




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  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   int=13		,					_lowerCamelCase:   int=32		,					_lowerCamelCase:   Union[str, Any]=2		,					_lowerCamelCase:   str=3		,					_lowerCamelCase:   Any=16		,					_lowerCamelCase:   Tuple=[1, 2, 1]		,					_lowerCamelCase:   Dict=[2, 2, 4]		,					_lowerCamelCase:   Dict=2		,					_lowerCamelCase:   List[str]=2.0		,					_lowerCamelCase:   Tuple=True		,					_lowerCamelCase:   Union[str, Any]=0.0		,					_lowerCamelCase:   Dict=0.0		,					_lowerCamelCase:   List[str]=0.1		,					_lowerCamelCase:   Optional[Any]="gelu"		,					_lowerCamelCase:   List[Any]=False		,					_lowerCamelCase:   Tuple=True		,					_lowerCamelCase:   Union[str, Any]=0.02		,					_lowerCamelCase:   List[str]=1E-5		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Dict=10		,					_lowerCamelCase:   List[Any]=8		,					_lowerCamelCase:   Any=["stage1", "stage2", "stage3"]		,					_lowerCamelCase:   str=[1, 2, 3]		,					):
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    patch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    embed_dim
											SCREAMING_SNAKE_CASE_   	=    depths
											SCREAMING_SNAKE_CASE_   	=    num_heads
											SCREAMING_SNAKE_CASE_   	=    window_size
											SCREAMING_SNAKE_CASE_   	=    mlp_ratio
											SCREAMING_SNAKE_CASE_   	=    qkv_bias
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    drop_path_rate
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    use_absolute_embeddings
											SCREAMING_SNAKE_CASE_   	=    patch_norm
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    is_training
											SCREAMING_SNAKE_CASE_   	=    scope
											SCREAMING_SNAKE_CASE_   	=    use_labels
											SCREAMING_SNAKE_CASE_   	=    type_sequence_label_size
											SCREAMING_SNAKE_CASE_   	=    encoder_stride
											SCREAMING_SNAKE_CASE_   	=    out_features
											SCREAMING_SNAKE_CASE_   	=    out_indices


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

											SCREAMING_SNAKE_CASE_   	=    self.get_config()

											return config, pixel_values, labels


				def     _A							(							self:   Optional[int]		):
											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:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    MaskFormerSwinModel(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
											SCREAMING_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     _A							(							self:   Dict		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    MaskFormerSwinBackbone(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    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		)		,					[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(_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    ['''stem''']
																		SCREAMING_SNAKE_CASE_   	=    MaskFormerSwinBackbone(config=_lowerCamelCase		)





				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    config_and_inputs
											SCREAMING_SNAKE_CASE_   	=    {'''pixel_values''': pixel_values}
											return config, inputs_dict






@require_torch
class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					(
				    (
				        MaskFormerSwinModel,
				        MaskFormerSwinBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__					:							Any	      =					{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					False
				SCREAMING_SNAKE_CASE__					:							Dict	      =					False
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					False
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					False
				SCREAMING_SNAKE_CASE__					:							Dict	      =					False


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    MaskFormerSwinModelTester(self		)
											SCREAMING_SNAKE_CASE_   	=    ConfigTester(self		,					config_class=_lowerCamelCase		,					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:   Tuple		):
											pass


				def     _A							(							self:   Union[str, Any]		):
											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:   Union[str, Any]		):
											return


				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*_lowerCamelCase		)


				@unittest.skip('''Swin does not use inputs_embeds'''		)
				def     _A							(							self:   List[Any]		):
											pass


				@unittest.skip('''Swin does not support feedforward chunking'''		)
				def     _A							(							self:   int		):
											pass


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

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


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE_   	=    ['''pixel_values''']
																		self.assertListEqual(arg_names[:1]		,					_lowerCamelCase		)


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


				@unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''		)
				def     _A							(							self:   Optional[int]		):
											pass


				def     _A							(							self:   str		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(**self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)		)

											SCREAMING_SNAKE_CASE_   	=    outputs.hidden_states

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

											SCREAMING_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     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

											SCREAMING_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:
																		SCREAMING_SNAKE_CASE_   	=    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"]
																		SCREAMING_SNAKE_CASE_   	=    True

																		self.check_hidden_states_output(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE_   	=    3

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

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

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_   	=    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"]
																		SCREAMING_SNAKE_CASE_   	=    True
																		self.check_hidden_states_output(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					(padded_height, padded_width)		)


				@unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''		)
				def     _A							(							self:   Tuple		):
											pass


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


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





				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

											def set_nan_tensor_to_zero(_lowerCamelCase:   Union[str, Any]		):
																		SCREAMING_SNAKE_CASE_   	=    0
																		return t

											def check_equivalence(_lowerCamelCase:   Dict		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Tuple={}		):
																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		,					return_dict=_lowerCamelCase		,					**_lowerCamelCase		)
																									SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		,					return_dict=_lowerCamelCase		,					**_lowerCamelCase		).to_tuple()

																									def recursive_check(_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   List[str]		):
																																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:
																		SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
																		model.to(_lowerCamelCase		)
																		model.eval()

																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
																		check_equivalence(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)

																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		,					return_labels=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		,					return_labels=_lowerCamelCase		)
																		check_equivalence(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)

																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
																		check_equivalence(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					{'''output_hidden_states''': True}		)

																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		,					return_labels=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		,					return_labels=_lowerCamelCase		)
																		check_equivalence(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					{'''output_hidden_states''': True}		)






@require_torch
class  __magic_name__						(  unittest.TestCase    ,  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					(MaskFormerSwinBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					MaskFormerSwinConfig


				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    MaskFormerSwinModelTester(self		)





				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE_   	=    inputs_dict['''pixel_values'''].shape[0]

											for backbone_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_   	=    backbone_class(_lowerCamelCase		)
																		backbone.to(_lowerCamelCase		)
																		backbone.eval()

																		SCREAMING_SNAKE_CASE_   	=    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
																		SCREAMING_SNAKE_CASE_   	=    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)
																																SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_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:
																									SCREAMING_SNAKE_CASE_   	=    backbone(**_lowerCamelCase		,					output_attentions=_lowerCamelCase		)
																									self.assertIsNotNone(outputs.attentions		)








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


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(*merge_sort(unsorted), sep=""",""")


