


import heapq
import sys

import numpy as np

lowerCAmelCase__      		= tuple[int, int]







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self)			->				Optional[int]:
            _A      :       List[str]				    = []
            _A      :       Optional[int]				    = set()


     def  _lowerCamelCase (						self)			->				Any:
            if not self.empty():
                   return self.elements[0][0]
            else:
                   return float("inf")


     def  _lowerCamelCase (						self)			->				List[str]:
            return len(self.elements) == 0


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[Any]:
            if item not in self.set:
                   heapq.heappush(self.elements ,						(priority, item))
                   self.set.add(__lowerCamelCase)
            else:
                   # update
                   # print("update", item)
                   _A      :       Optional[Any]				    = []
                   ((_A)    ,			(_A))      :       Dict				    = heapq.heappop(self.elements)
                   while x != item:
                          temp.append((pri, x))
                          ((_A)    ,			(_A))      :       Optional[Any]				    = heapq.heappop(self.elements)
                   temp.append((priority, item))
                   for pro, xxx in temp:
                          heapq.heappush(self.elements ,						(pro, xxx))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            if item in self.set:
                   self.set.remove(__lowerCamelCase)
                   _A      :       Any				    = []
                   ((_A)    ,			(_A))      :       List[Any]				    = heapq.heappop(self.elements)
                   while x != item:
                          temp.append((pro, x))
                          ((_A)    ,			(_A))      :       Union[str, Any]				    = heapq.heappop(self.elements)
                   for prito, yyy in temp:
                          heapq.heappush(self.elements ,						(prito, yyy))


     def  _lowerCamelCase (						self)			->				Any:
            return self.elements[0][1]


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            ((_A)    ,			(_A))      :       Any				    = heapq.heappop(self.elements)
            self.set.remove(__lowerCamelCase)
            return (priority, item)




def 							_UpperCAmelCase				(UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  TPos						):
       # euclidean distance
       _A      :       Optional[Any]				    = np.array(UpperCamelCase__						)
       _A      :       str				    = np.array(UpperCamelCase__						)
       return np.linalg.norm(a - b						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  TPos						):
       # integer division by time variable
       return consistent_heuristic(UpperCamelCase__				,							UpperCamelCase__						) // t
def 							_UpperCAmelCase				(UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  TPos						):
       # manhattan distance
       return abs(p[0] - goal[0]						) + abs(p[1] - goal[1]						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  int				,							UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  dict[TPos, float]						):
       _A      :       str				    = g_function[start] + Wa * heuristics[i](UpperCamelCase__				,							UpperCamelCase__						)
       return ans
def 							_UpperCAmelCase				(UpperCamelCase__     :  Dict				,							UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  str						):
       _A      :       List[Any]				    = np.chararray((n, n)						)
       for i in range(UpperCamelCase__						):
              for j in range(UpperCamelCase__						):
                     _A      :       Optional[int]				    = "*"

       for i in range(UpperCamelCase__						):
              for j in range(UpperCamelCase__						):
                     if (j, (n - 1) - i) in blocks:
                            _A      :       Dict				    = "#"

       _A      :       Optional[int]				    = "-"
       _A      :       Union[str, Any]				    = back_pointer[goal]
       while x != start:
              ((_A)    ,			(_A))      :       Optional[Any]				    = x
              # print(x)
              _A      :       Union[str, Any]				    = "-"
              _A      :       List[Any]				    = back_pointer[x]
       _A      :       int				    = "-"

       for i in range(UpperCamelCase__						):
              for j in range(UpperCamelCase__						):
                     if (i, j) == (0, n - 1):
                            print(grid[i][j]				,							end=" "						)
                            print("<-- End position"				,							end=" "						)
                     else:
                            print(grid[i][j]				,							end=" "						)
              print()
       print("^"						)
       print("Start position"						)
       print()
       print("# is an obstacle"						)
       print("- is the path taken by algorithm"						)
       print("PATH TAKEN BY THE ALGORITHM IS:-"						)
       _A      :       Optional[Any]				    = back_pointer[goal]
       while x != start:
              print(UpperCamelCase__				,							end=" "						)
              _A      :       List[Any]				    = back_pointer[x]
       print(UpperCamelCase__						)
       sys.exit()
def 							_UpperCAmelCase				(UpperCamelCase__     :  TPos						):
       if p[0] < 0 or p[0] > n - 1:
              return False
       if p[1] < 0 or p[1] > n - 1:
              return False
       return True
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  int				,							UpperCamelCase__     :  str				,							UpperCamelCase__     :  Any				,							):
       for itera in range(UpperCamelCase__						):
              open_list[itera].remove_element(UpperCamelCase__						)
       # print("s", s)
       # print("j", j)
       ((_A)    ,			(_A))      :       int				    = s
       _A      :       Dict				    = (x - 1, y)
       _A      :       Any				    = (x + 1, y)
       _A      :       Dict				    = (x, y + 1)
       _A      :       Union[str, Any]				    = (x, y - 1)

