
'''simple docstring'''





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

      from transformers import MaskFormerSwinBackbone
      from transformers.models.maskformer import MaskFormerSwinModel






class 							UpperCAmelCase_							:

 '''simple docstring'''







 def __init__(					self		:  Union[str, Any]			,				snake_case__		:  Tuple			,				snake_case__		:  List[str]=13			,				snake_case__		:  int=32			,				snake_case__		:  List[str]=2			,				snake_case__		:  int=3			,				snake_case__		:  Dict=16			,				snake_case__		:  Any=[1, 2, 1]			,				snake_case__		:  Optional[Any]=[2, 2, 4]			,				snake_case__		:  List[Any]=2			,				snake_case__		:  Optional[Any]=2.0			,				snake_case__		:  Optional[Any]=True			,				snake_case__		:  int=0.0			,				snake_case__		:  Dict=0.0			,				snake_case__		:  Union[str, Any]=0.1			,				snake_case__		:  int="gelu"			,				snake_case__		:  Optional[int]=False			,				snake_case__		:  List[Any]=True			,				snake_case__		:  Dict=0.02			,				snake_case__		:  List[Any]=1e-5			,				snake_case__		:  Dict=True			,				snake_case__		:  Optional[int]=None			,				snake_case__		:  List[str]=True			,				snake_case__		:  Optional[Any]=10			,				snake_case__		:  Any=8			,				snake_case__		:  Dict=["stage1", "stage2", "stage3"]			,				snake_case__		:  str=[1, 2, 3]			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							parent
   UpperCAmelCase__      :					Union[str, Any]				       =							batch_size
   UpperCAmelCase__      :					int				       =							image_size
   UpperCAmelCase__      :					Optional[Any]				       =							patch_size
   UpperCAmelCase__      :					Union[str, Any]				       =							num_channels
   UpperCAmelCase__      :					Optional[Any]				       =							embed_dim
   UpperCAmelCase__      :					Dict				       =							depths
   UpperCAmelCase__      :					List[Any]				       =							num_heads
   UpperCAmelCase__      :					Union[str, Any]				       =							window_size
   UpperCAmelCase__      :					Dict				       =							mlp_ratio
   UpperCAmelCase__      :					Optional[int]				       =							qkv_bias
   UpperCAmelCase__      :					Any				       =							hidden_dropout_prob
   UpperCAmelCase__      :					Optional[Any]				       =							attention_probs_dropout_prob
   UpperCAmelCase__      :					Tuple				       =							drop_path_rate
   UpperCAmelCase__      :					int				       =							hidden_act
   UpperCAmelCase__      :					Optional[Any]				       =							use_absolute_embeddings
   UpperCAmelCase__      :					List[Any]				       =							patch_norm
   UpperCAmelCase__      :					List[str]				       =							layer_norm_eps
   UpperCAmelCase__      :					List[str]				       =							initializer_range
   UpperCAmelCase__      :					Tuple				       =							is_training
   UpperCAmelCase__      :					List[str]				       =							scope
   UpperCAmelCase__      :					List[str]				       =							use_labels
   UpperCAmelCase__      :					List[str]				       =							type_sequence_label_size
   UpperCAmelCase__      :					List[Any]				       =							encoder_stride
   UpperCAmelCase__      :					Union[str, Any]				       =							out_features
   UpperCAmelCase__      :					List[Any]				       =							out_indices







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

   UpperCAmelCase__      :					int				       =							None
   if self.use_labels:
     UpperCAmelCase__      :					str				       =							ids_tensor([self.batch_size]			,				self.type_sequence_label_size )

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_config()

   return config, pixel_values, labels







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return MaskFormerSwinConfig(
       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			,				out_features=self.out_features			,				out_indices=self.out_indices			,				)







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  str			,				snake_case__		:  Optional[Any]			,				snake_case__		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							MaskFormerSwinModel(config=snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					str				       =							model(snake_case__ )

   UpperCAmelCase__      :					Dict				       =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
   UpperCAmelCase__      :					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 							UpperCamelCase  (					self		:  Optional[int]			,				snake_case__		:  str			,				snake_case__		:  str			,				snake_case__		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							MaskFormerSwinBackbone(config=snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					Tuple				       =							model(snake_case__ )

   # verify feature maps
   self.parent.assertEqual(len(result.feature_maps )			,				len(config.out_features ) )
   self.parent.assertListEqual(list(result.feature_maps[0].shape )			,				[13, 16, 16, 16] )

   # verify channels
   self.parent.assertEqual(len(model.channels )			,				len(config.out_features ) )
   self.parent.assertListEqual(model.channels			,				[16, 32, 64] )

