






"""simple docstring"""





import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
  from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class 					a						:






    def __init__(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							List[str]=13	,						__SCREAMING_SNAKE_CASE					:							Tuple=32	,						__SCREAMING_SNAKE_CASE					:							Any=3	,						__SCREAMING_SNAKE_CASE					:							Tuple=[3, 3, 5]	,						__SCREAMING_SNAKE_CASE					:							Optional[int]=[32, 16, 24]	,						__SCREAMING_SNAKE_CASE					:							str=[16, 24, 40]	,						__SCREAMING_SNAKE_CASE					:							int=[1, 1, 2]	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]=[1, 1, 2]	,						__SCREAMING_SNAKE_CASE					:							int=[1, 6, 6]	,						__SCREAMING_SNAKE_CASE					:							List[str]=True	,						__SCREAMING_SNAKE_CASE					:							int=True	,						__SCREAMING_SNAKE_CASE					:							int=37	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]="gelu"	,						__SCREAMING_SNAKE_CASE					:							Any=10	,						)					->		Any:
      lowerCamelCase_   					=						parent
      lowerCamelCase_   					=						batch_size
      lowerCamelCase_   					=						image_size
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						kernel_sizes
      lowerCamelCase_   					=						in_channels
      lowerCamelCase_   					=						out_channels
      lowerCamelCase_   					=						strides
      lowerCamelCase_   					=						num_block_repeats
      lowerCamelCase_   					=						expand_ratios
      lowerCamelCase_   					=						is_training
      lowerCamelCase_   					=						hidden_act
      lowerCamelCase_   					=						num_labels
      lowerCamelCase_   					=						use_labels






    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      lowerCamelCase_   					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

      lowerCamelCase_   					=						self.get_config()
      return config, pixel_values, labels






    def        UpperCamelCase	(	self					:							int )					->		str:
      return EfficientNetConfig(
          num_channels=self.num_channels	,						kernel_sizes=self.kernel_sizes	,						in_channels=self.in_channels	,						out_channels=self.out_channels	,						strides=self.strides	,						num_block_repeats=self.num_block_repeats	,						expand_ratios=self.expand_ratios	,						hidden_act=self.hidden_act	,						num_labels=self.num_labels	,						)






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							List[Any] )					->		Optional[int]:
      lowerCamelCase_   					=						EfficientNetModel(config=__SCREAMING_SNAKE_CASE )
      model.to(__SCREAMING_SNAKE_CASE )
      model.eval()
      lowerCamelCase_   					=						model(__SCREAMING_SNAKE_CASE )
      # expected last hidden states: B, C, H // 4, W // 4
      self.parent.assertEqual(
          result.last_hidden_state.shape	,						(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)	,						)






    def        UpperCamelCase	(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							str	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		List[str]:
      lowerCamelCase_   					=						EfficientNetForImageClassification(__SCREAMING_SNAKE_CASE )
      model.to(__SCREAMING_SNAKE_CASE )
      model.eval()
      lowerCamelCase_   					=						model(__SCREAMING_SNAKE_CASE	,						labels=__SCREAMING_SNAKE_CASE )
      self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.num_labels) )






    def        UpperCamelCase	(	self					:							List[str] )					->		str:
      lowerCamelCase_   					=						self.prepare_config_and_inputs()
      lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						config_and_inputs
      lowerCamelCase_   					=						{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 					a						( __snake_case  ,				__snake_case  ,				unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:					Tuple						=			(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE						:					List[str]						=			(
        {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE						:					Any						=			False
    SCREAMING_SNAKE_CASE						:					Union[str, Any]						=			False
    SCREAMING_SNAKE_CASE						:					int						=			False
    SCREAMING_SNAKE_CASE						:					Tuple						=			False
    SCREAMING_SNAKE_CASE						:					int						=			False






    def        UpperCamelCase	(	self					:							int )					->		Any:
      lowerCamelCase_   					=						EfficientNetModelTester(self )
      lowerCamelCase_   					=						ConfigTester(
          self	,						config_class=__SCREAMING_SNAKE_CASE	,						has_text_modality=__SCREAMING_SNAKE_CASE	,						hidden_size=37 )






    def        UpperCamelCase	(	self					:							Dict )					->		Dict:
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		int:
      return