       for neighbours in [left, right, up, down]:
              if neighbours not in blocks:
                     if valid(UpperCamelCase__						) and neighbours not in visited:
                            # print("neighbour", neighbours)
                            visited.add(UpperCamelCase__						)
                            _A      :       Dict				    = -1
                            _A      :       List[str]				    = float("inf"						)

                     if valid(UpperCamelCase__						) and g_function[neighbours] > g_function[s] + 1:
                            _A      :       Union[str, Any]				    = g_function[s] + 1
                            _A      :       int				    = s
                            if neighbours not in close_list_anchor:
                                   open_list[0].put(UpperCamelCase__				,							key(UpperCamelCase__				,							0				,							UpperCamelCase__				,							UpperCamelCase__						)						)
                                   if neighbours not in close_list_inad:
                                          for var in range(1				,							UpperCamelCase__						):
                                                 if key(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						) <= Wa * key(
                                                     UpperCamelCase__				,							0				,							UpperCamelCase__				,							UpperCamelCase__						):
                                                        open_list[j].put(
                                                            UpperCamelCase__				,							key(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)						)
def 							_UpperCAmelCase				():
       _A      :       Union[str, Any]				    = []
       for x in range(1				,							5						):
              for y in range(1				,							6						):
                     some_list.append((x, y)						)

       for x in range(15				,							20						):
              some_list.append((x, 17)						)

       for x in range(10				,							19						):
              for y in range(1				,							15						):
                     some_list.append((x, y)						)

    # L block
       for x in range(1				,							4						):
              for y in range(12				,							19						):
                     some_list.append((x, y)						)
       for x in range(3				,							13						):
              for y in range(16				,							19						):
                     some_list.append((x, y)						)
       return some_list


lowerCAmelCase__      		= {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowerCAmelCase__      		= [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowerCAmelCase__      		= make_common_ground()


lowerCAmelCase__      		= blocks_blk
# hyper parameters
lowerCAmelCase__      		= 1
lowerCAmelCase__      		= 1
lowerCAmelCase__      		= 20
lowerCAmelCase__      		= 3  # one consistent and two other inconsistent

# start and end destination
lowerCAmelCase__      		= (0, 0)
lowerCAmelCase__      		= (n - 1, n - 1)

lowerCAmelCase__      		= 1
def 							_UpperCAmelCase				(UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  TPos				,							UpperCamelCase__     :  int						):
       _A      :       Optional[int]				    = {start: 0, goal: float("inf"						)}
       _A      :       int				    = {start: -1, goal: -1}
       _A      :       Dict				    = []
       _A      :       Dict				    = set()

       for i in range(UpperCamelCase__						):
              open_list.append(PriorityQueue()						)
              open_list[i].put(UpperCamelCase__				,							key(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)						)

       _A      :       list[int]				    = []
       _A      :       list[int]				    = []
       while open_list[0].minkey() < float("inf"						):
              for i in range(1				,							UpperCamelCase__						):
                     # print(open_list[0].minkey(), open_list[i].minkey())
                     if open_list[i].minkey() <= Wa * open_list[0].minkey():
                            global t
                            t += 1
                            if g_function[goal] <= open_list[i].minkey():
                                   if g_function[goal] < float("inf"						):
                                          do_something(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
                            else:
                                   _A    ,			_A      :       Tuple				    = open_list[i].top_show()
                                   visited.add(UpperCamelCase__						)
                                   expand_state(
                                       UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							)
                                   close_list_inad.append(UpperCamelCase__						)
                     else:
                            if g_function[goal] <= open_list[0].minkey():
                                   if g_function[goal] < float("inf"						):
                                          do_something(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
                            else:
                                   _A      :       Dict				    = open_list[0].top_show()
                                   visited.add(UpperCamelCase__						)
                                   expand_state(
                                       UpperCamelCase__				,							0				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							)
                                   close_list_anchor.append(UpperCamelCase__						)
       print("No path found to goal"						)
       print()
       for i in range(n - 1				,							-1				,							-1						):
              for j in range(UpperCamelCase__						):
                     if (j, i) in blocks:
                            print("#"				,							end=" "						)
                     elif (j, i) in back_pointer:
                            if (j, i) == (n - 1, n - 1):
                                   print("*"				,							end=" "						)
                            else:
                                   print("-"				,							end=" "						)
                     else:
                            print("*"				,							end=" "						)
                     if (j, i) == (n - 1, n - 1):
                            print("<-- End position"				,							end=" "						)
              print()
       print("^"						)
       print("Start position"						)
       print()
       print("# is an obstacle"						)
       print("- is the path taken by algorithm"						)


if __name__ == "__main__":
    multi_a_star(start, goal, n_heuristic)




