



"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  _snake_case   	=      prime_factors(lowerCAmelCase_	)
  if is_square_free(lowerCAmelCase_	):
    return -1 if len(lowerCAmelCase_	) % 2 else 1
  return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""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     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.config_class(**self.inputs_dict				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''tf_padding'''				)				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''depth_multiplier'''				)				)



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Tuple=1_3							,       __lowerCamelCase    :	List[Any]=3							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Dict=0.2_5							,       __lowerCamelCase    :	Optional[Any]=8							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[Any]=1_0_2_4							,       __lowerCamelCase    :	Union[str, Any]=3_2							,       __lowerCamelCase    :	int="relu6"							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	Tuple=0.0_2							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Union[str, Any]=1_0							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      num_channels
     _snake_case   	=      image_size
     _snake_case   	=      depth_multiplier
     _snake_case   	=      min_depth
     _snake_case   	=      tf_padding
     _snake_case   	=      int(last_hidden_size * depth_multiplier				)
     _snake_case   	=      output_stride
     _snake_case   	=      hidden_act
     _snake_case   	=      classifier_dropout_prob
     _snake_case   	=      use_labels
     _snake_case   	=      is_training
     _snake_case   	=      num_labels
     _snake_case   	=      initializer_range
     _snake_case   	=      scope



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels, pixel_labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """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    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.num_labels
     _snake_case   	=      MobileNetVaForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)				)





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	List[Any]           =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
   A__		:	Optional[int]           =  (
       {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__		:	List[str]           =  False
   A__		:	Tuple           =  False
   A__		:	List[Any]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModelTester(self				)
     _snake_case   	=      MobileNetVaConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not output attentions'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



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



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     def check_hidden_states_output(__lowerCamelCase    :	Any							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]				):
       _snake_case   	=      model_class(__lowerCamelCase				)
       model.to(__lowerCamelCase				)
       model.eval()

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

       _snake_case   	=      outputs.hidden_states

       _snake_case   	=      2_6
       self.assertEqual(len(__lowerCamelCase				)							,       __lowerCamelCase				)

     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      MobileNetVaModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				int:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







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



   @cached_property
   def    __UpperCAmelCase    (       self    :	List[str]				):



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





   @slow
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

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

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_0_0_1)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				).to(__lowerCamelCase				)

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





