





'''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, 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 				a_       (    _lowerCAmelCase							):





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        lowercase_      :str         =			self.config_class(**self.inputs_dict						)
        self.parent.assertTrue(hasattr(lowercase     ,				"tf_padding"						)						)
        self.parent.assertTrue(hasattr(lowercase     ,				"depth_multiplier"						)						)







class 				a_       :





     def __init__(			self		: str     ,				lowercase		: Dict     ,				lowercase		: int=13     ,				lowercase		: Tuple=3     ,				lowercase		: Optional[int]=32     ,				lowercase		: int=0.25     ,				lowercase		: int=8     ,				lowercase		: int=True     ,				lowercase		: List[Any]=1_024     ,				lowercase		: List[Any]=32     ,				lowercase		: Union[str, Any]="relu6"     ,				lowercase		: Optional[int]=0.1     ,				lowercase		: Optional[Any]=0.02     ,				lowercase		: Optional[Any]=True     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: Union[str, Any]=10     ,				lowercase		: int=None     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			parent
        lowercase_      :Optional[Any]         =			batch_size
        lowercase_      :Optional[Any]         =			num_channels
        lowercase_      :List[Any]         =			image_size
        lowercase_      :Optional[int]         =			depth_multiplier
        lowercase_      :Dict         =			min_depth
        lowercase_      :Any         =			tf_padding
        lowercase_      :Optional[Any]         =			int(last_hidden_size * depth_multiplier						)
        lowercase_      :str         =			output_stride
        lowercase_      :List[Any]         =			hidden_act
        lowercase_      :Optional[Any]         =			classifier_dropout_prob
        lowercase_      :Any         =			use_labels
        lowercase_      :Optional[Any]         =			is_training
        lowercase_      :Dict         =			num_labels
        lowercase_      :str         =			initializer_range
        lowercase_      :Optional[Any]         =			scope





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

        lowercase_      :Optional[int]         =			self.get_config()

        return config, pixel_values, labels, pixel_labels





     def 							lowercase__				(			self		: List[str]						):





        """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 							lowercase__				(			self		: int     ,				lowercase		: List[str]     ,				lowercase		: Any     ,				lowercase		: List[Any]     ,				lowercase		: Any						):





        """simple docstring"""




        lowercase_      :List[str]         =			MobileNetVaModel(config=lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :Optional[int]         =			model(lowercase						)
        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 							lowercase__				(			self		: List[str]     ,				lowercase		: Union[str, Any]     ,				lowercase		: Any     ,				lowercase		: Any     ,				lowercase		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Dict         =			self.num_labels
        lowercase_      :Tuple         =			MobileNetVaForImageClassification(lowercase						)
        model.to(lowercase						)
        model.eval()
        lowercase_      :List[str]         =			model(lowercase     ,				labels=lowercase						)
        self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.num_labels)						)







     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




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







@require_torch
class 				a_       (    _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 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :Dict         =			MobileNetVaModelTester(self						)
        lowercase_      :Union[str, Any]         =			MobileNetVaConfigTester(self     ,				config_class=lowercase     ,				has_text_modality=lowercase						)





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        self.config_tester.run_common_tests()





     @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"						)
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        pass





     @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"						)
     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        pass





     @unittest.skip(reason="MobileNetV1 does not output attentions"						)
     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        pass





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      ,							lowercase_      :Dict         =			self.model_tester.prepare_config_and_inputs_for_common()

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

           lowercase_      :Union[str, Any]         =			["pixel_values"]
           self.assertListEqual(arg_names[:1]     ,				lowercase						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        def check_hidden_states_output(lowercase		: Optional[Any]     ,				lowercase		: Tuple     ,				lowercase		: List[Any]						):
           lowercase_      :List[Any]         =			model_class(lowercase						)
           model.to(lowercase						)
           model.eval()

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

           lowercase_      :List[str]         =			outputs.hidden_states

           lowercase_      :str         =			26
           self.assertEqual(len(lowercase						)     ,				lowercase						)

        lowercase_      ,							lowercase_      :str         =			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(lowercase     ,				lowercase     ,				lowercase						)

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

           check_hidden_states_output(lowercase     ,				lowercase     ,				lowercase						)





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :List[Any]         =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase						)







     @slow
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase_      :int         =			MobileNetVaModel.from_pretrained(lowercase						)
           self.assertIsNotNone(lowercase						)







def 							UpperCAmelCase_  (						):
   lowercase_      :List[Any]         =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
   return image


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





     @cached_property
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        return (
            MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"						) if is_vision_available() else None
        )







     @slow
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :List[str]         =			MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"						).to(lowercase						)

        lowercase_      :List[Any]         =			self.default_image_processor
        lowercase_      :List[Any]         =			prepare_img()
        lowercase_      :Any         =			image_processor(images=lowercase     ,				return_tensors="pt"						).to(lowercase						)

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

        # verify the logits
        lowercase_      :str         =			torch.Size((1, 1_001)						)
        self.assertEqual(outputs.logits.shape     ,				lowercase						)

        lowercase_      :Tuple         =			torch.tensor([-4.17_39, -1.12_33, 3.12_05]						).to(lowercase						)

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









'''simple docstring'''


def 							UpperCAmelCase_  (						__lowerCamelCase : int | float | str			):
   try:
      lowercase_      :Optional[int]         =			float(__lowerCamelCase			)
   except ValueError:
      raise ValueError("Please enter a valid number"			)
   lowercase_      :Dict         =			decimal - int(__lowerCamelCase			)
   if fractional_part == 0:
      return int(__lowerCamelCase			), 1
   else:
      lowercase_      :Tuple         =			len(str(__lowerCamelCase			).split("."			)[1]			)
      lowercase_      :Optional[Any]         =			int(decimal * (10**number_of_frac_digits)			)
      lowercase_      :Dict         =			10**number_of_frac_digits
      lowercase_      ,							lowercase_      :Optional[int]         =			denominator, numerator
      while True:
         lowercase_      :Any         =			dividend % divisor
         if remainder == 0:
            break
         lowercase_      ,							lowercase_      :Optional[int]         =			divisor, remainder
      lowercase_      ,							lowercase_      :Optional[int]         =			numerator / divisor, denominator / divisor
      return int(__lowerCamelCase			), int(__lowerCamelCase			)


if __name__ == "__main__":
 print(F'''{decimal_to_fraction(2) = }''')
 print(F'''{decimal_to_fraction(89.0) = }''')
 print(F'''{decimal_to_fraction('67') = }''')
 print(F'''{decimal_to_fraction('45.0') = }''')
 print(F'''{decimal_to_fraction(1.5) = }''')
 print(F'''{decimal_to_fraction('6.25') = }''')
 print(F'''{decimal_to_fraction('78td') = }''')


