






'''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			:		Optional[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[int]=13     ,      UpperCAmelCase			:		str=32     ,      UpperCAmelCase			:		Any=2     ,      UpperCAmelCase			:		Dict=3     ,      UpperCAmelCase			:		str=16     ,      UpperCAmelCase			:		Union[str, Any]=[1, 2, 1]     ,      UpperCAmelCase			:		List[str]=[2, 2, 4]     ,      UpperCAmelCase			:		Tuple=2     ,      UpperCAmelCase			:		Tuple=2.0     ,      UpperCAmelCase			:		Optional[Any]=True     ,      UpperCAmelCase			:		Dict=0.0     ,      UpperCAmelCase			:		Tuple=0.0     ,      UpperCAmelCase			:		List[str]=0.1     ,      UpperCAmelCase			:		str="gelu"     ,      UpperCAmelCase			:		Dict=False     ,      UpperCAmelCase			:		Tuple=True     ,      UpperCAmelCase			:		str=0.0_2     ,      UpperCAmelCase			:		List[str]=1e-5     ,      UpperCAmelCase			:		Any=True     ,      UpperCAmelCase			:		Optional[Any]=None     ,      UpperCAmelCase			:		Optional[Any]=True     ,      UpperCAmelCase			:		Dict=10     ,      UpperCAmelCase			:		Dict=8     ,      UpperCAmelCase			:		str=["stage1", "stage2", "stage3"]     ,      UpperCAmelCase			:		List[str]=[1, 2, 3]     ,      )  ->  Any:


       '''simple docstring'''

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






      def 				A__      ( self			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

       lowercase					:   List[str]				  =self.get_config()

       return config, pixel_values, labels






      def 				A__      ( self			:		Any		)  ->  Optional[Any]:


       '''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 				A__      ( self			:		int     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Any		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   Any				  =MaskFormerSwinModel(config=UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()
       lowercase					:   Dict				  =model(UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
       lowercase					:   List[str]				  =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 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Any				  =MaskFormerSwinBackbone(config=UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()
       lowercase					:   str				  =model(UpperCAmelCase		)

       # 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(UpperCAmelCase		):
        lowercase					:   Dict				  =['''stem''']
        lowercase					:   str				  =MaskFormerSwinBackbone(config=UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.prepare_config_and_inputs()
       lowercase  ,		lowercase  ,		lowercase					:   List[Any]				  =config_and_inputs
       lowercase					:   Any				  ={'''pixel_values''': pixel_values}
       return config, inputs_dict





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




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              MaskFormerSwinModel,
              MaskFormerSwinBackbone,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase_								=			{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Any		)  ->  str:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =MaskFormerSwinModelTester(self		)
       lowercase					:   List[str]				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      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 				A__      ( self			:		Any		)  ->  str:


       '''simple docstring'''

       pass






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''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 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       return






      def 				A__      ( self			:		Optional[int]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*UpperCAmelCase		)






      @unittest.skip('''Swin does not use inputs_embeds'''		)
      def 				A__      ( self			:		Optional[int]		)  ->  Dict:


       '''simple docstring'''

       pass






      @unittest.skip('''Swin does not support feedforward chunking'''		)
      def 				A__      ( self			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       pass






      def 				A__      ( self			:		str		)  ->  str:


       '''simple docstring'''

       lowercase  ,		lowercase					:   int				  =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowercase					:   Optional[Any]				  =model_class(UpperCAmelCase		)
        self.assertIsInstance(model.get_input_embeddings()     ,      (nn.Module)		)
        lowercase					:   int				  =model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(UpperCAmelCase     ,      nn.Linear		)		)






      def 				A__      ( self			:		Any		)  ->  str:


       '''simple docstring'''

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

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

        lowercase					:   Optional[Any]				  =['''pixel_values''']
        self.assertListEqual(arg_names[:1]     ,      UpperCAmelCase		)






      @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''		)
      def 				A__      ( self			:		str		)  ->  Any:


       '''simple docstring'''

       pass






      @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''		)
      def 				A__      ( self			:		List[str]		)  ->  Optional[Any]:


       '''simple docstring'''

       pass






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Tuple				  =model_class(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       with torch.no_grad():
        lowercase					:   Union[str, Any]				  =model(**self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase		)		)

       lowercase					:   Tuple				  =outputs.hidden_states

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

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

       lowercase					:   Dict				  =(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 				A__      ( self			:		List[Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase  ,		lowercase					:   List[str]				  =self.model_tester.prepare_config_and_inputs_for_common()

       lowercase					:   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:
        lowercase					:   List[Any]				  =True
        self.check_hidden_states_output(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

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

        self.check_hidden_states_output(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase  ,		lowercase					:   int				  =self.model_tester.prepare_config_and_inputs_for_common()
       lowercase					:   List[str]				  =3

       lowercase					:   List[Any]				  =(
           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					:   List[str]				  =(
           config.patch_size
           if isinstance(config.patch_size     ,      collections.abc.Iterable		)
           else (config.patch_size, config.patch_size)
       )

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

       for model_class in self.all_model_classes:
        lowercase					:   Union[str, Any]				  =True
        self.check_hidden_states_output(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      (padded_height, padded_width)		)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase					:   Any				  =True
        self.check_hidden_states_output(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      (padded_height, padded_width)		)






      @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''		)
      def 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       pass






      @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''		)
      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       pass






      @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''		)
      def 				A__      ( self			:		Union[str, Any]		)  ->  str:


       '''simple docstring'''

       pass






      def 				A__      ( self			:		Optional[Any]		)  ->  Any:


       '''simple docstring'''

       lowercase  ,		lowercase					:   int				  =self.model_tester.prepare_config_and_inputs_for_common()

       def set_nan_tensor_to_zero(UpperCAmelCase			:		List[str]		):
        lowercase					:   Optional[int]				  =0
        return t

       def check_equivalence(UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[int]={}		):
        with torch.no_grad():
         lowercase					:   int				  =model(**UpperCAmelCase     ,      return_dict=UpperCAmelCase     ,      **UpperCAmelCase		)
         lowercase					:   Tuple				  =model(**UpperCAmelCase     ,      return_dict=UpperCAmelCase     ,      **UpperCAmelCase		).to_tuple()

         def recursive_check(UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Tuple		):
          if isinstance(UpperCAmelCase     ,      (List, Tuple)		):
           for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase     ,      UpperCAmelCase		):
            recursive_check(UpperCAmelCase     ,      UpperCAmelCase		)
          elif isinstance(UpperCAmelCase     ,      UpperCAmelCase		):
           for tuple_iterable_value, dict_iterable_value in zip(
               tuple_object.values()     ,      dict_object.values()		):
            recursive_check(UpperCAmelCase     ,      UpperCAmelCase		)
          elif tuple_object is None:
           return
          else:
           self.assertTrue(
               torch.allclose(
                   set_nan_tensor_to_zero(UpperCAmelCase		)     ,      set_nan_tensor_to_zero(UpperCAmelCase		)     ,      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(UpperCAmelCase		).any()} and `inf`: {torch.isinf(UpperCAmelCase		)}. Dict has'
                   f' `nan`: {torch.isnan(UpperCAmelCase		).any()} and `inf`: {torch.isinf(UpperCAmelCase		)}.'
               )     ,      )

         recursive_check(UpperCAmelCase     ,      UpperCAmelCase		)

       for model_class in self.all_model_classes:
        lowercase					:   Any				  =model_class(UpperCAmelCase		)
        model.to(UpperCAmelCase		)
        model.eval()

        lowercase					:   List[Any]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase		)
        lowercase					:   Union[str, Any]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase		)
        check_equivalence(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

        lowercase					:   Optional[int]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase     ,      return_labels=UpperCAmelCase		)
        lowercase					:   List[str]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase     ,      return_labels=UpperCAmelCase		)
        check_equivalence(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

        lowercase					:   Union[str, Any]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase		)
        lowercase					:   List[str]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase		)
        check_equivalence(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      {'''output_hidden_states''': True}		)

        lowercase					:   int				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase     ,      return_labels=UpperCAmelCase		)
        lowercase					:   List[Any]				  =self._prepare_for_class(UpperCAmelCase     ,      UpperCAmelCase     ,      return_labels=UpperCAmelCase		)
        check_equivalence(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      {'''output_hidden_states''': True}		)





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




      """simple docstring"""




      UpperCamelCase_								=			(MaskFormerSwinBackbone,) if is_torch_available() else ()
      UpperCamelCase_								=			MaskFormerSwinConfig






      def 				A__      ( self			:		Optional[int]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =MaskFormerSwinModelTester(self		)






      def 				A__      ( self			:		List[str]		)  ->  Tuple:


       '''simple docstring'''

       lowercase  ,		lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs_for_common()
       lowercase					:   Tuple				  =inputs_dict['''pixel_values'''].shape[0]

       for backbone_class in self.all_model_classes:
        lowercase					:   str				  =backbone_class(UpperCAmelCase		)
        backbone.to(UpperCAmelCase		)
        backbone.eval()

        lowercase					:   Optional[int]				  =backbone(**UpperCAmelCase		)

        # Test default outputs and verify feature maps
        self.assertIsInstance(outputs.feature_maps     ,      UpperCAmelCase		)
        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
        lowercase					:   List[Any]				  =backbone(**UpperCAmelCase     ,      output_hidden_states=UpperCAmelCase		)
        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)
          lowercase  ,		lowercase  ,		lowercase					:   str				  =hidden_state.shape
          self.assertTrue((h_batch_size, h_n_channels)     ,      (batch_size, n_channels)		)

            # Test output_attentions=True
        if self.has_attentions:
         lowercase					:   str				  =backbone(**UpperCAmelCase     ,      output_attentions=UpperCAmelCase		)
         self.assertIsNotNone(outputs.attentions		)











'''simple docstring'''



SCREAMING_SNAKE_CASE						=	'Alexander Joslin'

import operator as op

from .stack import Stack


def 							lowercase_				(    __A :       str			)       ->      int:



 """simple docstring"""




 lowercase					:   int				  ={'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

 lowercase					:   Stack[int]				  =Stack()
 lowercase					:   Stack[str]				  =Stack()

 for i in equation:
  if i.isdigit():
   # RULE 1
   operand_stack.push(int(__A			)			)
  elif i in operators:
   # RULE 2
   operator_stack.push(__A			)
  elif i == ")":
   # RULE 4
   lowercase					:   Optional[Any]				  =operator_stack.peek()
   operator_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()
   lowercase					:   Optional[Any]				  =operand_stack.peek()
   operand_stack.pop()

   lowercase					:   List[str]				  =operators[opr](__A					,      __A			)
   operand_stack.push(__A			)

    # RULE 5
 return operand_stack.peek()


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	'(5 + ((4 * 2) * (2 + 3)))'
      # answer = 45
      print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



