






'''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					:   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					:   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					:   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					:   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					:   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					:   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					:   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					:   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'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



