






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, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor
class 						lowerCamelCase_				(      lowerCamelCase ):







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:int        =      self.config_class(**self.inputs_dict     )
        self.parent.assertTrue(hasattr(__lowerCAmelCase       ,					'''tf_padding'''     )     )
        self.parent.assertTrue(hasattr(__lowerCAmelCase       ,					'''depth_multiplier'''     )     )







class 						lowerCamelCase_				:







    def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=0.25       ,					__lowerCAmelCase=8       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=1_0_2_4       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase="relu6"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=None       ,					):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      parent
        __magic_name__		:Any        =      batch_size
        __magic_name__		:List[Any]        =      num_channels
        __magic_name__		:List[Any]        =      image_size
        __magic_name__		:Any        =      depth_multiplier
        __magic_name__		:List[Any]        =      min_depth
        __magic_name__		:List[str]        =      tf_padding
        __magic_name__		:List[str]        =      int(last_hidden_size * depth_multiplier     )
        __magic_name__		:Optional[int]        =      output_stride
        __magic_name__		:Dict        =      hidden_act
        __magic_name__		:List[Any]        =      classifier_dropout_prob
        __magic_name__		:int        =      use_labels
        __magic_name__		:Optional[int]        =      is_training
        __magic_name__		:str        =      num_labels
        __magic_name__		:Optional[Any]        =      initializer_range
        __magic_name__		:List[Any]        =      scope







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Dict        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        __magic_name__		:str        =      self.get_config()

        return config, pixel_values, labels, pixel_labels







    def 			A	(						self     ):
        """simple docstring"""
        return MobileNetVaConfig(
            num_channels=self.num_channels       ,					image_size=self.image_size       ,					depth_multiplier=self.depth_multiplier       ,					min_depth=self.min_depth       ,					tf_padding=self.tf_padding       ,					hidden_act=self.hidden_act       ,					classifier_dropout_prob=self.classifier_dropout_prob       ,					initializer_range=self.initializer_range       ,					)







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      MobileNetVaModel(config=__lowerCAmelCase     )
        model.to(__lowerCAmelCase     )
        model.eval()
        __magic_name__		:str        =      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,
            )       ,					)







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      self.num_labels
        __magic_name__		:Union[str, Any]        =      MobileNetVaForImageClassification(__lowerCAmelCase     )
        model.to(__lowerCAmelCase     )
        model.eval()
        __magic_name__		:int        =      model(__lowerCAmelCase       ,					labels=__lowerCAmelCase     )
        self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      self.prepare_config_and_inputs()
        __magic_name__		:Union[str, Any]        =      config_and_inputs
        __magic_name__		:Optional[Any]        =      {'''pixel_values''': pixel_values}
        return config, inputs_dict







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

    a__					       =  False
    a__					       =  False
    a__					       =  False
    a__					       =  False







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:str        =      MobileNetVaModelTester(self     )
        __magic_name__		:Tuple        =      MobileNetVaConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    @unittest.skip(reason='''MobileNetV1 does not output attentions'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:int        =      self.model_tester.prepare_config_and_inputs_for_common()

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

            __magic_name__		:List[str]        =      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:int        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        def check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
            __magic_name__		:Optional[int]        =      model_class(__lowerCAmelCase     )
            model.to(__lowerCAmelCase     )
            model.eval()

            with torch.no_grad():
                __magic_name__		:List[str]        =      model(**self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

            __magic_name__		:List[str]        =      outputs.hidden_states

            __magic_name__		:Optional[int]        =      2_6
            self.assertEqual(len(__lowerCAmelCase     )       ,					__lowerCAmelCase     )

        __magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

            check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase     )







    @slow
    def 			A	(						self     ):
        """simple docstring"""
        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __magic_name__		:Optional[int]        =      MobileNetVaModel.from_pretrained(__lowerCAmelCase     )
            self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







    """simple docstring"""






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

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







    @cached_property
    def 			A	(						self     ):
        """simple docstring"""
        return (
            MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''     ) if is_vision_available() else None
        )







    @slow
    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Any        =      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''     ).to(__lowerCAmelCase     )

        __magic_name__		:Union[str, Any]        =      self.default_image_processor
        __magic_name__		:str        =      prepare_img()
        __magic_name__		:Optional[int]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''pt'''     ).to(__lowerCAmelCase     )

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

        # verify the logits
        __magic_name__		:str        =      torch.Size((1, 1_0_0_1)     )
        self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

        __magic_name__		:Union[str, Any]        =      torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(__lowerCAmelCase     )

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














from __future__ import annotations

from math import pi, sqrt

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative''' )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative''' )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






