



"""simple docstring"""




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.config_class(**self.inputs_dict		)
           self.parent.assertTrue(hasattr(A	,      "hidden_sizes"		)		)
           self.parent.assertTrue(hasattr(A	,      "num_attention_heads"		)		)







class       UpperCAmelCase_    :



      def __init__(	self	:      Union[str, Any]	,      A	:      Optional[Any]	,      A	:      List[str]=1_3	,      A	:      List[str]=6_4	,      A	:      Union[str, Any]=3	,      A	:      List[str]=3	,      A	:      Dict=2	,      A	:      List[Any]=1	,      A	:      Union[str, Any]=1_6	,      A	:      Dict=[1_2_8, 2_5_6, 3_8_4]	,      A	:      List[str]=[4, 6, 8]	,      A	:      Any=[2, 3, 4]	,      A	:      str=[1_6, 1_6, 1_6]	,      A	:      Optional[int]=0	,      A	:      List[Any]=[2, 2, 2]	,      A	:      Any=[2, 2, 2]	,      A	:      Union[str, Any]=0.02	,      A	:      Union[str, Any]=True	,      A	:      Dict=True	,      A	:      List[Any]=2	,      ):
           _UpperCAmelCase     :      int														=       parent
           _UpperCAmelCase     :      List[Any]														=       batch_size
           _UpperCAmelCase     :      Optional[int]														=       image_size
           _UpperCAmelCase     :      Tuple														=       num_channels
           _UpperCAmelCase     :      Optional[Any]														=       kernel_size
           _UpperCAmelCase     :      int														=       stride
           _UpperCAmelCase     :      Tuple														=       padding
           _UpperCAmelCase     :      str														=       hidden_sizes
           _UpperCAmelCase     :      int														=       num_attention_heads
           _UpperCAmelCase     :      Optional[Any]														=       depths
           _UpperCAmelCase     :      Any														=       key_dim
           _UpperCAmelCase     :      Dict														=       drop_path_rate
           _UpperCAmelCase     :      Optional[int]														=       patch_size
           _UpperCAmelCase     :      Optional[Any]														=       attention_ratio
           _UpperCAmelCase     :      Optional[int]														=       mlp_ratio
           _UpperCAmelCase     :      int														=       initializer_range
           _UpperCAmelCase     :      List[str]														=       [
               ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
               ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
           ]
           _UpperCAmelCase     :      Optional[Any]														=       is_training
           _UpperCAmelCase     :      Any														=       use_labels
           _UpperCAmelCase     :      Any														=       num_labels
           _UpperCAmelCase     :      Optional[Any]														=       initializer_range



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

           _UpperCAmelCase     :      Union[str, Any]														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Tuple														=       ids_tensor([self.batch_size]	,      self.num_labels		)

           _UpperCAmelCase     :      List[str]														=       self.get_config()
           return config, pixel_values, labels



      def 		snake_case_   (	self	:      List[str]		):
           return LevitConfig(
               image_size=self.image_size	,      num_channels=self.num_channels	,      kernel_size=self.kernel_size	,      stride=self.stride	,      padding=self.padding	,      patch_size=self.patch_size	,      hidden_sizes=self.hidden_sizes	,      num_attention_heads=self.num_attention_heads	,      depths=self.depths	,      key_dim=self.key_dim	,      drop_path_rate=self.drop_path_rate	,      mlp_ratio=self.mlp_ratio	,      attention_ratio=self.attention_ratio	,      initializer_range=self.initializer_range	,      down_ops=self.down_ops	,      )



      def 		snake_case_   (	self	:      str	,      A	:      str	,      A	:      Tuple	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       LevitModel(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Tuple														=       model(A		)
           _UpperCAmelCase     :      Dict														=       (self.image_size, self.image_size)
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       image_size[0], image_size[1]
           for _ in range(4		):
                _UpperCAmelCase     :      Dict														=       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
                _UpperCAmelCase     :      Any														=       floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
           self.parent.assertEqual(
               result.last_hidden_state.shape	,      (self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])	,      )



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



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







@require_torch
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					(
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:  str						=					(
          {
              'feature-extraction': LevitModel,
              'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

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



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      str														=       LevitModelTester(self		)
           _UpperCAmelCase     :      Any														=       ConfigTester(self	,      config_class=A	,      has_text_modality=A	,      hidden_size=3_7		)



      def 		snake_case_   (	self	:      Tuple		):
           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 		snake_case_   (	self	:      Union[str, Any]		):
           return



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



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



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



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs_for_common()

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

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



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

                with torch.no_grad():
                     _UpperCAmelCase     :      Tuple														=       model(**self._prepare_for_class(A	,      A		)		)

                _UpperCAmelCase     :      int														=       outputs.hidden_states

                _UpperCAmelCase     :      Optional[Any]														=       len(self.model_tester.depths		) + 1
                self.assertEqual(len(A		)	,      A		)