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      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=1_3 ,						__lowerCamelCase=3_2 ,						__lowerCamelCase=2 ,						__lowerCamelCase=3 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=[1, 2, 1] ,						__lowerCamelCase=[2, 2, 4] ,						__lowerCamelCase=2 ,						__lowerCamelCase=2.0 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=False ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=True ,						__lowerCamelCase=None ,						__lowerCamelCase=True ,						__lowerCamelCase=1_0 ,						__lowerCamelCase=8 ,						__lowerCamelCase=["stage1", "stage2", "stage3"] ,						__lowerCamelCase=[1, 2, 3] ,						)			->				Optional[Any]:
            _A      :       int				    = parent
            _A      :       Optional[Any]				    = batch_size
            _A      :       str				    = image_size
            _A      :       Tuple				    = patch_size
            _A      :       Tuple				    = num_channels
            _A      :       Optional[int]				    = embed_dim
            _A      :       Dict				    = depths
            _A      :       Any				    = num_heads
            _A      :       Any				    = window_size
            _A      :       int				    = mlp_ratio
            _A      :       Any				    = qkv_bias
            _A      :       Union[str, Any]				    = hidden_dropout_prob
            _A      :       Optional[Any]				    = attention_probs_dropout_prob
            _A      :       Dict				    = drop_path_rate
            _A      :       List[Any]				    = hidden_act
            _A      :       Any				    = use_absolute_embeddings
            _A      :       Optional[int]				    = patch_norm
            _A      :       Tuple				    = layer_norm_eps
            _A      :       List[str]				    = initializer_range
            _A      :       Optional[int]				    = is_training
            _A      :       Optional[Any]				    = scope
            _A      :       Optional[int]				    = use_labels
            _A      :       Dict				    = type_sequence_label_size
            _A      :       str				    = encoder_stride
            _A      :       Optional[int]				    = out_features
            _A      :       Optional[int]				    = out_indices


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[str]				    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

            _A      :       Optional[Any]				    = None
            if self.use_labels:
                   _A      :       Any				    = ids_tensor([self.batch_size] ,						self.type_sequence_label_size)

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

            return config, pixel_values, labels


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[Any]:
            _A      :       Dict				    = MaskFormerSwinModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       int				    = model(__lowerCamelCase)

            _A      :       Any				    = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
            _A      :       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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Dict:
            _A      :       Optional[Any]				    = MaskFormerSwinBackbone(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       Dict				    = model(__lowerCamelCase)

            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps) ,						len(config.out_features))
            self.parent.assertListEqual(list(result.feature_maps[0].shape) ,						[1_3, 1_6, 1_6, 1_6])

            # verify channels
            self.parent.assertEqual(len(model.channels) ,						len(config.out_features))
            self.parent.assertListEqual(model.channels ,						[1_6, 3_2, 6_4])

            # verify ValueError
            with self.parent.assertRaises(__lowerCamelCase):
                   _A      :       Union[str, Any]				    = ["stem"]
                   _A      :       Union[str, Any]				    = MaskFormerSwinBackbone(config=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       Any				    = self.prepare_config_and_inputs()
            _A    ,			_A    ,			_A      :       List[Any]				    = config_and_inputs
            _A      :       Optional[int]				    = {"pixel_values": pixel_values}
            return config, inputs_dict







@require_torch
class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= (
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     __SCREAMING_SNAKE_CASE  			= {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False


     def  _lowerCamelCase (						self)			->				str:
            _A      :       Union[str, Any]				    = MaskFormerSwinModelTester(self)
            _A      :       Optional[int]				    = ConfigTester(self ,						config_class=__lowerCamelCase ,						embed_dim=3_7)


     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
             " `nn.DataParallel`"
         ))
     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            pass


     def  _lowerCamelCase (						self)			->				int:
            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  _lowerCamelCase (						self)			->				str:
            return


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[int]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Any				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowerCamelCase)


