





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        _a    (    UpperCamelCase__):

      """simple docstring"""


      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.config_class(**self.inputs_dict					)
          self.parent.assertTrue(hasattr(__lowerCamelCase	,    "tf_padding"					)					)
          self.parent.assertTrue(hasattr(__lowerCamelCase	,    "depth_multiplier"					)					)




class        _a    :

      """simple docstring"""


      def __init__(		self:			Optional[Any]	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			Optional[int]=13	,    __lowerCamelCase:			Tuple=3	,    __lowerCamelCase:			Tuple=32	,    __lowerCamelCase:			Dict=0.25	,    __lowerCamelCase:			List[Any]=8	,    __lowerCamelCase:			Dict=8	,    __lowerCamelCase:			Union[str, Any]=6	,    __lowerCamelCase:			Any=32	,    __lowerCamelCase:			List[str]=True	,    __lowerCamelCase:			str=True	,    __lowerCamelCase:			List[Any]=True	,    __lowerCamelCase:			int="relu6"	,    __lowerCamelCase:			Dict=1280	,    __lowerCamelCase:			int=0.1	,    __lowerCamelCase:			str=0.02	,    __lowerCamelCase:			List[str]=True	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			Optional[int]=10	,    __lowerCamelCase:			Dict=None	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     parent
          UpperCamelCase__:	Optional[Any]					=     batch_size
          UpperCamelCase__:	str					=     num_channels
          UpperCamelCase__:	Optional[Any]					=     image_size
          UpperCamelCase__:	Optional[int]					=     depth_multiplier
          UpperCamelCase__:	Optional[int]					=     depth_divisible_by
          UpperCamelCase__:	Any					=     min_depth
          UpperCamelCase__:	Any					=     expand_ratio
          UpperCamelCase__:	List[str]					=     tf_padding
          UpperCamelCase__:	Dict					=     output_stride
          UpperCamelCase__:	Optional[int]					=     first_layer_is_expansion
          UpperCamelCase__:	List[str]					=     finegrained_output
          UpperCamelCase__:	Optional[int]					=     hidden_act
          UpperCamelCase__:	List[str]					=     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier					)
          UpperCamelCase__:	Optional[int]					=     classifier_dropout_prob
          UpperCamelCase__:	List[str]					=     use_labels
          UpperCamelCase__:	List[str]					=     is_training
          UpperCamelCase__:	Optional[int]					=     num_labels
          UpperCamelCase__:	List[Any]					=     initializer_range
          UpperCamelCase__:	int					=     scope






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          UpperCamelCase__:	Tuple					=     None
          UpperCamelCase__:	Optional[int]					=     None
          if self.use_labels:
              UpperCamelCase__:	Dict					=     ids_tensor([self.batch_size]	,    self.num_labels					)
              UpperCamelCase__:	int					=     ids_tensor([self.batch_size, self.image_size, self.image_size]	,    self.num_labels					)

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

          return config, pixel_values, labels, pixel_labels






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          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:			Optional[int]	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     MobileNetVaModel(config=__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	Optional[Any]					=     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:			Tuple	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[int]	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			str					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.num_labels
          UpperCamelCase__:	int					=     MobileNetVaForImageClassification(__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	str					=     model(__lowerCamelCase	,    labels=__lowerCamelCase					)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)					)






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     self.num_labels
          UpperCamelCase__:	int					=     MobileNetVaForSemanticSegmentation(__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          UpperCamelCase__:	Tuple					=     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,
              )	,    )
          UpperCamelCase__:	str					=     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:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.prepare_config_and_inputs()
          UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Optional[Any]					=     config_and_inputs
          UpperCamelCase__:	str					=     {"pixel_values": pixel_values}
          return config, inputs_dict




@require_torch
class        _a    (    UpperCamelCase__						,			UpperCamelCase__						,			unittest.TestCase):

      """simple docstring"""


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

      UpperCamelCase__  		=       False
      UpperCamelCase__  		=       False
      UpperCamelCase__  		=       False
      UpperCamelCase__  		=       False






