

'''simple docstring'''



import random







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = a[left_index]
     __SCREAMING_SNAKE_CASE      = left_index + 1
     for j in range(left_index + 1 ,       a__		):
          if a[j] < pivot:
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = a[i], a[j]
               i += 1
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = a[i - 1], a[left_index]
     return i - 1







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     if left < right:
          __SCREAMING_SNAKE_CASE      = random.randint(a__ ,       right - 1		)
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = (
              a[left],
              a[pivot],
          )  # switches the pivot with the left most bound
          __SCREAMING_SNAKE_CASE      = partition(a__ ,       a__ ,       a__		)
          quick_sort_random(
              a__ ,       a__ ,       a__		)  # recursive quicksort to the left of the pivot point
          quick_sort_random(
              a__ ,       pivot_index + 1 ,       a__		)  # recursive quicksort to the right of the pivot point







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = input("""Enter numbers separated by a comma:\n"""		).strip()
     __SCREAMING_SNAKE_CASE      = [int(a__		) for item in user_input.split(""","""		)]

     quick_sort_random(a__ ,       0 ,       len(a__		)		)

     print(a__		)


if __name__ == "__main__":
  main()









'''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 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.config_class(**self.inputs_dict     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""tf_padding"""     )     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""depth_multiplier"""     )     )







class 		lowerCAmelCase__  :



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = num_channels
      __SCREAMING_SNAKE_CASE      = image_size
      __SCREAMING_SNAKE_CASE      = depth_multiplier
      __SCREAMING_SNAKE_CASE      = min_depth
      __SCREAMING_SNAKE_CASE      = tf_padding
      __SCREAMING_SNAKE_CASE      = int(last_hidden_size * depth_multiplier     )
      __SCREAMING_SNAKE_CASE      = output_stride
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = classifier_dropout_prob
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = scope
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

      __SCREAMING_SNAKE_CASE      = self.get_config()

      return config, pixel_values, labels, pixel_labels
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Tuple:


      """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   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModel(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE     )
      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   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.num_labels
      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				labels=__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )


 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




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







@require_torch
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



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

 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			str     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModelTester(self     )
      __SCREAMING_SNAKE_CASE      = MobileNetVaConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE   ,				has_text_modality=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

           __SCREAMING_SNAKE_CASE      = ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[Any]:


      """simple docstring"""




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

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

           __SCREAMING_SNAKE_CASE      = outputs.hidden_states

           __SCREAMING_SNAKE_CASE      = 26
           self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

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

           check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE     )


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


      """simple docstring"""




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







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image







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



 """simple docstring"""



 @cached_property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




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


 @slow
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self.default_image_processor
      __SCREAMING_SNAKE_CASE      = prepare_img()
      __SCREAMING_SNAKE_CASE      = image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""     ).to(__SCREAMING_SNAKE_CASE     )

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

      # verify the logits
      __SCREAMING_SNAKE_CASE      = torch.Size((1, 1_001)     )
      self.assertEqual(outputs.logits.shape   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(__SCREAMING_SNAKE_CASE     )

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






