
"""simple docstring"""




import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
   from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class 		a__						:
   def __init__(			self	,    _a	,    _a=13	,    _a=32	,    _a=2	,    _a=3	,    _a=16	,    _a=[1, 2, 1]	,    _a=[2, 2, 4]	,    _a=2	,    _a=2.0	,    _a=True	,    _a=0.0	,    _a=0.0	,    _a=0.1	,    _a="gelu"	,    _a=False	,    _a=True	,    _a=0.0_2	,    _a=1E-5	,    _a=True	,    _a=None	,    _a=True	,    _a=10	,    _a=8	,    ):
          lowercase    :				List[Any]       		=							parent
          lowercase    :				Union[str, Any]       		=							batch_size
          lowercase    :				Optional[int]       		=							image_size
          lowercase    :				Tuple       		=							patch_size
          lowercase    :				Tuple       		=							num_channels
          lowercase    :				List[str]       		=							embed_dim
          lowercase    :				List[str]       		=							depths
          lowercase    :				int       		=							num_heads
          lowercase    :				str       		=							window_size
          lowercase    :				Optional[Any]       		=							mlp_ratio
          lowercase    :				int       		=							qkv_bias
          lowercase    :				Optional[Any]       		=							hidden_dropout_prob
          lowercase    :				Optional[int]       		=							attention_probs_dropout_prob
          lowercase    :				Any       		=							drop_path_rate
          lowercase    :				Any       		=							hidden_act
          lowercase    :				List[str]       		=							use_absolute_embeddings
          lowercase    :				Tuple       		=							patch_norm
          lowercase    :				Union[str, Any]       		=							layer_norm_eps
          lowercase    :				Dict       		=							initializer_range
          lowercase    :				List[str]       		=							is_training
          lowercase    :				Any       		=							scope
          lowercase    :				int       		=							use_labels
          lowercase    :				int       		=							type_sequence_label_size
          lowercase    :				str       		=							encoder_stride
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          lowercase    :				int       		=							self.get_config()

          return config, pixel_values, labels
   def 			__magic_name__			(			self				):
          return SwinvaConfig(
              image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    embed_dim=self.embed_dim	,    depths=self.depths	,    num_heads=self.num_heads	,    window_size=self.window_size	,    mlp_ratio=self.mlp_ratio	,    qkv_bias=self.qkv_bias	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    drop_path_rate=self.drop_path_rate	,    hidden_act=self.hidden_act	,    use_absolute_embeddings=self.use_absolute_embeddings	,    path_norm=self.patch_norm	,    layer_norm_eps=self.layer_norm_eps	,    initializer_range=self.initializer_range	,    encoder_stride=self.encoder_stride	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				int       		=							SwinvaModel(config=_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				List[str]       		=							model(_a				)

          lowercase    :				str       		=							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
          lowercase    :				List[Any]       		=							int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, expected_seq_len, expected_dim)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Tuple       		=							SwinvaForMaskedImageModeling(config=_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				str       		=							model(_a				)
          self.parent.assertEqual(
              result.logits.shape	,    (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

          # test greyscale images
          lowercase    :				Tuple       		=							1
          lowercase    :				Dict       		=							SwinvaForMaskedImageModeling(_a				)
          model.to(_a				)
          model.eval()

          lowercase    :				int       		=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          lowercase    :				Optional[int]       		=							model(_a				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, 1, self.image_size, self.image_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							self.type_sequence_label_size
          lowercase    :				List[str]       		=							SwinvaForImageClassification(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Dict       		=							model(_a	,    labels=_a				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.prepare_config_and_inputs()
          lowercase		,    lowercase		,    lowercase    :				Optional[int]       		=							config_and_inputs
          lowercase    :				Optional[int]       		=							{"pixel_values": pixel_values}
          return config, inputs_dict


@require_torch
class 		a__						(      a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(
       (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
   )
   __lowerCAmelCase            =				(
       {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							SwinvaModelTester(self				)
          lowercase    :				str       		=							ConfigTester(self	,    config_class=_a	,    embed_dim=37				)
   def 			__magic_name__			(			self				):
          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 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a				)
   @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip(reason="Swinv2 does not use inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Optional[int]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				str       		=							model_class(_a				)
                 self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)				)
                 lowercase    :				Any       		=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_a	,    nn.Linear				)				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase    :				Any       		=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				Optional[Any]       		=							True

          for model_class in self.all_model_classes:
                 lowercase    :				Dict       		=							True
                 lowercase    :				Optional[Any]       		=							False
                 lowercase    :				Optional[Any]       		=							True
                 lowercase    :				Union[str, Any]       		=							model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 with torch.no_grad():
                        lowercase    :				Optional[Any]       		=							model(**self._prepare_for_class(_a	,    _a				)				)
                 lowercase    :				Optional[Any]       		=							outputs.attentions
                 lowercase    :				Tuple       		=							len(self.model_tester.depths				)
                 self.assertEqual(len(_a				)	,    _a				)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 lowercase    :				str       		=							True
                 lowercase    :				Tuple       		=							config.window_size**2
                 lowercase    :				Dict       		=							model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 with torch.no_grad():
                        lowercase    :				Union[str, Any]       		=							model(**self._prepare_for_class(_a	,    _a				)				)
                 lowercase    :				List[str]       		=							outputs.attentions
                 self.assertEqual(len(_a				)	,    _a				)