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     MobileNetVaModelTester(self					)
          UpperCamelCase__:	Tuple					=     MobileNetVaConfigTester(self	,    config_class=__lowerCamelCase	,    has_text_modality=__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          self.config_tester.run_common_tests()






      @unittest.skip(reason="MobileNetV2 does not use inputs_embeds"					)
      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          pass






      @unittest.skip(reason="MobileNetV2 does not support input and output embeddings"					)
      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          pass






      @unittest.skip(reason="MobileNetV2 does not output attentions"					)
      def 			UpperCAmelCase_       (		self:			str					):
          '''simple docstring'''


          pass






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


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

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

              UpperCamelCase__:	List[str]					=     ["pixel_values"]
              self.assertListEqual(arg_names[:1]	,    __lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          def check_hidden_states_output(__lowerCamelCase:			Any	,    __lowerCamelCase:			List[Any]	,    __lowerCamelCase:			Union[str, Any]					):
              UpperCamelCase__:	Dict					=     model_class(__lowerCamelCase					)
              model.to(__lowerCamelCase					)
              model.eval()

              with torch.no_grad():
                  UpperCamelCase__:	int					=     model(**self._prepare_for_class(__lowerCamelCase	,    __lowerCamelCase					)					)

              UpperCamelCase__:	Optional[Any]					=     outputs.hidden_states

              UpperCamelCase__:	List[Any]					=     16
              self.assertEqual(len(__lowerCamelCase					)	,    __lowerCamelCase					)

          UpperCamelCase__				, UpperCamelCase__:	List[str]					=     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCamelCase__:	Dict					=     True
              check_hidden_states_output(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)

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

              check_hidden_states_output(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase					)






      @slow
      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase__:	List[str]					=     MobileNetVaModel.from_pretrained(__lowerCamelCase					)
              self.assertIsNotNone(__lowerCamelCase					)



def 				lowerCAmelCase_ (							):
    UpperCamelCase__:	List[str]					=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image




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

      """simple docstring"""


      @cached_property
      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          return (
              MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"					) if is_vision_available() else None
          )






      @slow
      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"					).to(__lowerCamelCase					)

          UpperCamelCase__:	Tuple					=     self.default_image_processor
          UpperCamelCase__:	str					=     prepare_img()
          UpperCamelCase__:	Dict					=     image_processor(images=__lowerCamelCase	,    return_tensors="pt"					).to(__lowerCamelCase					)

          # forward pass
          with torch.no_grad():
              UpperCamelCase__:	Tuple					=     model(**__lowerCamelCase					)

          # verify the logits
          UpperCamelCase__:	Dict					=     torch.Size((1, 1001)					)
          self.assertEqual(outputs.logits.shape	,    __lowerCamelCase					)

          UpperCamelCase__:	List[str]					=     torch.tensor([0.2_445, -1.1_993, 0.1_905]					).to(__lowerCamelCase					)

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






      @slow
      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"					)
          UpperCamelCase__:	Optional[int]					=     model.to(__lowerCamelCase					)

          UpperCamelCase__:	int					=     MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"					)

          UpperCamelCase__:	Optional[int]					=     prepare_img()
          UpperCamelCase__:	Optional[int]					=     image_processor(images=__lowerCamelCase	,    return_tensors="pt"					).to(__lowerCamelCase					)

          # forward pass
          with torch.no_grad():
              UpperCamelCase__:	Tuple					=     model(**__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     outputs.logits

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

          UpperCamelCase__:	Optional[int]					=     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=__lowerCamelCase	,    )

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








from __future__ import annotations

from collections import namedtuple



def 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    UpperCamelCase__:	List[str]					=     namedtuple("result"    ,"name value")
    if (voltage, current, power).count(0) != 1:
        raise ValueError("Only one argument must be 0")
    elif power < 0:
        raise ValueError(
            "Power cannot be negative in any electrical/electronics system")
    elif voltage == 0:
        return result("voltage"    ,power / current)
    elif current == 0:
        return result("current"    ,power / voltage)
    elif power == 0:
        return result("power"    ,float(round(abs(voltage * current)    ,2)))
    else:
        raise ValueError("Exactly one argument must be 0")


if __name__ == "__main__":
   import doctest

   doctest.testmod()

