



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 		lowerCAmelCase_ :







		"""simple docstring"""
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=13     ,    _SCREAMING_SNAKE_CASE=32     ,    _SCREAMING_SNAKE_CASE=2     ,    _SCREAMING_SNAKE_CASE=3     ,    _SCREAMING_SNAKE_CASE=16     ,    _SCREAMING_SNAKE_CASE=[1, 2, 1]     ,    _SCREAMING_SNAKE_CASE=[2, 2, 4]     ,    _SCREAMING_SNAKE_CASE=2     ,    _SCREAMING_SNAKE_CASE=2.0     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=0.0     ,    _SCREAMING_SNAKE_CASE=0.0     ,    _SCREAMING_SNAKE_CASE=0.1     ,    _SCREAMING_SNAKE_CASE="gelu"     ,    _SCREAMING_SNAKE_CASE=False     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=0.0_2     ,    _SCREAMING_SNAKE_CASE=1e-5     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=10     ,    _SCREAMING_SNAKE_CASE=8     ,    _SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"]     ,    _SCREAMING_SNAKE_CASE=[1, 2, 3]     ,    )     ->  Optional[Any]:
									__UpperCamelCase          =  parent
									__UpperCamelCase          =  batch_size
									__UpperCamelCase          =  image_size
									__UpperCamelCase          =  patch_size
									__UpperCamelCase          =  num_channels
									__UpperCamelCase          =  embed_dim
									__UpperCamelCase          =  depths
									__UpperCamelCase          =  num_heads
									__UpperCamelCase          =  window_size
									__UpperCamelCase          =  mlp_ratio
									__UpperCamelCase          =  qkv_bias
									__UpperCamelCase          =  hidden_dropout_prob
									__UpperCamelCase          =  attention_probs_dropout_prob
									__UpperCamelCase          =  drop_path_rate
									__UpperCamelCase          =  hidden_act
									__UpperCamelCase          =  use_absolute_embeddings
									__UpperCamelCase          =  patch_norm
									__UpperCamelCase          =  layer_norm_eps
									__UpperCamelCase          =  initializer_range
									__UpperCamelCase          =  is_training
									__UpperCamelCase          =  scope
									__UpperCamelCase          =  use_labels
									__UpperCamelCase          =  type_sequence_label_size
									__UpperCamelCase          =  encoder_stride
									__UpperCamelCase          =  out_features
									__UpperCamelCase          =  out_indices
		def 						__lowercase(		self			)     ->  List[str]:
									__UpperCamelCase          =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

									__UpperCamelCase          =  self.get_config()

									return config, pixel_values, labels
		def 						__lowercase(		self			)     ->  str:
									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 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			)     ->  List[str]:
									__UpperCamelCase          =  MaskFormerSwinModel(config=__UpperCamelCase			)
									model.to(__UpperCamelCase			)
									model.eval()
									__UpperCamelCase          =  model(__UpperCamelCase			)

									__UpperCamelCase          =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
									__UpperCamelCase          =  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 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			)     ->  int:
									__UpperCamelCase          =  MaskFormerSwinBackbone(config=__UpperCamelCase			)
									model.to(__UpperCamelCase			)
									model.eval()
									__UpperCamelCase          =  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			):
																__UpperCamelCase          =  ['stem']
																__UpperCamelCase          =  MaskFormerSwinBackbone(config=__UpperCamelCase			)



		def 						__lowercase(		self			)     ->  Dict:
									__UpperCamelCase          =  self.prepare_config_and_inputs()
									__UpperCamelCase     ,				__UpperCamelCase     ,				__UpperCamelCase          =  config_and_inputs
									__UpperCamelCase          =  {'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class 		lowerCAmelCase_ (			__snake_case ,       __snake_case ,       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 						__lowercase(		self			)     ->  Optional[Any]:
									__UpperCamelCase          =  MaskFormerSwinModelTester(self			)
									__UpperCamelCase          =  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 						__lowercase(		self			)     ->  Dict:
									pass
		def 						__lowercase(		self			)     ->  Optional[int]:
									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 						__lowercase(		self			)     ->  Optional[Any]:
									return
		def 						__lowercase(		self			)     ->  List[str]:
									__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase			)
		def 						__lowercase(		self			)     ->  int:
									__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__UpperCamelCase			)
		@unittest.skip('Swin does not use inputs_embeds'			)
		def 						__lowercase(		self			)     ->  List[str]:
									pass
		@unittest.skip('Swin does not support feedforward chunking'			)
		def 						__lowercase(		self			)     ->  int:
									pass
		def 						__lowercase(		self			)     ->  Union[str, Any]:
									__UpperCamelCase     ,				__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																__UpperCamelCase          =  model_class(__UpperCamelCase			)
																self.assertIsInstance(model.get_input_embeddings()     ,    (nn.Module)			)
																__UpperCamelCase          =  model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(__UpperCamelCase     ,    nn.Linear			)			)
		def 						__lowercase(		self			)     ->  Optional[int]:
									__UpperCamelCase     ,				__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs_for_common()

