




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






							"""simple docstring"""






							snake_case_		    =     parent
							snake_case_		    =     batch_size
							snake_case_		    =     image_size
							snake_case_		    =     patch_size
							snake_case_		    =     num_channels
							snake_case_		    =     embed_dim
							snake_case_		    =     depths
							snake_case_		    =     num_heads
							snake_case_		    =     window_size
							snake_case_		    =     mlp_ratio
							snake_case_		    =     qkv_bias
							snake_case_		    =     hidden_dropout_prob
							snake_case_		    =     attention_probs_dropout_prob
							snake_case_		    =     drop_path_rate
							snake_case_		    =     hidden_act
							snake_case_		    =     use_absolute_embeddings
							snake_case_		    =     patch_norm
							snake_case_		    =     layer_norm_eps
							snake_case_		    =     initializer_range
							snake_case_		    =     is_training
							snake_case_		    =     scope
							snake_case_		    =     use_labels
							snake_case_		    =     type_sequence_label_size
							snake_case_		    =     encoder_stride
							snake_case_		    =     out_features
							snake_case_		    =     out_indices






						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							snake_case_		    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							snake_case_		    =     self.get_config()

							return config, pixel_values, labels






						def        snake_case__						(						self							:     Optional[int]						):






							"""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        snake_case__						(						self							:     List[str]       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     List[str]       ,	__lowercase							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     MaskFormerSwinModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     model(__lowercase						)

							snake_case_		    =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
							snake_case_		    =     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        snake_case__						(						self							:     int       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     Tuple       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     MaskFormerSwinBackbone(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     model(__lowercase						)

							# 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(__lowercase						):
								snake_case_		    =     ["stem"]
								snake_case_		    =     MaskFormerSwinBackbone(config=__lowercase						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.prepare_config_and_inputs()
							snake_case_    ,      snake_case_    ,      snake_case_		    =     config_and_inputs
							snake_case_		    =     {"pixel_values": pixel_values}
							return config, inputs_dict







@require_torch
class 		UpperCAmelCase   (				UpperCAmelCase__       ,   UpperCAmelCase__       ,   unittest.TestCase			):







						'''simple docstring'''





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






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     MaskFormerSwinModelTester(self						)
							snake_case_		    =     ConfigTester(self       ,	config_class=__lowercase       ,	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        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     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        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							return






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__lowercase						)






						@unittest.skip("Swin does not use inputs_embeds"						)
						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		    =     model_class(__lowercase						)
								self.assertIsInstance(model.get_input_embeddings()       ,	(nn.Module)						)
								snake_case_		    =     model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(__lowercase       ,	nn.Linear						)						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

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

								snake_case_		    =     ["pixel_values"]
								self.assertListEqual(arg_names[:1]       ,	__lowercase						)






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






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     Dict       ,	__lowercase							:     int       ,	__lowercase							:     List[Any]						):






							"""simple docstring"""






							snake_case_		    =     model_class(__lowercase						)
							model.to(__lowercase						)
							model.eval()

							with torch.no_grad():
								snake_case_		    =     model(**self._prepare_for_class(__lowercase       ,	__lowercase						)						)

							snake_case_		    =     outputs.hidden_states

							snake_case_		    =     getattr(
							    self.model_tester       ,	"expected_num_hidden_layers"       ,	len(self.model_tester.depths						) + 1						)
							self.assertEqual(len(__lowercase						)       ,	__lowercase						)

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

							snake_case_		    =     (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        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							snake_case_		    =     (
							    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:
								snake_case_		    =     True
								self.check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_		    =     True

								self.check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_		    =     3

							snake_case_		    =     (
							    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)
							)
							snake_case_		    =     (
							    config.patch_size
							    if isinstance(config.patch_size       ,	collections.abc.Iterable						)
							    else (config.patch_size, config.patch_size)
							)

							snake_case_		    =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							snake_case_		    =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
								snake_case_		    =     True
								self.check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase       ,	(padded_height, padded_width)						)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_		    =     True
								self.check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase       ,	(padded_height, padded_width)						)






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






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							def set_nan_tensor_to_zero(__lowercase							:     Any						):
								snake_case_		    =     0
								return t

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

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

									recursive_check(__lowercase       ,	__lowercase						)

							for model_class in self.all_model_classes:
								snake_case_		    =     model_class(__lowercase						)
								model.to(__lowercase						)
								model.eval()

								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase						)
								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase						)
								check_equivalence(__lowercase       ,	__lowercase       ,	__lowercase						)

