



"""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, MobileNetVaForSemanticSegmentation, MobileNetVaModel
 from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Dict														=       self.config_class(**self.inputs_dict		)
           self.parent.assertTrue(hasattr(A	,      "tf_padding"		)		)
           self.parent.assertTrue(hasattr(A	,      "depth_multiplier"		)		)







class       UpperCAmelCase_    :



      def __init__(	self	:      Optional[int]	,      A	:      int	,      A	:      Any=1_3	,      A	:      Dict=3	,      A	:      List[str]=3_2	,      A	:      Tuple=0.25	,      A	:      Any=8	,      A	:      Union[str, Any]=8	,      A	:      Optional[int]=6	,      A	:      Optional[Any]=3_2	,      A	:      List[str]=True	,      A	:      Tuple=True	,      A	:      Dict=True	,      A	:      str="relu6"	,      A	:      Union[str, Any]=1_2_8_0	,      A	:      Any=0.1	,      A	:      Dict=0.02	,      A	:      Any=True	,      A	:      List[str]=True	,      A	:      Union[str, Any]=1_0	,      A	:      List[str]=None	,      ):
           _UpperCAmelCase     :      Optional[Any]														=       parent
           _UpperCAmelCase     :      Dict														=       batch_size
           _UpperCAmelCase     :      Optional[int]														=       num_channels
           _UpperCAmelCase     :      Optional[int]														=       image_size
           _UpperCAmelCase     :      Union[str, Any]														=       depth_multiplier
           _UpperCAmelCase     :      List[str]														=       depth_divisible_by
           _UpperCAmelCase     :      List[str]														=       min_depth
           _UpperCAmelCase     :      Any														=       expand_ratio
           _UpperCAmelCase     :      str														=       tf_padding
           _UpperCAmelCase     :      Tuple														=       output_stride
           _UpperCAmelCase     :      List[str]														=       first_layer_is_expansion
           _UpperCAmelCase     :      Union[str, Any]														=       finegrained_output
           _UpperCAmelCase     :      int														=       hidden_act
           _UpperCAmelCase     :      str														=       last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
           _UpperCAmelCase     :      int														=       classifier_dropout_prob
           _UpperCAmelCase     :      Optional[Any]														=       use_labels
           _UpperCAmelCase     :      Union[str, Any]														=       is_training
           _UpperCAmelCase     :      Any														=       num_labels
           _UpperCAmelCase     :      Union[str, Any]														=       initializer_range
           _UpperCAmelCase     :      Optional[Any]														=       scope



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

           _UpperCAmelCase     :      int														=       None
           _UpperCAmelCase     :      Tuple														=       None
           if self.use_labels:
                _UpperCAmelCase     :      List[Any]														=       ids_tensor([self.batch_size]	,      self.num_labels		)
                _UpperCAmelCase     :      int														=       ids_tensor([self.batch_size, self.image_size, self.image_size]	,      self.num_labels		)

           _UpperCAmelCase     :      Optional[int]														=       self.get_config()

           return config, pixel_values, labels, pixel_labels



      def 		snake_case_   (	self	:      int		):
           return MobileNetVaConfig(
               num_channels=self.num_channels	,      image_size=self.image_size	,      depth_multiplier=self.depth_multiplier	,      depth_divisible_by=self.depth_divisible_by	,      min_depth=self.min_depth	,      expand_ratio=self.expand_ratio	,      output_stride=self.output_stride	,      first_layer_is_expansion=self.first_layer_is_expansion	,      finegrained_output=self.finegrained_output	,      hidden_act=self.hidden_act	,      tf_padding=self.tf_padding	,      classifier_dropout_prob=self.classifier_dropout_prob	,      initializer_range=self.initializer_range	,      )



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      List[str]	,      A	:      str	,      A	:      str	,      A	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       MobileNetVaModel(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      int														=       model(A		)
           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,
               )	,      )
           self.parent.assertEqual(
               result.pooler_output.shape	,      (self.batch_size, self.last_hidden_size)	,      )