    @unittest.skip(reason='EfficientNet does not use inputs_embeds' )
    def        UpperCamelCase	(	self					:							Tuple )					->		int:
      pass






    @unittest.skip(reason='EfficientNet does not support input and output embeddings' )
    def        UpperCamelCase	(	self					:							Optional[Any] )					->		int:
      pass






    @unittest.skip(reason='EfficientNet does not use feedforward chunking' )
    def        UpperCamelCase	(	self					:							Tuple )					->		Any:
      pass






    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      lowerCamelCase_					,     lowerCamelCase_   					=						self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCamelCase_   					=						['pixel_values']
        self.assertListEqual(arg_names[:1]	,						__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      lowerCamelCase_   					=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		Any:
      def check_hidden_states_output(__SCREAMING_SNAKE_CASE					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Optional[Any] ):
        lowerCamelCase_   					=						model_class(__SCREAMING_SNAKE_CASE )
        model.to(__SCREAMING_SNAKE_CASE )
        model.eval()

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

        lowerCamelCase_   					=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
        lowerCamelCase_   					=						sum(config.num_block_repeats ) * 4
        self.assertEqual(len(__SCREAMING_SNAKE_CASE )	,						__SCREAMING_SNAKE_CASE )

        # EfficientNet'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 // 2, self.model_tester.image_size // 2]	,						)

      lowerCamelCase_					,     lowerCamelCase_   					=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_   					=						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"]
        lowerCamelCase_   					=						True

        check_hidden_states_output(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Dict )					->		Optional[int]:
      lowerCamelCase_   					=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def        UpperCamelCase	(	self					:							str )					->		Union[str, Any]:
      pass






    @slow
    def        UpperCamelCase	(	self					:							Any )					->		Optional[int]:
      for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_   					=						EfficientNetModel.from_pretrained(__SCREAMING_SNAKE_CASE )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE )






    @is_pipeline_test
    @require_vision
    @slow
    def        UpperCamelCase	(	self					:							int )					->		int:
      super().test_pipeline_image_classification()







def 					lowerCamelCase__	(							)				->    List[Any]:
  lowerCamelCase_   					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
  return image






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






    @cached_property
    def        UpperCamelCase	(	self					:							Dict )					->		Dict:
      return AutoImageProcessor.from_pretrained('google/efficientnet-b7' ) if is_vision_available() else None






    @slow
    def        UpperCamelCase	(	self					:							int )					->		Union[str, Any]:
      lowerCamelCase_   					=						EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7' ).to(__SCREAMING_SNAKE_CASE )

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

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

      # verify the logits
      lowerCamelCase_   					=						torch.Size((1, 1000) )
      self.assertEqual(outputs.logits.shape	,						__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						torch.tensor([0.0_001, 0.0_002, 0.0_002] ).to(__SCREAMING_SNAKE_CASE )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]	,						__SCREAMING_SNAKE_CASE	,						atol=1e-4 ) )












"""simple docstring"""





from __future__ import annotations

from random import randrange







def 					lowerCamelCase__	(							_lowerCamelCase	:		list			)				->    list:
  if len(_lowerCamelCase			) < 2:
    return collection
  lowerCamelCase_   					=						randrange(len(_lowerCamelCase			)			)  # Use random element as pivot
  lowerCamelCase_   					=						collection[pivot_index]
  lowerCamelCase_   					=						[]  # All elements greater than pivot
  lowerCamelCase_   					=						[]  # All elements less than or equal to pivot

  for element in collection[:pivot_index]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  for element in collection[pivot_index + 1 :]:
    (greater if element > pivot else lesser).append(_lowerCamelCase			)

  return [*quick_sort(_lowerCamelCase			), pivot, *quick_sort(_lowerCamelCase			)]


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Dict    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE :    List[str]    =						[int(item) for item in user_input.split(''',''')]
  print(quick_sort(unsorted))




