

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 					a__						:
			"""simple docstring"""







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






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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

							A__        =			self.get_config()

							return config, pixel_values, labels


			def      UpperCamelCase		(    self       )  ->						str:
							'''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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			MaskFormerSwinModel(config=lowercase       )
							model.to(lowercase       )
							model.eval()
							A__        =			model(lowercase       )

							A__        =			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
							A__        =			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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			MaskFormerSwinBackbone(config=lowercase       )
							model.to(lowercase       )
							model.eval()
							A__        =			model(lowercase       )

							# 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(lowercase       ):
											A__        =			["stem"]
											A__        =			MaskFormerSwinBackbone(config=lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






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

@require_torch
class 					a__						(						snake_case							,	snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       (
			    (
			        MaskFormerSwinModel,
			        MaskFormerSwinBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			__lowerCamelCase        =       {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			MaskFormerSwinModelTester(self       )
							A__        =			ConfigTester(self      ,      config_class=lowercase      ,      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      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''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      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowercase       )


			@unittest.skip("Swin does not use inputs_embeds"       )
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							pass


			@unittest.skip("Swin does not support feedforward chunking"       )
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

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


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

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

											A__        =			["pixel_values"]
											self.assertListEqual(arg_names[:1]      ,      lowercase       )


			@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn\'t support output_attentions"       )
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							pass


			@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"       )
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			model_class(lowercase       )
							model.to(lowercase       )
							model.eval()

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

							A__        =			outputs.hidden_states

							A__        =			getattr(
							    self.model_tester      ,      "expected_num_hidden_layers"      ,      len(self.model_tester.depths       ) + 1       )
							self.assertEqual(len(lowercase       )      ,      lowercase       )

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

							A__        =			(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      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

							A__        =			(
							    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__        =			True
											self.check_hidden_states_output(lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )

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

											self.check_hidden_states_output(lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()
							A__        =			3

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

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

							for model_class in self.all_model_classes:
											A__        =			True
											self.check_hidden_states_output(lowercase      ,      lowercase      ,      lowercase      ,      (padded_height, padded_width)       )

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


			@unittest.skip(reason="MaskFormerSwin doesn\'t have pretrained checkpoints"       )
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							pass


			@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"       )
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							pass


			@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"       )
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

							def set_nan_tensor_to_zero(lowercase       ):
											A__        =			0
											return t

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

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

															recursive_check(lowercase      ,      lowercase       )

							for model_class in self.all_model_classes:
											A__        =			model_class(lowercase       )
											model.to(lowercase       )
											model.eval()

											A__        =			self._prepare_for_class(lowercase      ,      lowercase       )
											A__        =			self._prepare_for_class(lowercase      ,      lowercase       )
											check_equivalence(lowercase      ,      lowercase      ,      lowercase       )

											A__        =			self._prepare_for_class(lowercase      ,      lowercase      ,      return_labels=lowercase       )
											A__        =			self._prepare_for_class(lowercase      ,      lowercase      ,      return_labels=lowercase       )
											check_equivalence(lowercase      ,      lowercase      ,      lowercase       )

											A__        =			self._prepare_for_class(lowercase      ,      lowercase       )
											A__        =			self._prepare_for_class(lowercase      ,      lowercase       )
											check_equivalence(lowercase      ,      lowercase      ,      lowercase      ,      {"output_hidden_states": True}       )

											A__        =			self._prepare_for_class(lowercase      ,      lowercase      ,      return_labels=lowercase       )
											A__        =			self._prepare_for_class(lowercase      ,      lowercase      ,      return_labels=lowercase       )
											check_equivalence(lowercase      ,      lowercase      ,      lowercase      ,      {"output_hidden_states": True}       )

@require_torch
class 					a__						(						unittest.TestCase							,	snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       (MaskFormerSwinBackbone,) if is_torch_available() else ()
			__lowerCamelCase        =       MaskFormerSwinConfig


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			MaskFormerSwinModelTester(self       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()
							A__        =			inputs_dict["pixel_values"].shape[0]

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

											A__        =			backbone(**lowercase       )

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

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




import math

lowerCAmelCase__	   = 1_0
lowerCAmelCase__	   = 7
lowerCAmelCase__	   = BALLS_PER_COLOUR * NUM_COLOURS



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 2_0      )			->      str:




				'''simple docstring'''





				A__        =			math.comb(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				A__        =			math.comb(NUM_BALLS - BALLS_PER_COLOUR	,       SCREAMING_SNAKE_CASE_      )

				A__        =			NUM_COLOURS * (1 - missing_colour / total)

				return F'{result:.9f}'


if __name__ == "__main__":
	print(solution(2_0))

