






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
    from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor





class 						SCREAMING_SNAKE_CASE				( snake_case					):





       """simple docstring"""

       def       __A (  self:   str   )    ->				Any:
            _A								= self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(__A		,     '''tf_padding'''   )   )
            self.parent.assertTrue(hasattr(__A		,     '''depth_multiplier'''   )   )





class 						SCREAMING_SNAKE_CASE				:





       """simple docstring"""

       def __init__(  self:   Any		,     __A:   Tuple		,     __A:   Optional[int]=13		,     __A:   Optional[Any]=3		,     __A:   Optional[Any]=32		,     __A:   Optional[Any]=0.25		,     __A:   Optional[Any]=8		,     __A:   Dict=8		,     __A:   str=6		,     __A:   Union[str, Any]=32		,     __A:   Any=True		,     __A:   int=True		,     __A:   List[Any]=True		,     __A:   Union[str, Any]="relu6"		,     __A:   Dict=12_80		,     __A:   Dict=0.1		,     __A:   List[str]=0.02		,     __A:   List[str]=True		,     __A:   List[Any]=True		,     __A:   Optional[Any]=10		,     __A:   Union[str, Any]=None		,     )    ->				Any:
            _A								= parent
            _A								= batch_size
            _A								= num_channels
            _A								= image_size
            _A								= depth_multiplier
            _A								= depth_divisible_by
            _A								= min_depth
            _A								= expand_ratio
            _A								= tf_padding
            _A								= output_stride
            _A								= first_layer_is_expansion
            _A								= finegrained_output
            _A								= hidden_act
            _A								= last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier   )
            _A								= classifier_dropout_prob
            _A								= use_labels
            _A								= is_training
            _A								= num_labels
            _A								= initializer_range
            _A								= scope

       def       __A (  self:   str   )    ->				str:
            _A								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _A								= None
            _A								= None
            if self.use_labels:
                 _A								= ids_tensor([self.batch_size]		,     self.num_labels   )
                 _A								= ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels   )

            _A								= self.get_config()

            return config, pixel_values, labels, pixel_labels

       def       __A (  self:   str   )    ->				Optional[Any]:
            return MobileNetVaConfig(
                num_channels=self.num_channels		,     image_size=self.image_size		,     depth_multiplier=self.depth_multiplier		,     depth_divisible_by=self.depth_divisible_by		,     min_depth=self.min_depth		,     expand_ratio=self.expand_ratio		,     output_stride=self.output_stride		,     first_layer_is_expansion=self.first_layer_is_expansion		,     finegrained_output=self.finegrained_output		,     hidden_act=self.hidden_act		,     tf_padding=self.tf_padding		,     classifier_dropout_prob=self.classifier_dropout_prob		,     initializer_range=self.initializer_range		,     )

       def       __A (  self:   List[str]		,     __A:   Union[str, Any]		,     __A:   List[Any]		,     __A:   int		,     __A:   Optional[int]   )    ->				Optional[Any]:
            _A								= MobileNetVaModel(config=__A   )
            model.to(__A   )
            model.eval()
            _A								= model(__A   )
            self.parent.assertEqual(
                result.last_hidden_state.shape		,     (
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )		,     )
            self.parent.assertEqual(
                result.pooler_output.shape		,     (self.batch_size, self.last_hidden_size)		,     )

       def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   List[str]   )    ->				Any:
            _A								= self.num_labels
            _A								= MobileNetVaForImageClassification(__A   )
            model.to(__A   )
            model.eval()
            _A								= model(__A		,     labels=__A   )
            self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)   )

       def       __A (  self:   Optional[int]		,     __A:   Tuple		,     __A:   str		,     __A:   Dict		,     __A:   List[Any]   )    ->				Optional[Any]:
            _A								= self.num_labels
            _A								= MobileNetVaForSemanticSegmentation(__A   )
            model.to(__A   )
            model.eval()
            _A								= model(__A   )
            self.parent.assertEqual(
                result.logits.shape		,     (
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )		,     )
            _A								= model(__A		,     labels=__A   )
            self.parent.assertEqual(
                result.logits.shape		,     (
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )		,     )



       def       __A (  self:   str   )    ->				Optional[Any]:
            _A								= self.prepare_config_and_inputs()
            _A							,_A							,_A							,_A								= config_and_inputs
            _A								= {'''pixel_values''': pixel_values}
            return config, inputs_dict





@require_torch
class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case  ,	unittest.TestCase					):





       """simple docstring"""

