __UpperCamelCase					:List[Any]        =		model(
                pixel_values=_a			,			pixel_mask=_a			,			mask_labels=_a			,			class_labels=_a)

        comm_check_on_output(_a)

        self.parent.assertTrue(result.loss is not None)
        self.parent.assertEqual(result.loss.shape			,			torch.Size([1]))


@require_torch
class      lowerCamelCase_					(       UpperCAmelCase_						,     UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Tuple      =				(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    a__     :						Optional[Any]      =				(
        {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    a__     :						Tuple      =				False
    a__     :						Union[str, Any]      =				False
    a__     :						Optional[int]      =				False
    a__     :						str      =				False







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:int        =		MaskFormerModelTester(self)
        __UpperCamelCase					:Tuple        =		ConfigTester(self			,			config_class=_a			,			has_text_modality=_a)







    def       UpperCamelCase__       (     self)	->     List[Any]:
        self.config_tester.run_common_tests()







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Any        =		self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(_a			,			**_a			,			output_hidden_states=_a)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Tuple        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_a)







    @unittest.skip(reason='''MaskFormer does not use inputs_embeds''')
    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        pass







    @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''')
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        pass







    @unittest.skip(reason='''MaskFormer is not a generative model''')
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        pass







    @unittest.skip(reason='''MaskFormer does not use token embeddings''')
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        pass







    @require_torch_multi_gpu
    @unittest.skip(
        reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
    def       UpperCamelCase__       (     self)	->     Dict:
        pass







    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        pass







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:int        =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase					:List[Any]        =		model_class(_a)
            __UpperCamelCase					:int        =		inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __UpperCamelCase					:Union[str, Any]        =		[*signature.parameters.keys()]

            __UpperCamelCase					:Any        =		["pixel_values"]
            self.assertListEqual(arg_names[:1]			,			_a)







    @slow
    def       UpperCamelCase__       (     self)	->     List[Any]:
        for model_name in ["facebook/maskformer-swin-small-coco"]:
            __UpperCamelCase					:Tuple        =		MaskFormerModel.from_pretrained(_a)
            self.assertIsNotNone(_a)







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:str        =		(self.model_tester.min_size,) * 2
        __UpperCamelCase					:str        =		{
            "pixel_values": torch.randn((2, 3, *size)			,			device=_a),
            "mask_labels": torch.randn((2, 10, *size)			,			device=_a),
            "class_labels": torch.zeros(2			,			10			,			device=_a).long(),
        }

        __UpperCamelCase					:List[str]        =		MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(_a)
        __UpperCamelCase					:str        =		model(**_a)
        self.assertTrue(outputs.loss is not None)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:Tuple        =		self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskformer_model(_a			,			**_a			,			output_hidden_states=_a)







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Tuple        =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase					:str        =		model_class(_a).to(_a)
            __UpperCamelCase					:Union[str, Any]        =		model(**_a			,			output_attentions=_a)
            self.assertTrue(outputs.attentions is not None)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        if not self.model_tester.is_training:
            return
        # only MaskFormerForInstanceSegmentation has the loss
        __UpperCamelCase					:Any        =		self.all_model_classes[1]
        __UpperCamelCase					:str        =		self.model_tester.prepare_config_and_inputs()

        __UpperCamelCase					:List[Any]        =		model_class(_a)
        model.to(_a)
        model.train()

        __UpperCamelCase					:int        =		model(_a			,			mask_labels=_a			,			class_labels=_a).loss
        loss.backward()







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # only MaskFormerForInstanceSegmentation has the loss
        __UpperCamelCase					:Any        =		self.all_model_classes[1]
        __UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        __UpperCamelCase					:Dict        =		True
        __UpperCamelCase					:Optional[Any]        =		True

        __UpperCamelCase					:Dict        =		model_class(_a)
        model.to(_a)
        model.train()

        __UpperCamelCase					:int        =		model(_a			,			mask_labels=_a			,			class_labels=_a)

        __UpperCamelCase					:Union[str, Any]        =		outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        __UpperCamelCase					:Optional[int]        =		outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()
        # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
        __UpperCamelCase					:str        =		outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        __UpperCamelCase					:int        =		outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=_a)

        self.assertIsNotNone(encoder_hidden_states.grad)
        self.assertIsNotNone(pixel_decoder_hidden_states.grad)
        self.assertIsNotNone(transformer_decoder_hidden_states.grad)
        self.assertIsNotNone(attentions.grad)
__lowercase										= 1e-4



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Any        =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image


@require_vision
@slow
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    @cached_property
    def       UpperCamelCase__       (     self)	->     int:
        return (
            MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''')
            if is_vision_available()
            else None
        )