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      Optional[Any]	,      A	:      Any	,      A	:      Any	,      A	:      Any		):
           _UpperCAmelCase     :      str														=       self.num_labels
           _UpperCAmelCase     :      List[str]														=       MobileNetVaForImageClassification(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      str														=       model(A	,      labels=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)		)



      def 		snake_case_   (	self	:      Tuple	,      A	:      Any	,      A	:      Optional[int]	,      A	:      Dict	,      A	:      List[Any]		):
           _UpperCAmelCase     :      Dict														=       self.num_labels
           _UpperCAmelCase     :      List[Any]														=       MobileNetVaForSemanticSegmentation(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      int														=       model(A		)
           self.parent.assertEqual(
               result.logits.shape	,      (
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )	,      )
           _UpperCAmelCase     :      Dict														=       model(A	,      labels=A		)
           self.parent.assertEqual(
               result.logits.shape	,      (
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )	,      )



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[str]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       config_and_inputs
           _UpperCAmelCase     :      Dict														=       {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					(
          (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:  int						=					(
          {
              'feature-extraction': MobileNetVaModel,
              'image-classification': MobileNetVaForImageClassification,
              'image-segmentation': MobileNetVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

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



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Dict														=       MobileNetVaModelTester(self		)
           _UpperCAmelCase     :      List[str]														=       MobileNetVaConfigTester(self	,      config_class=A	,      has_text_modality=A		)



      def 		snake_case_   (	self	:      List[str]		):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="MobileNetV2 does not use inputs_embeds"		)
      def 		snake_case_   (	self	:      List[Any]		):
           pass



      @unittest.skip(reason="MobileNetV2 does not support input and output embeddings"		)
      def 		snake_case_   (	self	:      Optional[Any]		):
           pass



      @unittest.skip(reason="MobileNetV2 does not output attentions"		)
      def 		snake_case_   (	self	:      List[Any]		):
           pass



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase     :      Any														=       ["pixel_values"]
                self.assertListEqual(arg_names[:1]	,      A		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      Tuple		):
           def check_hidden_states_output(A	:      int	,      A	:      Optional[Any]	,      A	:      Optional[int]		):
                _UpperCAmelCase     :      Optional[int]														=       model_class(A		)
                model.to(A		)
                model.eval()

                with torch.no_grad():
                     _UpperCAmelCase     :      List[Any]														=       model(**self._prepare_for_class(A	,      A		)		)

                _UpperCAmelCase     :      str														=       outputs.hidden_states

                _UpperCAmelCase     :      Tuple														=       1_6
                self.assertEqual(len(A		)	,      A		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase     :      Tuple														=       True
                check_hidden_states_output(A	,      A	,      A		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase     :      Union[str, Any]														=       True

                check_hidden_states_output(A	,      A	,      A		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*A		)



      @slow
      def 		snake_case_   (	self	:      int		):
           for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      List[str]														=       MobileNetVaModel.from_pretrained(A		)
                self.assertIsNotNone(A		)







def 		__snake_case		( )    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image






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



      @cached_property
      def 		snake_case_   (	self	:      Dict		):
           return (
               MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"		) if is_vision_available() else None
           )



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Dict														=       MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"		).to(A		)

           _UpperCAmelCase     :      int														=       self.default_image_processor
           _UpperCAmelCase     :      Any														=       prepare_img()
           _UpperCAmelCase     :      List[Any]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase     :      int														=       model(**A		)

           # verify the logits
           _UpperCAmelCase     :      Optional[int]														=       torch.Size((1, 1_0_0_1)		)
           self.assertEqual(outputs.logits.shape	,      A		)

           _UpperCAmelCase     :      Any														=       torch.tensor([0.2_445, -1.1_993, 0.1_905]		).to(A		)

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



      @slow
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[Any]														=       MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)
           _UpperCAmelCase     :      int														=       model.to(A		)

           _UpperCAmelCase     :      int														=       MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)

           _UpperCAmelCase     :      Union[str, Any]														=       prepare_img()
           _UpperCAmelCase     :      Dict														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase     :      int														=       model(**A		)
           _UpperCAmelCase     :      List[str]														=       outputs.logits

           # verify the logits
           _UpperCAmelCase     :      Optional[int]														=       torch.Size((1, 2_1, 6_5, 6_5)		)
           self.assertEqual(logits.shape	,      A		)

           _UpperCAmelCase     :      Optional[int]														=       torch.tensor(
               [
                   [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                   [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                   [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
               ]	,      device=A	,      )

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]	,      A	,      atol=1e-4		)		)





"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


if __name__ == "__main__":
 _lowerCAmelCase							:   Any				=				input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase							:   int				=				[int(item) for item in user_input.split(",")]
 print(patience_sort(unsorted))