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]				)	,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,    )
                 lowercase    :				int       		=							len(_a				)

                 # Check attention is always last and order is fine
                 lowercase    :				Optional[Any]       		=							True
                 lowercase    :				Optional[Any]       		=							True
                 lowercase    :				Dict       		=							model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 with torch.no_grad():
                        lowercase    :				List[str]       		=							model(**self._prepare_for_class(_a	,    _a				)				)

                 if hasattr(self.model_tester	,    "num_hidden_states_types"				):
                        lowercase    :				int       		=							self.model_tester.num_hidden_states_types
                 else:
                        # also another +1 for reshaped_hidden_states
                        lowercase    :				Dict       		=							2
                 self.assertEqual(out_len + added_hidden_states	,    len(_a				)				)

                 lowercase    :				Tuple       		=							outputs.attentions

                 self.assertEqual(len(_a				)	,    _a				)

                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]				)	,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a				):
          lowercase    :				Optional[int]       		=							model_class(_a				)
          model.to(_a				)
          model.eval()

          with torch.no_grad():
                 lowercase    :				Optional[int]       		=							model(**self._prepare_for_class(_a	,    _a				)				)

          lowercase    :				Optional[Any]       		=							outputs.hidden_states

          lowercase    :				Union[str, Any]       		=							getattr(
              self.model_tester	,    "expected_num_hidden_layers"	,    len(self.model_tester.depths				) + 1				)
          self.assertEqual(len(_a				)	,    _a				)

          # Swinv2 has a different seq_length
          lowercase    :				List[str]       		=							(
              config.patch_size
              if isinstance(config.patch_size	,    collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          lowercase    :				Tuple       		=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]				)	,    [num_patches, self.model_tester.embed_dim]	,    )

          lowercase    :				Optional[Any]       		=							outputs.reshaped_hidden_states
          self.assertEqual(len(_a				)	,    _a				)

          lowercase		,    lowercase		,    lowercase		,    lowercase    :				List[str]       		=							reshaped_hidden_states[0].shape
          lowercase    :				List[Any]       		=							(
              reshaped_hidden_states[0].view(_a	,    _a	,    height * width				).permute(0	,    2	,    1				)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]				)	,    [num_patches, self.model_tester.embed_dim]	,    )
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				List[str]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				List[str]       		=							(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size	,    collections.abc.Iterable				)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
                 lowercase    :				List[Any]       		=							True
                 self.check_hidden_states_output(_a	,    _a	,    _a	,    _a				)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase    :				Dict       		=							True

                 self.check_hidden_states_output(_a	,    _a	,    _a	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Tuple       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				List[Any]       		=							3

          lowercase    :				Optional[int]       		=							(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size	,    collections.abc.Iterable				)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          lowercase    :				str       		=							(
              config.patch_size
              if isinstance(config.patch_size	,    collections.abc.Iterable				)
              else (config.patch_size, config.patch_size)
          )

          lowercase    :				Optional[int]       		=							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          lowercase    :				Dict       		=							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
                 lowercase    :				Tuple       		=							True
                 self.check_hidden_states_output(_a	,    _a	,    _a	,    (padded_height, padded_width)				)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase    :				List[Any]       		=							True
                 self.check_hidden_states_output(_a	,    _a	,    _a	,    (padded_height, padded_width)				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_a				)
   @slow
   def 			__magic_name__			(			self				):
          for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase    :				int       		=							SwinvaModel.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)

   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				Tuple       		=							_config_zero_init(_a				)
          for model_class in self.all_model_classes:
                 lowercase    :				str       		=							model_class(config=_a				)
                 for name, param in model.named_parameters():
                        if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                               self.assertIn(
                                   ((param.data.mean() * 1E9).round() / 1E9).item()	,    [0.0, 1.0]	,    msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,    )


@require_vision
@require_torch
class 		a__						(      unittest.TestCase						):
   @cached_property
   def 			__magic_name__			(			self				):
          return (
              AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"				)
              if is_vision_available()
              else None
          )

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"				).to(
              _a				)
          lowercase    :				Any       		=							self.default_image_processor

          lowercase    :				List[str]       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
          lowercase    :				int       		=							image_processor(images=_a	,    return_tensors="pt"				).to(_a				)

          # forward pass
          with torch.no_grad():
                 lowercase    :				List[Any]       		=							model(**_a				)

          # verify the logits
          lowercase    :				Optional[int]       		=							torch.Size((1, 1_000)				)
          self.assertEqual(outputs.logits.shape	,    _a				)
          lowercase    :				Tuple       		=							torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]				).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 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


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


