




"""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 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=13	,				_a=32	,				_a=2	,				_a=3	,				_a=16	,				_a=[1, 2, 1]	,				_a=[2, 2, 4]	,				_a=2	,				_a=2.0	,				_a=True	,				_a=0.0	,				_a=0.0	,				_a=0.1	,				_a="gelu"	,				_a=False	,				_a=True	,				_a=0.02	,				_a=1e-5	,				_a=True	,				_a=None	,				_a=True	,				_a=10	,				_a=8	,				_a=["stage1", "stage2", "stage3"]	,				_a=[1, 2, 3]	,				):





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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

							lowerCamelCase       =      self.get_config()

							return config, pixel_values, labels





				def      _lowerCAmelCase       ( self				):





							"""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      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      MaskFormerSwinModel(config=_a				)
							model.to(_a				)
							model.eval()
							lowerCamelCase       =      model(_a				)

							lowerCamelCase       =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
							lowerCamelCase       =      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	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      MaskFormerSwinBackbone(config=_a				)
							model.to(_a				)
							model.eval()
							lowerCamelCase       =      model(_a				)

							# 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(_a				):
										lowerCamelCase       =      ["""stem"""]
										lowerCamelCase       =      MaskFormerSwinBackbone(config=_a				)





				def      _lowerCAmelCase       ( self				):





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






@require_torch
class 					__magic_name__							( UpperCAmelCase__		,      UpperCAmelCase__		,      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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      MaskFormerSwinModelTester(self				)
							lowerCamelCase       =      ConfigTester(self	,				config_class=_a	,				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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*_a				)





				@unittest.skip("""Swin does not use inputs_embeds"""				)
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				@unittest.skip("""Swin does not support feedforward chunking"""				)
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCamelCase       =      ["""pixel_values"""]
										self.assertListEqual(arg_names[:1]	,				_a				)





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





							"""simple docstring"""
							pass





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





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      model_class(_a				)
							model.to(_a				)
							model.eval()

							with torch.no_grad():
										lowerCamelCase       =      model(**self._prepare_for_class(_a	,				_a				)				)

							lowerCamelCase       =      outputs.hidden_states

							lowerCamelCase       =      getattr(
							    self.model_tester	,				"""expected_num_hidden_layers"""	,				len(self.model_tester.depths				) + 1				)
							self.assertEqual(len(_a				)	,				_a				)

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

							lowerCamelCase       =      (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				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase       =      (
							    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:
										lowerCamelCase       =      True
										self.check_hidden_states_output(_a	,				_a	,				_a	,				_a				)

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

										self.check_hidden_states_output(_a	,				_a	,				_a	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase       =      3

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

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

							for model_class in self.all_model_classes:
										lowerCamelCase       =      True
										self.check_hidden_states_output(_a	,				_a	,				_a	,				(padded_height, padded_width)				)

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





				@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""				)
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





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





							"""simple docstring"""
							pass





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





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

							def set_nan_tensor_to_zero(_a				):
										lowerCamelCase       =      0
										return t

							def check_equivalence(_a	,				_a	,				_a	,				_a={}				):
										with torch.no_grad():
													lowerCamelCase       =      model(**_a	,				return_dict=_a	,				**_a				)
													lowerCamelCase       =      model(**_a	,				return_dict=_a	,				**_a				).to_tuple()

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

													recursive_check(_a	,				_a				)

							for model_class in self.all_model_classes:
										lowerCamelCase       =      model_class(_a				)
										model.to(_a				)
										model.eval()

										lowerCamelCase       =      self._prepare_for_class(_a	,				_a				)
										lowerCamelCase       =      self._prepare_for_class(_a	,				_a				)
										check_equivalence(_a	,				_a	,				_a				)

										lowerCamelCase       =      self._prepare_for_class(_a	,				_a	,				return_labels=_a				)
										lowerCamelCase       =      self._prepare_for_class(_a	,				_a	,				return_labels=_a				)
										check_equivalence(_a	,				_a	,				_a				)

										lowerCamelCase       =      self._prepare_for_class(_a	,				_a				)
										lowerCamelCase       =      self._prepare_for_class(_a	,				_a				)
										check_equivalence(_a	,				_a	,				_a	,				{"""output_hidden_states""": True}				)

										lowerCamelCase       =      self._prepare_for_class(_a	,				_a	,				return_labels=_a				)
										lowerCamelCase       =      self._prepare_for_class(_a	,				_a	,				return_labels=_a				)
										check_equivalence(_a	,				_a	,				_a	,				{"""output_hidden_states""": True}				)






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






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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      MaskFormerSwinModelTester(self				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase       =      inputs_dict["""pixel_values"""].shape[0]

							for backbone_class in self.all_model_classes:
										lowerCamelCase       =      backbone_class(_a				)
										backbone.to(_a				)
										backbone.eval()

										lowerCamelCase       =      backbone(**_a				)

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

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









"""simple docstring"""




from __future__ import annotations

lowerCAmelCase							:    str  							=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase							:    Dict  							=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			lowerCamelCase       =      len(snake_case__       )

			for i in range(snake_case__       ):
						lowerCamelCase       =      -1
						for j in range(i + 1 ,				snake_case__       ):
									if arr[i] < arr[j]:
												lowerCamelCase       =      arr[j]
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			for i, outer in enumerate(snake_case__       ):
						lowerCamelCase       =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												lowerCamelCase       =      inner
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      len(snake_case__       )
			lowerCamelCase       =      []
			lowerCamelCase       =      [-1] * arr_size

			for index in reversed(range(snake_case__       )       ):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									lowerCamelCase       =      stack[-1]
						stack.append(arr[index]       )
			return result


if __name__ == "__main__":
					from doctest import testmod
					from timeit import timeit

					testmod()
					print(next_greatest_element_slow(arr))
					print(next_greatest_element_fast(arr))
					print(next_greatest_element(arr))

					lowerCAmelCase							:    Dict  							=							(
					    """from __main__ import arr, next_greatest_element_slow, """
					    """next_greatest_element_fast, next_greatest_element"""
					)
					print(
					    """next_greatest_element_slow():""",
					    timeit("""next_greatest_element_slow(arr)""", setup=setup),
					)
					print(
					    """next_greatest_element_fast():""",
					    timeit("""next_greatest_element_fast(arr)""", setup=setup),
					)
					print(
					    """     next_greatest_element():""",
					    timeit("""next_greatest_element(arr)""", setup=setup),
					)



