

def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  len(_SCREAMING_SNAKE_CASE    )
    for _ in range(
        0	,				int(((no_of_elements - 1) / 2) + 1    )    ):  # we don't need to traverse to end of list as
        for j in range(0	,				no_of_elements - 1    ):
            if (
                lst[j + 1] < lst[j]
            ):  # applying bubble sort algorithm from left to right (or forwards)
                SCREAMING_SNAKE_CASE_													=  lst[j + 1]
                SCREAMING_SNAKE_CASE_													=  lst[j]
                SCREAMING_SNAKE_CASE_													=  temp
            if (
                lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
            ):  # applying bubble sort algorithm from right to left (or backwards)
                SCREAMING_SNAKE_CASE_													=  lst[no_of_elements - 1 - j]
                SCREAMING_SNAKE_CASE_													=  lst[no_of_elements - 2 - j]
                SCREAMING_SNAKE_CASE_													=  temp
    return lst


if __name__ == "__main__":
       print("enter the list to be sorted")
       UpperCamelCase__					: Union[str, Any]								=      [int(x) for x in input().split()]  # inputing elements of the list in one line
       UpperCamelCase__					: Dict								=      double_sort(lst)
       print("the sorted list is")
       print(sorted_lst)





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor



class  __snake_case					:

  def __init__(   self  ,						_A  ,						_A=13  ,						_A=32  ,						_A=3  ,						_A=4  ,						_A=[10, 20, 30, 40]  ,						_A=[2, 2, 3, 2]  ,						_A=True  ,						_A=True  ,						_A=37  ,						_A="gelu"  ,						_A=10  ,						_A=0.0_2  ,						_A=3  ,						_A=None  ,						):
      SCREAMING_SNAKE_CASE_													=  parent
      SCREAMING_SNAKE_CASE_													=  batch_size
      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  num_stages
      SCREAMING_SNAKE_CASE_													=  hidden_sizes
      SCREAMING_SNAKE_CASE_													=  depths
      SCREAMING_SNAKE_CASE_													=  is_training
      SCREAMING_SNAKE_CASE_													=  use_labels
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  scope

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      SCREAMING_SNAKE_CASE_													=  None
      if self.use_labels:
          SCREAMING_SNAKE_CASE_													=  ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)

      SCREAMING_SNAKE_CASE_													=  self.get_config()

      return config, pixel_values, labels

  def   lowerCAmelCase__       (   self):
      return ConvNextConfig(
          num_channels=self.num_channels  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						num_stages=self.num_stages  ,						hidden_act=self.hidden_act  ,						is_decoder=_A  ,						initializer_range=self.initializer_range  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel(config=_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						training=_A)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.type_sequence_label_size
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification(_A)
      SCREAMING_SNAKE_CASE_													=  model(_A  ,						labels=_A  ,						training=_A)
      self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size))


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  config_and_inputs
      SCREAMING_SNAKE_CASE_													=  {'pixel_values': pixel_values}
      return config, inputs_dict






@require_tf
class  __snake_case					(      lowerCAmelCase__       ,	lowerCAmelCase__       ,	unittest.TestCase    ):
  __lowerCAmelCase       :		str  	=					(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  __lowerCAmelCase       :		List[str]  	=					(
      {'feature-extraction': TFConvNextModel, 'image-classification': TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowerCAmelCase       :		Union[str, Any]  	=					False
  __lowerCAmelCase       :		Dict  	=					False
  __lowerCAmelCase       :		Optional[int]  	=					False
  __lowerCAmelCase       :		Optional[Any]  	=					False
  __lowerCAmelCase       :		List[Any]  	=					False

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModelTester(self)
      SCREAMING_SNAKE_CASE_													=  ConfigTester(
          self  ,						config_class=_A  ,						has_text_modality=_A  ,						hidden_size=37  ,						)

  @unittest.skip(reason='ConvNext does not use inputs_embeds')
  def   lowerCAmelCase__       (   self):
      pass

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0  ,						reason='TF does not support backprop for grouped convolutions on CPU.'  ,						)
  @slow
  def   lowerCAmelCase__       (   self):
      super().test_keras_fit()

  @unittest.skip(reason='ConvNext does not support input and output embeddings')
  def   lowerCAmelCase__       (   self):
      pass

  def   lowerCAmelCase__       (   self):
      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(_A)
          SCREAMING_SNAKE_CASE_													=  inspect.signature(model.call)
          # 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]  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)

  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU')) == 0  ,						reason='TF does not support backprop for grouped convolutions on CPU.'  ,						)
  def   lowerCAmelCase__       (   self):
      super().test_dataset_conversion()

  def   lowerCAmelCase__       (   self):
      def check_hidden_states_output(_A  ,						_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  model(**self._prepare_for_class(_A  ,						_A))
          SCREAMING_SNAKE_CASE_													=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          SCREAMING_SNAKE_CASE_													=  self.model_tester.num_stages
          self.assertEqual(len(_A)  ,						expected_num_stages + 1)

          # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:])  ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,						)

      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(_A  ,						_A  ,						_A)

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

          check_hidden_states_output(_A  ,						_A  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(_A  ,						_A  ,						_A  ,						_A={}):
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A)
          SCREAMING_SNAKE_CASE_													=  model(_A  ,						return_dict=_A  ,						**_A).to_tuple()

          def recursive_check(_A  ,						_A):
              if isinstance(_A  ,						(List, Tuple)):
                  for tuple_iterable_value, dict_iterable_value in zip(_A  ,						_A):
                      recursive_check(_A  ,						_A)
              elif tuple_object is None:
                  return
              else:
                  self.assertTrue(
                      all(tf.equal(_A  ,						_A))  ,						msg=(
                          'Tuple and dict output are not equal. Difference:'
                          f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"""
                      )  ,						)

              recursive_check(_A  ,						_A)

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_													=  model_class(_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          SCREAMING_SNAKE_CASE_													=  self._prepare_for_class(_A  ,						_A  ,						return_labels=_A)
          check_equivalence(_A  ,						_A  ,						_A  ,						{'output_hidden_states': True})

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_A)


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextModel.from_pretrained('facebook/convnext-tiny-224')
      self.assertIsNotNone(_A)






def    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
    return image





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

  @cached_property
  def   lowerCAmelCase__       (   self):
      return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224') if is_vision_available() else None


  @slow
  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224')

      SCREAMING_SNAKE_CASE_													=  self.default_image_processor
      SCREAMING_SNAKE_CASE_													=  prepare_img()
      SCREAMING_SNAKE_CASE_													=  image_processor(images=_A  ,						return_tensors='tf')

      # forward pass
      SCREAMING_SNAKE_CASE_													=  model(**_A)

      # verify the logits
      SCREAMING_SNAKE_CASE_													=  tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape  ,						_A)

      SCREAMING_SNAKE_CASE_													=  tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1])

      tf.debugging.assert_near(outputs.logits[0, :3]  ,						_A  ,						atol=1E-4)


