


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      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       self.config_class(**self.inputs_dict		)
        self.parent.assertTrue(hasattr(UpperCamelCase_   ,							'''tf_padding'''		)		)
        self.parent.assertTrue(hasattr(UpperCamelCase_   ,							'''depth_multiplier'''		)		)






class      UpperCamelCase					:


   '''simple docstring'''







   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_=1280   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=10   ,							UpperCamelCase_=None   ,							):
        lowercase_       :Optional[Any]     =       parent
        lowercase_       :Tuple     =       batch_size
        lowercase_       :List[Any]     =       num_channels
        lowercase_       :Tuple     =       image_size
        lowercase_       :Any     =       depth_multiplier
        lowercase_       :List[Any]     =       depth_divisible_by
        lowercase_       :Any     =       min_depth
        lowercase_       :Tuple     =       expand_ratio
        lowercase_       :List[str]     =       tf_padding
        lowercase_       :Dict     =       output_stride
        lowercase_       :Optional[Any]     =       first_layer_is_expansion
        lowercase_       :Optional[Any]     =       finegrained_output
        lowercase_       :List[Any]     =       hidden_act
        lowercase_       :int     =       last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
        lowercase_       :Dict     =       classifier_dropout_prob
        lowercase_       :Tuple     =       use_labels
        lowercase_       :List[Any]     =       is_training
        lowercase_       :Tuple     =       num_labels
        lowercase_       :str     =       initializer_range
        lowercase_       :List[str]     =       scope
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        lowercase_       :str     =       self.get_config()

        return config, pixel_values, labels, pixel_labels
   def 		UpperCamelCase  (     self		):
        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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Optional[Any]     =       MobileNetVaModel(config=UpperCamelCase_		)
        model.to(UpperCamelCase_		)
        model.eval()
        lowercase_       :Optional[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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       self.num_labels
        lowercase_       :Tuple     =       MobileNetVaForImageClassification(UpperCamelCase_		)
        model.to(UpperCamelCase_		)
        model.eval()
        lowercase_       :Any     =       model(UpperCamelCase_   ,							labels=UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :List[Any]     =       self.num_labels
        lowercase_       :List[str]     =       MobileNetVaForSemanticSegmentation(UpperCamelCase_		)
        model.to(UpperCamelCase_		)
        model.eval()
        lowercase_       :List[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,
            )   ,							)
        lowercase_       :List[Any]     =       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 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       self.prepare_config_and_inputs()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Tuple     =       config_and_inputs
        lowercase_       :List[Any]     =       {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	str									=(
       (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   lowercase					:	Union[str, Any]									=(
       {
           """feature-extraction""": MobileNetVaModel,
           """image-classification""": MobileNetVaForImageClassification,
           """image-segmentation""": MobileNetVaForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   lowercase					:	str									=False
   lowercase					:	Dict									=False
   lowercase					:	Optional[Any]									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       MobileNetVaModelTester(self		)
        lowercase_       :Union[str, Any]     =       MobileNetVaConfigTester(self   ,							config_class=UpperCamelCase_   ,							has_text_modality=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   @unittest.skip(reason='''MobileNetV2 does not use inputs_embeds'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skip(reason='''MobileNetV2 does not support input and output embeddings'''		)
   def 		UpperCamelCase  (     self		):
        pass
   @unittest.skip(reason='''MobileNetV2 does not output attentions'''		)
   def 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Optional[int]     =       self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_       :int     =       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        def check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Any     =       model_class(UpperCamelCase_		)
             model.to(UpperCamelCase_		)
             model.eval()

             with torch.no_grad():
                  lowercase_       :Optional[Any]     =       model(**self._prepare_for_class(UpperCamelCase_   ,							UpperCamelCase_		)		)

             lowercase_       :List[Any]     =       outputs.hidden_states

             lowercase_       :Union[str, Any]     =       16
             self.assertEqual(len(UpperCamelCase_		)   ,							UpperCamelCase_		)

        lowercase_    ,      lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_       :Optional[Any]     =       True
             check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)

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

             check_hidden_states_output(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_		)




   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :int     =       MobileNetVaModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)




def     UpperCamelCase    (      )					->							Dict:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
     return image






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


   '''simple docstring'''







   @cached_property
   def 		UpperCamelCase  (     self		):
        return (
            MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224'''		) if is_vision_available() else None
        )
   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224'''		).to(UpperCamelCase_		)

        lowercase_       :Optional[int]     =       self.default_image_processor
        lowercase_       :Optional[Any]     =       prepare_img()
        lowercase_       :Optional[Any]     =       image_processor(images=UpperCamelCase_   ,							return_tensors='''pt'''		).to(UpperCamelCase_		)

        # forward pass
        with torch.no_grad():
             lowercase_       :Any     =       model(**UpperCamelCase_		)

        # verify the logits
        lowercase_       :Tuple     =       torch.Size((1, 1001)		)
        self.assertEqual(outputs.logits.shape   ,							UpperCamelCase_		)

        lowercase_       :Optional[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 		UpperCamelCase  (     self		):
        lowercase_       :int     =       MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''		)
        lowercase_       :Optional[int]     =       model.to(UpperCamelCase_		)

        lowercase_       :int     =       MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''		)

        lowercase_       :Optional[Any]     =       prepare_img()
        lowercase_       :Optional[Any]     =       image_processor(images=UpperCamelCase_   ,							return_tensors='''pt'''		).to(UpperCamelCase_		)

        # forward pass
        with torch.no_grad():
             lowercase_       :Optional[Any]     =       model(**UpperCamelCase_		)
        lowercase_       :Any     =       outputs.logits

        # verify the logits
        lowercase_       :Any     =       torch.Size((1, 21, 65, 65)		)
        self.assertEqual(logits.shape   ,							UpperCamelCase_		)

        lowercase_       :Tuple     =       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		)		)





def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     def count_of_possible_combinations(_a		) -> int:
          if target < 0:
               return 0
          if target == 0:
               return 1
          return sum(count_of_possible_combinations(target - item		) for item in array		)

     return count_of_possible_combinations(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     def count_of_possible_combinations_with_dp_array(
         _a       ,		_a		) -> int:
          if target < 0:
               return 0
          if target == 0:
               return 1
          if dp_array[target] != -1:
               return dp_array[target]
          lowercase_       :Optional[int]     =       sum(
              count_of_possible_combinations_with_dp_array(target - item       ,		_a		)
              for item in array		)
          lowercase_       :List[Any]     =       answer
          return answer

     lowercase_       :Dict     =       [-1] * (target + 1)
     return count_of_possible_combinations_with_dp_array(_a       ,		_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       [0] * (target + 1)
     lowercase_       :int     =       1

     for i in range(1       ,		target + 1		):
          for j in range(_a		):
               if i - array[j] >= 0:
                    dp_array[i] += dp_array[i - array[j]]

     return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   SCREAMING_SNAKE_CASE       :		Optional[Any]         =				3
   SCREAMING_SNAKE_CASE       :		Tuple         =				5
   SCREAMING_SNAKE_CASE       :		List[Any]         =				[1, 2, 5]
   print(combination_sum_iv(n, array, target))