								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase       ,	return_labels=__lowercase						)
								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase       ,	return_labels=__lowercase						)
								check_equivalence(__lowercase       ,	__lowercase       ,	__lowercase						)

								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase						)
								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase						)
								check_equivalence(__lowercase       ,	__lowercase       ,	__lowercase       ,	{"output_hidden_states": True}						)

								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase       ,	return_labels=__lowercase						)
								snake_case_		    =     self._prepare_for_class(__lowercase       ,	__lowercase       ,	return_labels=__lowercase						)
								check_equivalence(__lowercase       ,	__lowercase       ,	__lowercase       ,	{"output_hidden_states": True}						)







@require_torch
class 		UpperCAmelCase   (				unittest.TestCase       ,   UpperCAmelCase__			):







						'''simple docstring'''





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






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






							"""simple docstring"""






							snake_case_		    =     MaskFormerSwinModelTester(self						)






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_		    =     inputs_dict["pixel_values"].shape[0]

							for backbone_class in self.all_model_classes:
								snake_case_		    =     backbone_class(__lowercase						)
								backbone.to(__lowercase						)
								backbone.eval()

								snake_case_		    =     backbone(**__lowercase						)

								# Test default outputs and verify feature maps
								self.assertIsInstance(outputs.feature_maps       ,	__lowercase						)
								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
								snake_case_		    =     backbone(**__lowercase       ,	output_hidden_states=__lowercase						)
								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)
										snake_case_    ,      snake_case_    ,      snake_case_		    =     hidden_state.shape
										self.assertTrue((h_batch_size, h_n_channels)       ,	(batch_size, n_channels)						)

            # Test output_attentions=True
								if self.has_attentions:
									snake_case_		    =     backbone(**__lowercase       ,	output_attentions=__lowercase						)
									self.assertIsNotNone(outputs.attentions						)






from __future__ import annotations

lowercase__				:     str 		=						[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	):







	'''simple docstring'''





	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the reference grid
	snake_case_		    =     1
	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the action grid

	snake_case_		    =     init[0]
	snake_case_		    =     init[1]
	snake_case_		    =     0
	snake_case_		    =     g + heuristic[x][y]  # cost from starting cell to destination cell
	snake_case_		    =     [[f, g, x, y]]

	snake_case_		    =     False  # flag that is set when search is complete
	snake_case_		    =     False  # flag set if we can't find expand

	while not found and not resign:
		if len(_A							) == 0:
			raise ValueError("Algorithm is unable to find solution"							)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			snake_case_		    =     cell.pop()
			snake_case_		    =     next_cell[2]
			snake_case_		    =     next_cell[3]
			snake_case_		    =     next_cell[1]

			if x == goal[0] and y == goal[1]:
				snake_case_		    =     True
			else:
				for i in range(len(_A							)							):  # to try out different valid actions
					snake_case_		    =     x + DIRECTIONS[i][0]
					snake_case_		    =     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(_A							) and ya >= 0 and ya < len(grid[0]							):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							snake_case_		    =     g + cost
							snake_case_		    =     ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]							)
							snake_case_		    =     1
							snake_case_		    =     i
	snake_case_		    =     []
	snake_case_		    =     goal[0]
	snake_case_		    =     goal[1]
	invpath.append([x, y]							)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		snake_case_		    =     x - DIRECTIONS[action[x][y]][0]
		snake_case_		    =     y - DIRECTIONS[action[x][y]][1]
		snake_case_		    =     xa
		snake_case_		    =     ya
		invpath.append([x, y]							)

	snake_case_		    =     []
	for i in range(len(_A							)							):
		path.append(invpath[len(_A							) - 1 - i]							)
	return path, action


if __name__ == "__main__":
		lowercase__				:     Union[str, Any] 		=						[
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 1, 0],
		    [0, 0, 0, 0, 1, 0],
		]

		lowercase__				:     Optional[Any] 		=						[0, 0]
		# all coordinates are given in format [y,x]
		lowercase__				:     Tuple 		=						[len(grid) - 1, len(grid[0]) - 1]
		lowercase__				:     Dict 		=						1

		# the cost map which pushes the path closer to the goal
		lowercase__				:     Any 		=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
		for i in range(len(grid)):
				for j in range(len(grid[0])):
						lowercase__				:     Union[str, Any] 		=						abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								lowercase__				:     int 		=						99

		lowercase__		,       lowercase__				:     Tuple 		=						search(grid, init, goal, cost, heuristic)

		print("ACTION MAP")
		for i in range(len(action)):
				print(action[i])

		for i in range(len(path)):
				print(path[i])