   # verify ValueError
   with self.parent.assertRaises(snake_case__ ):
     UpperCAmelCase__      :					str				       =							["stem"]
     UpperCAmelCase__      :					Tuple				       =							MaskFormerSwinBackbone(config=snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							self.prepare_config_and_inputs()
   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Union[str, Any]				       =							config_and_inputs
   UpperCAmelCase__      :					int				       =							{"pixel_values": pixel_values}
   return config, inputs_dict






@require_torch
class 							UpperCAmelCase_							(    A					,				A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    int				=							(
     (
         MaskFormerSwinModel,
         MaskFormerSwinBackbone,
     )
     if is_torch_available()
     else ()
 )
 lowercase_					:    int				=							{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
 lowercase_					:    Optional[int]				=							False
 lowercase_					:    int				=							False
 lowercase_					:    Tuple				=							False
 lowercase_					:    Tuple				=							False
 lowercase_					:    Union[str, Any]				=							False







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							MaskFormerSwinModelTester(self )
   UpperCAmelCase__      :					Any				       =							ConfigTester(self			,				config_class=snake_case__			,				embed_dim=37 )







 @require_torch_multi_gpu
 @unittest.skip(
     reason=(
         "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
         " `nn.DataParallel`"
     ) )
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   pass







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   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		:  int ):



   '''simple docstring'''




   return







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*snake_case__ )







 @unittest.skip("Swin does not use inputs_embeds" )
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   pass







 @unittest.skip("Swin does not support feedforward chunking" )
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   pass







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					Tuple				       =							model_class(snake_case__ )
     self.assertIsInstance(model.get_input_embeddings()			,				(nn.Module) )
     UpperCAmelCase__      :					List[str]				       =							model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(snake_case__			,				nn.Linear ) )







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							self.model_tester.prepare_config_and_inputs_for_common()

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

     UpperCAmelCase__      :					Tuple				       =							["pixel_values"]
     self.assertListEqual(arg_names[:1]			,				snake_case__ )







 @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions" )
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   pass







 @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone" )
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   pass







 def 							UpperCamelCase  (					self		:  str			,				snake_case__		:  Tuple			,				snake_case__		:  List[str]			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Any				       =							model_class(snake_case__ )
   model.to(snake_case__ )
   model.eval()

   with torch.no_grad():
     UpperCAmelCase__      :					Any				       =							model(**self._prepare_for_class(snake_case__			,				snake_case__ ) )

   UpperCAmelCase__      :					Optional[int]				       =							outputs.hidden_states

   UpperCAmelCase__      :					int				       =							getattr(
       self.model_tester			,				"expected_num_hidden_layers"			,				len(self.model_tester.depths ) + 1 )
   self.assertEqual(len(snake_case__ )			,				snake_case__ )

   # Swin has a different seq_length
   UpperCAmelCase__      :					Dict				       =							(
       config.patch_size
       if isinstance(config.patch_size			,				collections.abc.Iterable )
       else (config.patch_size, config.patch_size)
   )

   UpperCAmelCase__      :					List[str]				       =							(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]			,				)







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__      :					Dict				       =							(
       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:
     UpperCAmelCase__      :					List[str]				       =							True
     self.check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__			,				snake_case__ )

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

     self.check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      :					List[Any]				       =							3

   UpperCAmelCase__      :					Dict				       =							(
       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)
   )
   UpperCAmelCase__      :					Tuple				       =							(
       config.patch_size
       if isinstance(config.patch_size			,				collections.abc.Iterable )
       else (config.patch_size, config.patch_size)
   )

   UpperCAmelCase__      :					List[str]				       =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
   UpperCAmelCase__      :					Dict				       =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					str				       =							True
     self.check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__			,				(padded_height, padded_width) )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCAmelCase__      :					List[str]				       =							True
     self.check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__			,				(padded_height, padded_width) )







 @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints" )
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   pass







 @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   pass







 @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   pass







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							self.model_tester.prepare_config_and_inputs_for_common()

   def set_nan_tensor_to_zero(snake_case__		:  int ):
     UpperCAmelCase__      :					Optional[int]				       =							0
     return t

   def check_equivalence(snake_case__		:  Any			,				snake_case__		:  str			,				snake_case__		:  List[Any]			,				snake_case__		:  Any={} ):
     with torch.no_grad():
       UpperCAmelCase__      :					str				       =							model(**snake_case__			,				return_dict=snake_case__			,				**snake_case__ )
       UpperCAmelCase__      :					Optional[int]				       =							model(**snake_case__			,				return_dict=snake_case__			,				**snake_case__ ).to_tuple()