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Dict        =		MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''').to(_a)
        __UpperCamelCase					:Dict        =		self.default_image_processor
        __UpperCamelCase					:str        =		prepare_img()
        __UpperCamelCase					:Any        =		image_processor(_a			,			return_tensors='''pt''').to(_a)
        __UpperCamelCase					:str        =		inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
        # check size
        self.assertEqual(_a			,			(1, 3, 800, 1_088))

        with torch.no_grad():
            __UpperCamelCase					:Any        =		model(**_a)

        __UpperCamelCase					:List[Any]        =		torch.tensor(
            [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]).to(_a)
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]			,			_a			,			atol=_a))

        __UpperCamelCase					:Any        =		torch.tensor(
            [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]).to(_a)
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,			_a			,			atol=_a))

        __UpperCamelCase					:Tuple        =		torch.tensor(
            [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]).to(_a)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,			_a			,			atol=_a))







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Union[str, Any]        =		(
            MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
            .to(_a)
            .eval()
        )
        __UpperCamelCase					:Tuple        =		self.default_image_processor
        __UpperCamelCase					:str        =		prepare_img()
        __UpperCamelCase					:Union[str, Any]        =		image_processor(_a			,			return_tensors='''pt''').to(_a)
        __UpperCamelCase					:Union[str, Any]        =		inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
        # check size
        self.assertEqual(_a			,			(1, 3, 800, 1_088))

        with torch.no_grad():
            __UpperCamelCase					:Dict        =		model(**_a)
        # masks_queries_logits
        __UpperCamelCase					:Dict        =		outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape			,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,			)
        __UpperCamelCase					:Optional[int]        =		[
            [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
            [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
            [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
        ]
        __UpperCamelCase					:Dict        =		torch.tensor(_a).to(_a)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,			_a			,			atol=_a))
        # class_queries_logits
        __UpperCamelCase					:List[Any]        =		outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape			,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
        __UpperCamelCase					:List[Any]        =		torch.tensor(
            [
                [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
                [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
                [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
            ]).to(_a)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,			_a			,			atol=_a))







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Union[str, Any]        =		(
            MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''')
            .to(_a)
            .eval()
        )
        __UpperCamelCase					:List[Any]        =		self.default_image_processor
        __UpperCamelCase					:int        =		prepare_img()
        __UpperCamelCase					:Any        =		image_processor(_a			,			return_tensors='''pt''').to(_a)
        __UpperCamelCase					:List[str]        =		inputs["pixel_values"].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
        # check size
        self.assertEqual(_a			,			(1, 3, 800, 1_088))

        with torch.no_grad():
            __UpperCamelCase					:List[str]        =		model(**_a)
        # masks_queries_logits
        __UpperCamelCase					:str        =		outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape			,			(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,			)
        __UpperCamelCase					:Dict        =		[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
        __UpperCamelCase					:Any        =		torch.tensor(_a).to(_a)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,			_a			,			atol=_a))
        # class_queries_logits
        __UpperCamelCase					:Any        =		outputs.class_queries_logits
        self.assertEqual(
            class_queries_logits.shape			,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
        __UpperCamelCase					:List[Any]        =		torch.tensor(
            [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]).to(_a)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,			_a			,			atol=_a))







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:List[str]        =		(
            MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''')
            .to(_a)
            .eval()
        )
        __UpperCamelCase					:List[Any]        =		self.default_image_processor

        __UpperCamelCase					:Optional[int]        =		image_processor(
            [np.zeros((3, 800, 1_333)), np.zeros((3, 800, 1_333))]			,			segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)]			,			return_tensors='''pt'''			,			)

        __UpperCamelCase					:Optional[Any]        =		inputs["pixel_values"].to(_a)
        __UpperCamelCase					:int        =		[el.to(_a) for el in inputs["mask_labels"]]
        __UpperCamelCase					:List[str]        =		[el.to(_a) for el in inputs["class_labels"]]

        with torch.no_grad():
            __UpperCamelCase					:List[Any]        =		model(**_a)

        self.assertTrue(outputs.loss is not None)






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    print(f"""Vertex\tShortest Distance from vertex {src}"""     )
    for i, d in enumerate(SCREAMING_SNAKE_CASE     ):
        print(f"""{i}\t\t{d}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for j in range(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:int        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
            return True
    return False



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[float('''inf'''     )] * vertex_count
    __UpperCamelCase					:str        =		0.0

    for _ in range(vertex_count - 1     ):
        for j in range(SCREAMING_SNAKE_CASE     ):
            __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Dict        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
                __UpperCamelCase					:Any        =		distance[u] + w

    __UpperCamelCase					:Tuple        =		check_negative_cycle(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''     )

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowercase										= int(input('''Enter number of vertices: ''').strip())
    __lowercase										= int(input('''Enter number of edges: ''').strip())

    __lowercase										= [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        __lowercase			,	__lowercase			,	__lowercase										= (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        __lowercase										= {'''src''': src, '''dst''': dest, '''weight''': weight}

    __lowercase										= int(input('''\nEnter shortest path source:''').strip())
    __lowercase										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)