     @unittest.skip("Swin does not use inputs_embeds")
     def  _lowerCamelCase (						self)			->				str:
            pass


     @unittest.skip("Swin does not support feedforward chunking")
     def  _lowerCamelCase (						self)			->				List[Any]:
            pass


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A    ,			_A      :       Union[str, Any]				    = self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A      :       Union[str, Any]				    = model_class(__lowerCamelCase)
                   self.assertIsInstance(model.get_input_embeddings() ,						(nn.Module))
                   _A      :       Dict				    = model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(__lowerCamelCase ,						nn.Linear))


     def  _lowerCamelCase (						self)			->				Any:
            _A    ,			_A      :       List[Any]				    = self.model_tester.prepare_config_and_inputs_for_common()

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

                   _A      :       Optional[int]				    = ["pixel_values"]
                   self.assertListEqual(arg_names[:1] ,						__lowerCamelCase)


     @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions")
     def  _lowerCamelCase (						self)			->				Tuple:
            pass


     @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone")
     def  _lowerCamelCase (						self)			->				str:
            pass


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[int]:
            _A      :       Any				    = model_class(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()

            with torch.no_grad():
                   _A      :       str				    = model(**self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase))

            _A      :       Tuple				    = outputs.hidden_states

            _A      :       Any				    = getattr(
                self.model_tester ,						"expected_num_hidden_layers" ,						len(self.model_tester.depths) + 1)
            self.assertEqual(len(__lowerCamelCase) ,						__lowerCamelCase)

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

            _A      :       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  _lowerCamelCase (						self)			->				Dict:
            _A    ,			_A      :       List[str]				    = self.model_tester.prepare_config_and_inputs_for_common()

            _A      :       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)
            )

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

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _A      :       Optional[int]				    = True

                   self.check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Tuple:
            _A    ,			_A      :       List[str]				    = self.model_tester.prepare_config_and_inputs_for_common()
            _A      :       Optional[int]				    = 3

            _A      :       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)
            )
            _A      :       Optional[int]				    = (
                config.patch_size
                if isinstance(config.patch_size ,						collections.abc.Iterable)
                else (config.patch_size, config.patch_size)
            )

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

            for model_class in self.all_model_classes:
                   _A      :       List[Any]				    = True
                   self.check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						(padded_height, padded_width))

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


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


     @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
     def  _lowerCamelCase (						self)			->				List[str]:
            pass


     @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
     def  _lowerCamelCase (						self)			->				str:
            pass


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A    ,			_A      :       Any				    = self.model_tester.prepare_config_and_inputs_for_common()

            def set_nan_tensor_to_zero(__lowerCamelCase):
                   _A      :       Optional[int]				    = 0
                   return t

            def check_equivalence(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase={}):
                   with torch.no_grad():
                          _A      :       Any				    = model(**__lowerCamelCase ,						return_dict=__lowerCamelCase ,						**__lowerCamelCase)
                          _A      :       int				    = model(**__lowerCamelCase ,						return_dict=__lowerCamelCase ,						**__lowerCamelCase).to_tuple()

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

                          recursive_check(__lowerCamelCase ,						__lowerCamelCase)

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

                   _A      :       str				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase)
                   _A      :       Tuple				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase)
                   check_equivalence(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

                   _A      :       Any				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase ,						return_labels=__lowerCamelCase)
                   _A      :       List[Any]				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase ,						return_labels=__lowerCamelCase)
                   check_equivalence(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

                   _A      :       List[Any]				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase)
                   _A      :       str				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase)
                   check_equivalence(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						{"output_hidden_states": True})

                   _A      :       Union[str, Any]				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase ,						return_labels=__lowerCamelCase)
                   _A      :       Optional[Any]				    = self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase ,						return_labels=__lowerCamelCase)
                   check_equivalence(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						{"output_hidden_states": True})







@require_torch
class      lowerCAmelCase__       (				unittest.TestCase     ,    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= (MaskFormerSwinBackbone,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE  			= MaskFormerSwinConfig


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Tuple				    = MaskFormerSwinModelTester(self)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A    ,			_A      :       List[Any]				    = self.model_tester.prepare_config_and_inputs_for_common()
            _A      :       Union[str, Any]				    = inputs_dict["pixel_values"].shape[0]

            for backbone_class in self.all_model_classes:
                   _A      :       Optional[Any]				    = backbone_class(__lowerCamelCase)
                   backbone.to(__lowerCamelCase)
                   backbone.eval()

                   _A      :       List[Any]				    = backbone(**__lowerCamelCase)

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

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