                _UpperCAmelCase     :      int														=       (self.model_tester.image_size, self.model_tester.image_size)
                _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       image_size[0], image_size[1]
                for _ in range(4		):
                     _UpperCAmelCase     :      List[str]														=       floor(
                         (
                             (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                             / self.model_tester.stride
                         )
                         + 1		)
                     _UpperCAmelCase     :      Dict														=       floor(
                         (
                             (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                             / self.model_tester.stride
                         )
                         + 1		)
                # verify the first hidden states (first block)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]		)	,      [
                        height * width,
                        self.model_tester.hidden_sizes[0],
                    ]	,      )

           _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       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		)



      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
      def 		snake_case_   (	self	:      int		):
           pass



      def 		snake_case_   (	self	:      List[Any]	,      A	:      List[str]	,      A	:      Tuple	,      A	:      int=False		):
           _UpperCAmelCase     :      Tuple														=       super()._prepare_for_class(A	,      A	,      return_labels=A		)

           if return_labels:
                if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                     del inputs_dict["labels"]

           return inputs_dict



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



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



      def 		snake_case_   (	self	:      List[Any]		):
           if not self.model_tester.is_training:
                return

           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase     :      Optional[Any]														=       True

           for model_class in self.all_model_classes:
                # LevitForImageClassificationWithTeacher supports inference-only
                if (
                    model_class in get_values(A		)
                    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                ):
                     continue
                _UpperCAmelCase     :      List[str]														=       model_class(A		)
                model.to(A		)
                model.train()
                _UpperCAmelCase     :      Union[str, Any]														=       self._prepare_for_class(A	,      A	,      return_labels=A		)
                _UpperCAmelCase     :      Tuple														=       model(**A		).loss
                loss.backward()



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                return

           _UpperCAmelCase     :      Optional[int]														=       False
           _UpperCAmelCase     :      Union[str, Any]														=       True

           for model_class in self.all_model_classes:
                if model_class in get_values(A		) or not model_class.supports_gradient_checkpointing:
                     continue
                # LevitForImageClassificationWithTeacher supports inference-only
                if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                     continue
                _UpperCAmelCase     :      List[Any]														=       model_class(A		)
                model.gradient_checkpointing_enable()
                model.to(A		)
                model.train()
                _UpperCAmelCase     :      Tuple														=       self._prepare_for_class(A	,      A	,      return_labels=A		)
                _UpperCAmelCase     :      List[Any]														=       model(**A		).loss
                loss.backward()



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs_for_common()

           _UpperCAmelCase     :      str														=       [
               {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
               {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
               {"title": "regression", "num_labels": 1, "dtype": torch.float},
           ]

           for model_class in self.all_model_classes:
                if (
                    model_class
                    not in [
                        *get_values(A		),
                    ]
                    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                ):
                     continue

                for problem_type in problem_types:
                     with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'		):
                          _UpperCAmelCase     :      List[Any]														=       problem_type["title"]
                          _UpperCAmelCase     :      Dict														=       problem_type["num_labels"]

                          _UpperCAmelCase     :      Optional[int]														=       model_class(A		)
                          model.to(A		)
                          model.train()

                          _UpperCAmelCase     :      List[Any]														=       self._prepare_for_class(A	,      A	,      return_labels=A		)

                          if problem_type["num_labels"] > 1:
                               _UpperCAmelCase     :      Tuple														=       inputs["labels"].unsqueeze(1		).repeat(1	,      problem_type["num_labels"]		)

                          _UpperCAmelCase     :      int														=       inputs["labels"].to(problem_type["dtype"]		)

                          # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                          # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                          # they have the same size." which is a symptom something in wrong for the regression problem.
                          # See https://github.com/huggingface/transformers/issues/11780
                          with warnings.catch_warnings(record=A		) as warning_list:
                               _UpperCAmelCase     :      Dict														=       model(**A		).loss
                          for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message		):
                                    raise ValueError(
                                        f'Something is going wrong in the regression problem: intercepted {w.message}'		)

                          loss.backward()



      @slow
      def 		snake_case_   (	self	:      List[Any]		):
           for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      str														=       LevitModel.from_pretrained(A		)
                self.assertIsNotNone(A		)







def 		__snake_case		( )    ->  Dict:





     '''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	:      List[Any]		):
           return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)



      @slow
      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      str														=       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
               A		)

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

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

           # verify the logits
           _UpperCAmelCase     :      Union[str, Any]														=       torch.Size((1, 1_0_0_0)		)
           self.assertEqual(outputs.logits.shape	,      A		)

           _UpperCAmelCase     :      Any														=       torch.tensor([1.0_448, -0.3_745, -1.8_317]		).to(A		)

           self.assertTrue(torch.allclose(outputs.logits[0, :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))
