





"""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       :






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






							def 					__a     (     self   )			->				int:
								a   :	Optional[int]			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

								a   :	Optional[Any]			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	str			=       MaskFormerSwinModel(config=UpperCamelCase_   )
								model.to(UpperCamelCase_   )
								model.eval()
								a   :	Tuple			=       model(UpperCamelCase_   )

								a   :	Dict			=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
								a   :	int			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								a   :	Optional[Any]			=       MaskFormerSwinBackbone(config=UpperCamelCase_   )
								model.to(UpperCamelCase_   )
								model.eval()
								a   :	Dict			=       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_   ):
									a   :	Tuple			=       ["stem"]
									a   :	List[str]			=       MaskFormerSwinBackbone(config=UpperCamelCase_   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	str			=       self.prepare_config_and_inputs()
								a, a, a   :	int			=       config_and_inputs
								a   :	Any			=       {"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class  __UpperCamelCase       (		__lowerCamelCase	, __lowerCamelCase	, unittest.TestCase		):
							lowerCamelCase     :      int         =(
							    (
							        MaskFormerSwinModel,
							        MaskFormerSwinBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase     :      List[str]         ={'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
							lowerCamelCase     :      Tuple         =False
							lowerCamelCase     :      List[str]         =False
							lowerCamelCase     :      Union[str, Any]         =False
							lowerCamelCase     :      str         =False
							lowerCamelCase     :      str         =False






							def 					__a     (     self   )			->				str:
								a   :	Dict			=       MaskFormerSwinModelTester(self   )
								a   :	Optional[Any]			=       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   )			->				List[Any]:
								pass






							def 					__a     (     self   )			->				Optional[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   )			->				Tuple:
								return






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase_   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	int			=       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[Any]:
								pass






							@unittest.skip("Swin does not support feedforward chunking"   )
							def 					__a     (     self   )			->				str:
								pass






							def 					__a     (     self   )			->				List[str]:
								a, a   :	str			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a   :	Optional[int]			=       model_class(UpperCamelCase_   )
									self.assertIsInstance(model.get_input_embeddings()   ,      (nn.Module)   )
									a   :	Any			=       model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(UpperCamelCase_   ,      nn.Linear   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a, a   :	int			=       self.model_tester.prepare_config_and_inputs_for_common()

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

									a   :	List[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   )			->				List[str]:
								pass






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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	List[Any]			=       model_class(UpperCamelCase_   )
								model.to(UpperCamelCase_   )
								model.eval()

								with torch.no_grad():
									a   :	Tuple			=       model(**self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   )   )

								a   :	int			=       outputs.hidden_states

								a   :	int			=       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
								a   :	List[str]			=       (
								    config.patch_size
								    if isinstance(config.patch_size   ,      collections.abc.Iterable   )
								    else (config.patch_size, config.patch_size)
								)

								a   :	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   )			->				Dict:
								a, a   :	Tuple			=       self.model_tester.prepare_config_and_inputs_for_common()

								a   :	List[str]			=       (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   ,      collections.abc.Iterable   )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
									a   :	Optional[int]			=       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"]
									a   :	Union[str, Any]			=       True

									self.check_hidden_states_output(UpperCamelCase_   ,      UpperCamelCase_   ,      UpperCamelCase_   ,      UpperCamelCase_   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a, a   :	Dict			=       self.model_tester.prepare_config_and_inputs_for_common()
								a   :	str			=       3

								a   :	List[str]			=       (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   ,      collections.abc.Iterable   )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								a   :	List[Any]			=       (
								    config.patch_size
								    if isinstance(config.patch_size   ,      collections.abc.Iterable   )
								    else (config.patch_size, config.patch_size)
								)

								a   :	Optional[int]			=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								a   :	str			=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
									a   :	Optional[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"]
									a   :	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   )			->				List[str]:
								pass






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






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






							def 					__a     (     self   )			->				int:
								a, a   :	str			=       self.model_tester.prepare_config_and_inputs_for_common()

								def set_nan_tensor_to_zero(lowerCAmelCase__   ):
									a   :	int			=       0
									return t

								def check_equivalence(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__={}   ):
									with torch.no_grad():
										a   :	Optional[int]			=       model(**UpperCamelCase_   ,      return_dict=UpperCamelCase_   ,      **UpperCamelCase_   )
										a   :	Optional[Any]			=       model(**UpperCamelCase_   ,      return_dict=UpperCamelCase_   ,      **UpperCamelCase_   ).to_tuple()

										def recursive_check(lowerCAmelCase__   ,      lowerCAmelCase__   ):
											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:
									a   :	Any			=       model_class(UpperCamelCase_   )
									model.to(UpperCamelCase_   )
									model.eval()

									a   :	Dict			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   )
									a   :	List[Any]			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   )
									check_equivalence(UpperCamelCase_   ,      UpperCamelCase_   ,      UpperCamelCase_   )

									a   :	str			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   ,      return_labels=UpperCamelCase_   )
									a   :	Optional[int]			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   ,      return_labels=UpperCamelCase_   )
									check_equivalence(UpperCamelCase_   ,      UpperCamelCase_   ,      UpperCamelCase_   )

									a   :	Dict			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   )
									a   :	Any			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   )
									check_equivalence(UpperCamelCase_   ,      UpperCamelCase_   ,      UpperCamelCase_   ,      {"output_hidden_states": True}   )

									a   :	List[str]			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   ,      return_labels=UpperCamelCase_   )
									a   :	str			=       self._prepare_for_class(UpperCamelCase_   ,      UpperCamelCase_   ,      return_labels=UpperCamelCase_   )
									check_equivalence(UpperCamelCase_   ,      UpperCamelCase_   ,      UpperCamelCase_   ,      {"output_hidden_states": True}   )


@require_torch
class  __UpperCamelCase       (		unittest.TestCase	, __lowerCamelCase		):
							lowerCamelCase     :      Dict         =(MaskFormerSwinBackbone,) if is_torch_available() else ()
							lowerCamelCase     :      Union[str, Any]         =MaskFormerSwinConfig






							def 					__a     (     self   )			->				str:
								a   :	Dict			=       MaskFormerSwinModelTester(self   )






							def 					__a     (     self   )			->				str:
								a, a   :	Any			=       self.model_tester.prepare_config_and_inputs_for_common()
								a   :	List[str]			=       inputs_dict["pixel_values"].shape[0]

								for backbone_class in self.all_model_classes:
									a   :	Union[str, Any]			=       backbone_class(UpperCamelCase_   )
									backbone.to(UpperCamelCase_   )
									backbone.eval()

									a   :	List[str]			=       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
									a   :	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)
											a, a, a   :	List[str]			=       hidden_state.shape
											self.assertTrue((h_batch_size, h_n_channels)   ,      (batch_size, n_channels)   )

            # Test output_attentions=True
									if self.has_attentions:
										a   :	List[Any]			=       backbone(**UpperCamelCase_   ,      output_attentions=UpperCamelCase_   )
										self.assertIsNotNone(outputs.attentions   )









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