       def recursive_check(snake_case__		:  Any			,				snake_case__		:  Dict ):
         if isinstance(snake_case__			,				(List, Tuple) ):
           for tuple_iterable_value, dict_iterable_value in zip(snake_case__			,				snake_case__ ):
             recursive_check(snake_case__			,				snake_case__ )
         elif isinstance(snake_case__			,				snake_case__ ):
           for tuple_iterable_value, dict_iterable_value in zip(
               tuple_object.values()			,				dict_object.values() ):
             recursive_check(snake_case__			,				snake_case__ )
         elif tuple_object is None:
           return
         else:
           self.assertTrue(
               torch.allclose(
                   set_nan_tensor_to_zero(snake_case__ )			,				set_nan_tensor_to_zero(snake_case__ )			,				atol=1e-5 )			,				msg=(
                   "Tuple and dict output are not equal. Difference:"
                   F""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
                   F""" {torch.isnan(snake_case__ ).any()} and `inf`: {torch.isinf(snake_case__ )}. Dict has"""
                   F""" `nan`: {torch.isnan(snake_case__ ).any()} and `inf`: {torch.isinf(snake_case__ )}."""
               )			,				)

       recursive_check(snake_case__			,				snake_case__ )

   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					List[Any]				       =							model_class(snake_case__ )
     model.to(snake_case__ )
     model.eval()

     UpperCAmelCase__      :					int				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
     UpperCAmelCase__      :					str				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
     check_equivalence(snake_case__			,				snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					Tuple				       =							self._prepare_for_class(snake_case__			,				snake_case__			,				return_labels=snake_case__ )
     UpperCAmelCase__      :					List[str]				       =							self._prepare_for_class(snake_case__			,				snake_case__			,				return_labels=snake_case__ )
     check_equivalence(snake_case__			,				snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					Union[str, Any]				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
     UpperCAmelCase__      :					Any				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
     check_equivalence(snake_case__			,				snake_case__			,				snake_case__			,				{"output_hidden_states": True} )

     UpperCAmelCase__      :					Dict				       =							self._prepare_for_class(snake_case__			,				snake_case__			,				return_labels=snake_case__ )
     UpperCAmelCase__      :					Any				       =							self._prepare_for_class(snake_case__			,				snake_case__			,				return_labels=snake_case__ )
     check_equivalence(snake_case__			,				snake_case__			,				snake_case__			,				{"output_hidden_states": True} )






@require_torch
class 							UpperCAmelCase_							(    unittest.TestCase					,				A				):

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							(MaskFormerSwinBackbone,) if is_torch_available() else ()
 lowercase_					:    int				=							MaskFormerSwinConfig







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							MaskFormerSwinModelTester(self )







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      :					Tuple				       =							inputs_dict["pixel_values"].shape[0]

   for backbone_class in self.all_model_classes:
     UpperCAmelCase__      :					Any				       =							backbone_class(snake_case__ )
     backbone.to(snake_case__ )
     backbone.eval()

     UpperCAmelCase__      :					Optional[Any]				       =							backbone(**snake_case__ )

     # Test default outputs and verify feature maps
     self.assertIsInstance(outputs.feature_maps			,				snake_case__ )
     self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
     for feature_map, n_channels in zip(outputs.feature_maps			,				backbone.channels ):
       self.assertTrue(feature_map.shape[:2]			,				(batch_size, n_channels) )
     self.assertIsNone(outputs.hidden_states )
     self.assertIsNone(outputs.attentions )

     # Test output_hidden_states=True
     UpperCAmelCase__      :					Any				       =							backbone(**snake_case__			,				output_hidden_states=snake_case__ )
     self.assertIsNotNone(outputs.hidden_states )
     self.assertTrue(len(outputs.hidden_states )			,				len(backbone.stage_names ) )
     # We skip the stem layer
     for hidden_states, n_channels in zip(outputs.hidden_states[1:]			,				backbone.channels ):
       for hidden_state in hidden_states:
         # Hidden states are in the format (batch_size, (height * width), n_channels)
         UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							hidden_state.shape
         self.assertTrue((h_batch_size, h_n_channels)			,				(batch_size, n_channels) )

            # Test output_attentions=True
     if self.has_attentions:
       UpperCAmelCase__      :					int				       =							backbone(**snake_case__			,				output_attentions=snake_case__ )
       self.assertIsNotNone(outputs.attentions )





'''simple docstring'''





def snake_case_     (					lowercase__		):

  if len(lowercase__		) <= 1:
    return [tuple(lowercase__		)]

  UpperCAmelCase__      :					Union[str, Any]				       =							[]

  def generate(lowercase__   ,       lowercase__		):
    UpperCAmelCase__      :					str				       =							[0] * n
    res.append(tuple(lowercase__		)		)

    UpperCAmelCase__      :					Any				       =							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							arr[i], arr[0]
        else:
          UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							arr[i], arr[c[i]]
        res.append(tuple(lowercase__		)		)
        c[i] += 1
        UpperCAmelCase__      :					List[str]				       =							0
      else:
        UpperCAmelCase__      :					Dict				       =							0
        i += 1

  generate(len(lowercase__		)   ,       lowercase__		)
  return res


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



