'''simple docstring'''

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 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					List[str]:
         lowercase_     =      self.config_class(**self.inputs_dict)
         self.parent.assertTrue(hasattr(__lowerCAmelCase   ,		"tf_padding"))
         self.parent.assertTrue(hasattr(__lowerCAmelCase   ,		"depth_multiplier"))





class 							lowercase						:


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Tuple=13   ,		__lowerCAmelCase				:	Any=3   ,		__lowerCAmelCase				:	Optional[int]=32   ,		__lowerCAmelCase				:	Optional[Any]=0.25   ,		__lowerCAmelCase				:	List[Any]=8   ,		__lowerCAmelCase				:	Dict=8   ,		__lowerCAmelCase				:	int=6   ,		__lowerCAmelCase				:	Dict=32   ,		__lowerCAmelCase				:	Any=True   ,		__lowerCAmelCase				:	str=True   ,		__lowerCAmelCase				:	List[Any]=True   ,		__lowerCAmelCase				:	int="relu6"   ,		__lowerCAmelCase				:	int=1280   ,		__lowerCAmelCase				:	List[Any]=0.1   ,		__lowerCAmelCase				:	List[Any]=0.02   ,		__lowerCAmelCase				:	List[Any]=True   ,		__lowerCAmelCase				:	Any=True   ,		__lowerCAmelCase				:	Tuple=10   ,		__lowerCAmelCase				:	str=None   ,		)      ->					Tuple:
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      num_channels
         lowercase_     =      image_size
         lowercase_     =      depth_multiplier
         lowercase_     =      depth_divisible_by
         lowercase_     =      min_depth
         lowercase_     =      expand_ratio
         lowercase_     =      tf_padding
         lowercase_     =      output_stride
         lowercase_     =      first_layer_is_expansion
         lowercase_     =      finegrained_output
         lowercase_     =      hidden_act
         lowercase_     =      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier)
         lowercase_     =      classifier_dropout_prob
         lowercase_     =      use_labels
         lowercase_     =      is_training
         lowercase_     =      num_labels
         lowercase_     =      initializer_range
         lowercase_     =      scope


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[Any]:
         lowercase_     =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

         lowercase_     =      self.get_config()

         return config, pixel_values, labels, pixel_labels


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


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[int])      ->					List[Any]:
         lowercase_     =      self.num_labels
         lowercase_     =      MobileNetVaForSemanticSegmentation(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()
         lowercase_     =      model(__lowerCAmelCase)
         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_     =      model(__lowerCAmelCase   ,		labels=__lowerCAmelCase)
         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				:	Optional[int])      ->					Tuple:
         lowercase_     =      self.prepare_config_and_inputs()
         lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_     =      config_and_inputs
         lowercase_     =      {"pixel_values": pixel_values}
         return config, inputs_dict





@require_torch
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=(
       (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   lowerCamelCase_    					=(
       {
           'feature-extraction': MobileNetVaModel,
           'image-classification': MobileNetVaForImageClassification,
           'image-segmentation': MobileNetVaForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   lowerCamelCase_    					=False
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False


   def __UpperCAmelCase       (   self				:	int)      ->					str:
         lowercase_     =      MobileNetVaModelTester(self)
         lowercase_     =      MobileNetVaConfigTester(self   ,		config_class=__lowerCAmelCase   ,		has_text_modality=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Union[str, Any]:
         self.config_tester.run_common_tests()


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


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


   @unittest.skip(reason="MobileNetV2 does not output attentions")
   def __UpperCAmelCase       (   self				:	Tuple)      ->					Tuple:
         pass


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

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

               lowercase_     =      ["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str])      ->					str:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Optional[int]:
         def check_hidden_states_output(__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	int):
               lowercase_     =      model_class(__lowerCAmelCase)
               model.to(__lowerCAmelCase)
               model.eval()

               with torch.no_grad():
                     lowercase_     =      model(**self._prepare_for_class(__lowerCAmelCase   ,		__lowerCAmelCase))

               lowercase_     =      outputs.hidden_states

               lowercase_     =      16
               self.assertEqual(len(__lowerCAmelCase)   ,		__lowerCAmelCase)

         lowercase_ ,							lowercase_     =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase_     =      True
               check_hidden_states_output(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)

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

               check_hidden_states_output(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str])      ->					Any:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase)


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase_     =      MobileNetVaModel.from_pretrained(__lowerCAmelCase)
               self.assertIsNotNone(__lowerCAmelCase)




def __a (	)	->							Union[str, Any]:
      '''simple docstring'''


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






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


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


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         lowercase_     =      MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224").to(__lowerCAmelCase)

         lowercase_     =      self.default_image_processor
         lowercase_     =      prepare_img()
         lowercase_     =      image_processor(images=__lowerCAmelCase   ,		return_tensors="pt").to(__lowerCAmelCase)

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

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

         lowercase_     =      torch.tensor([0.2445, -1.1993, 0.1905]).to(__lowerCAmelCase)

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


   @slow
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         lowercase_     =      MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
         lowercase_     =      model.to(__lowerCAmelCase)

         lowercase_     =      MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")

         lowercase_     =      prepare_img()
         lowercase_     =      image_processor(images=__lowerCAmelCase   ,		return_tensors="pt").to(__lowerCAmelCase)

         # forward pass
         with torch.no_grad():
               lowercase_     =      model(**__lowerCAmelCase)
         lowercase_     =      outputs.logits

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

         lowercase_     =      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=__lowerCAmelCase   ,		)

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







'''simple docstring'''

def __a (	__lowerCamelCase			:						int = 200				)	->							int:
      '''simple docstring'''


      lowercase_     =      [1, 2, 5, 10, 20, 50, 100, 200]
      lowercase_     =      [0] * (pence + 1)
      lowercase_     =      1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(__lowerCamelCase ,		pence + 1 ,		1				):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682