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

																__UpperCamelCase          =  ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,    __UpperCamelCase			)
		@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'			)
		def 						__lowercase(		self			)     ->  List[Any]:
									pass
		@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'			)
		def 						__lowercase(		self			)     ->  Dict:
									pass
		def 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			)     ->  Union[str, Any]:
									__UpperCamelCase          =  model_class(__UpperCamelCase			)
									model.to(__UpperCamelCase			)
									model.eval()

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

									__UpperCamelCase          =  outputs.hidden_states

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

									__UpperCamelCase          =  (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 						__lowercase(		self			)     ->  Tuple:
									__UpperCamelCase     ,				__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs_for_common()

									__UpperCamelCase          =  (
									    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:
																__UpperCamelCase          =  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"]
																__UpperCamelCase          =  True

																self.check_hidden_states_output(__UpperCamelCase     ,    __UpperCamelCase     ,    __UpperCamelCase     ,    __UpperCamelCase			)
		def 						__lowercase(		self			)     ->  List[str]:
									__UpperCamelCase     ,				__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs_for_common()
									__UpperCamelCase          =  3

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

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

									for model_class in self.all_model_classes:
																__UpperCamelCase          =  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"]
																__UpperCamelCase          =  True
																self.check_hidden_states_output(__UpperCamelCase     ,    __UpperCamelCase     ,    __UpperCamelCase     ,    (padded_height, padded_width)			)
		@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'			)
		def 						__lowercase(		self			)     ->  Optional[int]:
									pass
		@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'			)
		def 						__lowercase(		self			)     ->  Any:
									pass
		@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'			)
		def 						__lowercase(		self			)     ->  List[Any]:
									pass



		def 						__lowercase(		self			)     ->  Dict:
									__UpperCamelCase     ,				__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs_for_common()

									def set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE			):
																__UpperCamelCase          =  0
																return t

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

																							def recursive_check(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE			):
																														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:
																__UpperCamelCase          =  model_class(__UpperCamelCase			)
																model.to(__UpperCamelCase			)
																model.eval()

																__UpperCamelCase          =  self._prepare_for_class(__UpperCamelCase     ,    __UpperCamelCase			)
																__UpperCamelCase          =  self._prepare_for_class(__UpperCamelCase     ,    __UpperCamelCase			)
																check_equivalence(__UpperCamelCase     ,    __UpperCamelCase     ,    __UpperCamelCase			)

																__UpperCamelCase          =  self._prepare_for_class(__UpperCamelCase     ,    __UpperCamelCase     ,    return_labels=__UpperCamelCase			)
																__UpperCamelCase          =  self._prepare_for_class(__UpperCamelCase     ,    __UpperCamelCase     ,    return_labels=__UpperCamelCase			)
																check_equivalence(__UpperCamelCase     ,    __UpperCamelCase     ,    __UpperCamelCase			)

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

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






@require_torch
class 		lowerCAmelCase_ (			unittest.TestCase ,       __snake_case ):







		"""simple docstring"""
		UpperCAmelCase__													=       (MaskFormerSwinBackbone,) if is_torch_available() else ()
		UpperCAmelCase__													=       MaskFormerSwinConfig
		def 						__lowercase(		self			)     ->  List[str]:
									__UpperCamelCase          =  MaskFormerSwinModelTester(self			)



		def 						__lowercase(		self			)     ->  int:
									__UpperCamelCase     ,				__UpperCamelCase          =  self.model_tester.prepare_config_and_inputs_for_common()
									__UpperCamelCase          =  inputs_dict['pixel_values'].shape[0]

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

																__UpperCamelCase          =  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
																__UpperCamelCase          =  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)
																														__UpperCamelCase     ,				__UpperCamelCase     ,				__UpperCamelCase          =  hidden_state.shape
																														self.assertTrue((h_batch_size, h_n_channels)     ,    (batch_size, n_channels)			)

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






from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
