






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 					_snake_case						(			_A ):






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :int         =   self.config_class(**self.inputs_dict       )
					self.parent.assertTrue(hasattr(UpperCamelCase					,"tf_padding"       )       )
					self.parent.assertTrue(hasattr(UpperCamelCase					,"depth_multiplier"       )       )




class 					_snake_case						:






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=13					,UpperCamelCase=3					,UpperCamelCase=32					,UpperCamelCase=0.25					,UpperCamelCase=8					,UpperCamelCase=8					,UpperCamelCase=6					,UpperCamelCase=32					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase="relu6"					,UpperCamelCase=1_280					,UpperCamelCase=0.1					,UpperCamelCase=0.02					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=10					,UpperCamelCase=None					,)						->    Any:
					snake_case__      :Tuple         =   parent
					snake_case__      :str         =   batch_size
					snake_case__      :Union[str, Any]         =   num_channels
					snake_case__      :Optional[int]         =   image_size
					snake_case__      :str         =   depth_multiplier
					snake_case__      :List[Any]         =   depth_divisible_by
					snake_case__      :Union[str, Any]         =   min_depth
					snake_case__      :Dict         =   expand_ratio
					snake_case__      :Any         =   tf_padding
					snake_case__      :Optional[Any]         =   output_stride
					snake_case__      :Optional[int]         =   first_layer_is_expansion
					snake_case__      :List[Any]         =   finegrained_output
					snake_case__      :str         =   hidden_act
					snake_case__      :List[Any]         =   last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier       )
					snake_case__      :Union[str, Any]         =   classifier_dropout_prob
					snake_case__      :Optional[Any]         =   use_labels
					snake_case__      :Optional[Any]         =   is_training
					snake_case__      :Dict         =   num_labels
					snake_case__      :Union[str, Any]         =   initializer_range
					snake_case__      :Dict         =   scope






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :List[Any]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

					snake_case__      :List[Any]         =   None
					snake_case__      :Dict         =   None
					if self.use_labels:
									snake_case__      :str         =   ids_tensor([self.batch_size]					,self.num_labels       )
									snake_case__      :List[str]         =   ids_tensor([self.batch_size, self.image_size, self.image_size]					,self.num_labels       )

					snake_case__      :Optional[Any]         =   self.get_config()

					return config, pixel_values, labels, pixel_labels






	def    lowerCAmelCase_							(		self       )						->    List[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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Optional[int]:
					snake_case__      :Dict         =   MobileNetVaModel(config=UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :Union[str, Any]         =   model(UpperCamelCase       )
					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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Dict:
					snake_case__      :List[Any]         =   self.num_labels
					snake_case__      :Dict         =   MobileNetVaForImageClassification(UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :Union[str, Any]         =   model(UpperCamelCase					,labels=UpperCamelCase       )
					self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Dict:
					snake_case__      :str         =   self.num_labels
					snake_case__      :List[str]         =   MobileNetVaForSemanticSegmentation(UpperCamelCase       )
					model.to(UpperCamelCase       )
					model.eval()
					snake_case__      :Optional[Any]         =   model(UpperCamelCase       )
					self.parent.assertEqual(
					    result.logits.shape					,(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )					,)
					snake_case__      :str         =   model(UpperCamelCase					,labels=UpperCamelCase       )
					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    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :List[Any]         =   self.prepare_config_and_inputs()
					snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :int         =   config_and_inputs
					snake_case__      :List[str]         =   {"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class 					_snake_case						(			_A       ,    _A       ,    unittest.TestCase ):
	_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    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[str]         =   MobileNetVaModelTester(self       )
					snake_case__      :int         =   MobileNetVaConfigTester(self					,config_class=UpperCamelCase					,has_text_modality=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    int:
					self.config_tester.run_common_tests()






	@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					pass






	@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"       )
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					pass






	@unittest.skip(reason="MobileNetV2 does not output attentions"       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__    ,						snake_case__      :str         =   self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case__      :Any         =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :List[str]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					def check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       ):
									snake_case__      :Union[str, Any]         =   model_class(UpperCamelCase       )
									model.to(UpperCamelCase       )
									model.eval()

									with torch.no_grad():
													snake_case__      :str         =   model(**self._prepare_for_class(UpperCamelCase					,UpperCamelCase       )       )

									snake_case__      :Dict         =   outputs.hidden_states

									snake_case__      :List[Any]         =   16
									self.assertEqual(len(UpperCamelCase       )					,UpperCamelCase       )

					snake_case__    ,						snake_case__      :Optional[int]         =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									snake_case__      :Optional[int]         =   True
									check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									snake_case__      :str         =   True

									check_hidden_states_output(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Tuple         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[Any]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__      :str         =   MobileNetVaModel.from_pretrained(UpperCamelCase       )
									self.assertIsNotNone(UpperCamelCase       )





def 					lowercase_ (    )     -> Tuple:





				'''simple docstring'''


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


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






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






	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Union[str, Any]         =   MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"       ).to(UpperCamelCase       )

					snake_case__      :int         =   self.default_image_processor
					snake_case__      :Any         =   prepare_img()
					snake_case__      :int         =   image_processor(images=UpperCamelCase					,return_tensors="pt"       ).to(UpperCamelCase       )

					# forward pass
					with torch.no_grad():
									snake_case__      :Union[str, Any]         =   model(**UpperCamelCase       )

					# verify the logits
					snake_case__      :Optional[Any]         =   torch.Size((1, 1_001)       )
					self.assertEqual(outputs.logits.shape					,UpperCamelCase       )

					snake_case__      :Any         =   torch.tensor([0.2445, -1.1993, 0.1905]       ).to(UpperCamelCase       )

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






	@slow
	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :List[Any]         =   MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"       )
					snake_case__      :Dict         =   model.to(UpperCamelCase       )

					snake_case__      :List[str]         =   MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"       )

					snake_case__      :List[str]         =   prepare_img()
					snake_case__      :Optional[Any]         =   image_processor(images=UpperCamelCase					,return_tensors="pt"       ).to(UpperCamelCase       )

					# forward pass
					with torch.no_grad():
									snake_case__      :Union[str, Any]         =   model(**UpperCamelCase       )
					snake_case__      :List[str]         =   outputs.logits

					# verify the logits
					snake_case__      :Dict         =   torch.Size((1, 21, 65, 65)       )
					self.assertEqual(logits.shape					,UpperCamelCase       )

					snake_case__      :Union[str, Any]         =   torch.tensor(
					    [
					        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
					        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
					        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
					    ]					,device=UpperCamelCase					,)

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







__UpperCAmelCase							:   int							=  {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase							:   List[str]							=  ["a", "b", "c", "d", "e"]





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							List[Any]	,	__snake_case     :							Tuple      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :List[Any]         =   start
				# add current to visited
				visited.append(__snake_case      )
				snake_case__      :List[str]         =   edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												snake_case__      :Any         =   topological_sort(__snake_case	,	__snake_case	,	__snake_case      )
    # if all neighbors visited add current to sort
				sort.append(__snake_case      )
				# if all vertices haven't been visited select a new one to visit
				if len(__snake_case      ) != len(__snake_case      ):
								for vertice in vertices:
												if vertice not in visited:
																snake_case__      :Any         =   topological_sort(__snake_case	,	__snake_case	,	__snake_case      )
    # return sort
				return sort


if __name__ == "__main__":
	__UpperCAmelCase							:   Tuple							=  topological_sort("a", [], [])
	print(sort)