       A_						  =					(
           (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       A_						  =					(
           {
               "feature-extraction": MobileNetVaModel,
               "image-classification": MobileNetVaForImageClassification,
               "image-segmentation": MobileNetVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       A_						  =					False
       A_						  =					False
       A_						  =					False
       A_						  =					False

       def       __A (  self:   Any   )    ->				Union[str, Any]:
            _A								= MobileNetVaModelTester(self   )
            _A								= MobileNetVaConfigTester(self		,     config_class=__A		,     has_text_modality=__A   )

       def       __A (  self:   Dict   )    ->				Optional[Any]:
            self.config_tester.run_common_tests()

       @unittest.skip(reason='''MobileNetV2 does not use inputs_embeds'''   )
       def       __A (  self:   Union[str, Any]   )    ->				List[Any]:
            pass

       @unittest.skip(reason='''MobileNetV2 does not support input and output embeddings'''   )
       def       __A (  self:   Union[str, Any]   )    ->				str:
            pass

       @unittest.skip(reason='''MobileNetV2 does not output attentions'''   )
       def       __A (  self:   List[str]   )    ->				int:
            pass

       def       __A (  self:   Any   )    ->				Union[str, Any]:
            _A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _A								= model_class(__A   )
                 _A								= inspect.signature(model.forward   )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _A								= [*signature.parameters.keys()]

                 _A								= ['''pixel_values''']
                 self.assertListEqual(arg_names[:1]		,     __A   )

       def       __A (  self:   int   )    ->				str:
            _A								= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__A   )

       def       __A (  self:   List[Any]   )    ->				Optional[Any]:
            def check_hidden_states_output(__A:   Tuple		,     __A:   List[str]		,     __A:   Optional[Any]   ):
                 _A								= model_class(__A   )
                 model.to(__A   )
                 model.eval()

                 with torch.no_grad():
                      _A								= model(**self._prepare_for_class(__A		,     __A   )   )

                 _A								= outputs.hidden_states

                 _A								= 16
                 self.assertEqual(len(__A   )		,     __A   )

            _A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _A								= True
                 check_hidden_states_output(__A		,     __A		,     __A   )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _A								= True

                 check_hidden_states_output(__A		,     __A		,     __A   )

       def       __A (  self:   Any   )    ->				Dict:
            _A								= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__A   )

       def       __A (  self:   List[str]   )    ->				Any:
            _A								= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*__A   )



       @slow
       def       __A (  self:   Union[str, Any]   )    ->				Optional[int]:
            for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A								= MobileNetVaModel.from_pretrained(__A   )
                 self.assertIsNotNone(__A   )




def      __A					(     ):

     '''simple docstring'''







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





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





       """simple docstring"""

       @cached_property
       def       __A (  self:   int   )    ->				Any:
            return (
                MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224'''   ) if is_vision_available() else None
            )

       @slow
       def       __A (  self:   Tuple   )    ->				Union[str, Any]:
            _A								= MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224'''   ).to(__A   )

            _A								= self.default_image_processor
            _A								= prepare_img()
            _A								= image_processor(images=__A		,     return_tensors='''pt'''   ).to(__A   )

            # forward pass
            with torch.no_grad():
                 _A								= model(**__A   )

            # verify the logits
            _A								= torch.Size((1, 10_01)   )
            self.assertEqual(outputs.logits.shape		,     __A   )

            _A								= torch.tensor([0.2_445, -1.1_993, 0.1_905]   ).to(__A   )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     __A		,     atol=1e-4   )   )



       @slow
       def       __A (  self:   Union[str, Any]   )    ->				Tuple:
            _A								= MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''   )
            _A								= model.to(__A   )

            _A								= MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''   )

            _A								= prepare_img()
            _A								= image_processor(images=__A		,     return_tensors='''pt'''   ).to(__A   )

            # forward pass
            with torch.no_grad():
                 _A								= model(**__A   )
            _A								= outputs.logits

            # verify the logits
            _A								= torch.Size((1, 21, 65, 65)   )
            self.assertEqual(logits.shape		,     __A   )

            _A								= torch.tensor(
                [
                    [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                    [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                    [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
                ]		,     device=__A		,     )

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,     __A		,     atol=1e-4   )   )











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

					for values in graph.values():
										for i in values:
															indegree[i] += 1

					for i in range(len(_lowercase ) ):
										if indegree[i] == 0:
															queue.append(_lowercase )

					while queue:
										_A								= queue.pop(0 )
										cnt += 1
										topo.append(_lowercase )
										for x in graph[vertex]:
															indegree[x] -= 1
															if indegree[x] == 0:
																				queue.append(_lowercase )

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



