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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(_UpperCamelCase    ,  """tf_padding"""))
          self.parent.assertTrue(hasattr(_UpperCamelCase    ,  """depth_multiplier"""))





class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Dict=13    ,  _UpperCamelCase     :				Optional[Any]=3    ,  _UpperCamelCase     :				int=32    ,  _UpperCamelCase     :				Any=0.2_5    ,  _UpperCamelCase     :				str=8    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				List[Any]=1024    ,  _UpperCamelCase     :				Optional[Any]=32    ,  _UpperCamelCase     :				Tuple="relu6"    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				Optional[int]=0.0_2    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				List[str]=10    ,  _UpperCamelCase     :				Optional[int]=None    ,  )				->List[Any]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


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

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

          _lowerCamelCase     :		List[str] 						=						self.get_config()

          return config, pixel_values, labels, pixel_labels



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Optional[Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						MobileNetVaModel(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)
          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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.num_labels
          _lowerCamelCase     :		int 						=						MobileNetVaForImageClassification(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase    ,  labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.prepare_config_and_inputs()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						config_and_inputs
          _lowerCamelCase     :		Any 						=						{"""pixel_values""": pixel_values}
          return config, inputs_dict





@require_torch
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
      _snake_case    		=       (
          {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
          if is_torch_available()
          else {}
      )

      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						MobileNetVaModelTester(self)
          _lowerCamelCase     :		List[Any] 						=						MobileNetVaConfigTester(self    ,  config_class=_UpperCamelCase    ,  has_text_modality=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          self.config_tester.run_common_tests()



      @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          pass



      @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Dict:


          """simple docstring"""


          pass



      @unittest.skip(reason="""MobileNetV1 does not output attentions""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Any:


          """simple docstring"""


          pass



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->List[str]:


          """simple docstring"""


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

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

              _lowerCamelCase     :		Dict 						=						["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[str]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          def check_hidden_states_output(_UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				List[Any]):
              _lowerCamelCase     :		Optional[int] 						=						model_class(_UpperCamelCase)
              model.to(_UpperCamelCase)
              model.eval()

              with torch.no_grad():
                  _lowerCamelCase     :		Optional[Any] 						=						model(**self._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase))

              _lowerCamelCase     :		Dict 						=						outputs.hidden_states

              _lowerCamelCase     :		Any 						=						26
              self.assertEqual(len(_UpperCamelCase)    ,  _UpperCamelCase)

          _lowerCamelCase						,							_lowerCamelCase     :		List[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              _lowerCamelCase     :		int 						=						True
              check_hidden_states_output(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _lowerCamelCase     :		List[str] 						=						True

              check_hidden_states_output(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase)




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase     :		Optional[int] 						=						MobileNetVaModel.from_pretrained(_UpperCamelCase)
              self.assertIsNotNone(_UpperCamelCase)







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
    return image




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





      '''simple docstring'''






      @cached_property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[int]:


          """simple docstring"""


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




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224""").to(_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						self.default_image_processor
          _lowerCamelCase     :		str 						=						prepare_img()
          _lowerCamelCase     :		Optional[int] 						=						image_processor(images=_UpperCamelCase    ,  return_tensors="""pt""").to(_UpperCamelCase)

          # forward pass
          with torch.no_grad():
              _lowerCamelCase     :		Optional[Any] 						=						model(**_UpperCamelCase)

          # verify the logits
          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 1001))
          self.assertEqual(outputs.logits.shape    ,  _UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]).to(_UpperCamelCase)

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





lowerCAmelCase			:							Tuple     =0  # The first color of the flag.
lowerCAmelCase			:							Union[str, Any]     =1  # The second color of the flag.
lowerCAmelCase			:							Any     =2  # The third color of the flag.
lowerCAmelCase			:							List[str]     =(red, white, blue)







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase			:							List[str]     =input("Enter numbers separated by commas:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item.strip()) for item in user_input.split(",")]
    print(F"""{dutch_national_flag_sort(unsorted)}""")




