



'''simple docstring'''

import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
				from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import SwinaSRImageProcessor

class __snake_case:


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_=13	,       A_=32	,       A_=1	,       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_=0.0_2	,       A_=1e-5	,       A_=True	,       A_=None	,       A_=False	,       A_=2	,       )	->	Any:
							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    =	upscale

							# here we set some attributes to make tests pass
							lowerCAmelCase    =	len(A_      )
							lowerCAmelCase    =	embed_dim
							lowerCAmelCase    =	(image_size // patch_size) ** 2


						def 				__snake_case    (					self      )	->	Optional[int]:
							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 				__snake_case    (					self      )	->	Tuple:
							return SwinaSRConfig(
							    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	,       upscale=self.upscale	,       )


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	Union[str, Any]:
							lowerCAmelCase    =	SwinaSRModel(config=A_      )
							model.to(A_      )
							model.eval()
							lowerCAmelCase    =	model(A_      )

							self.parent.assertEqual(
							    result.last_hidden_state.shape	,       (self.batch_size, self.embed_dim, self.image_size, self.image_size)      )


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	str:
							lowerCAmelCase    =	SwinaSRForImageSuperResolution(A_      )
							model.to(A_      )
							model.eval()
							lowerCAmelCase    =	model(A_      )

							lowerCAmelCase    =	self.image_size * self.upscale
							self.parent.assertEqual(
							    result.reconstruction.shape	,       (self.batch_size, self.num_channels, expected_image_size, expected_image_size)      )




						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	self.prepare_config_and_inputs()
							lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	config_and_inputs
							lowerCAmelCase    =	{"""pixel_values""": pixel_values}
							return config, inputs_dict







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


						'''simple docstring'''



						UpperCAmelCase							:    int           =		(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
						UpperCAmelCase							:    int           =		{"feature-extraction": SwinaSRModel} if is_torch_available() else {}

						UpperCAmelCase							:    List[str]           =		False
						UpperCAmelCase							:    List[Any]           =		False
						UpperCAmelCase							:    List[str]           =		False
						UpperCAmelCase							:    int           =		False
						UpperCAmelCase							:    Tuple           =		False


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	SwinaSRModelTester(self      )
							lowerCAmelCase    =	ConfigTester(self	,       config_class=A_	,       embed_dim=37      )


						def 				__snake_case    (					self      )	->	Optional[int]:
							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 				__snake_case    (					self      )	->	Union[str, Any]:
							lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						def 				__snake_case    (					self      )	->	Any:
							lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_super_resolution(*A_      )


						@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""      )
						def 				__snake_case    (					self      )	->	Tuple:
							pass


						@unittest.skip(reason="""Swin2SR does not use inputs_embeds"""      )
						def 				__snake_case    (					self      )	->	List[str]:
							pass


						@unittest.skip(reason="""Swin2SR does not support training yet"""      )
						def 				__snake_case    (					self      )	->	Any:
							pass


						@unittest.skip(reason="""Swin2SR does not support training yet"""      )
						def 				__snake_case    (					self      )	->	str:
							pass


						def 				__snake_case    (					self      )	->	List[str]:
							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 				__snake_case    (					self      )	->	Any:
							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_      )


						@slow
						def 				__snake_case    (					self      )	->	int:
							for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase    =	SwinaSRModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )


						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase,   lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase    =	_config_zero_init(A_      )
							for model_class in self.all_model_classes:
								lowerCAmelCase    =	model_class(config=A_      )
								for name, param in model.named_parameters():
									if "logit_scale" in name:
										continue
									if param.requires_grad:
										self.assertIn(
										    ((param.data.mean() * 1e9).round() / 1e9).item()	,       [0.0, 1.0]	,       msg=f'Parameter {name} of model {model_class} seems not properly initialized'	,       )




						def 				__snake_case    (					self      )	->	List[Any]:
							lowerCAmelCase,   lowerCAmelCase    =	self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase    =	True

							for model_class in self.all_model_classes:
								lowerCAmelCase    =	True
								lowerCAmelCase    =	False
								lowerCAmelCase    =	True
								lowerCAmelCase    =	model_class(A_      )
								model.to(A_      )
								model.eval()
								with torch.no_grad():
									lowerCAmelCase    =	model(**self._prepare_for_class(A_	,       A_      )      )
								lowerCAmelCase    =	outputs.attentions
								lowerCAmelCase    =	len(self.model_tester.depths      )
								self.assertEqual(len(A_      )	,       A_      )

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								lowerCAmelCase    =	True
								lowerCAmelCase    =	config.window_size**2
								lowerCAmelCase    =	model_class(A_      )
								model.to(A_      )
								model.eval()
								with torch.no_grad():
									lowerCAmelCase    =	model(**self._prepare_for_class(A_	,       A_      )      )
								lowerCAmelCase    =	outputs.attentions
								self.assertEqual(len(A_      )	,       A_      )

								self.assertListEqual(
								    list(attentions[0].shape[-3:]      )	,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,       )
								lowerCAmelCase    =	len(A_      )

								# Check attention is always last and order is fine
								lowerCAmelCase    =	True
								lowerCAmelCase    =	True
								lowerCAmelCase    =	model_class(A_      )
								model.to(A_      )
								model.eval()
								with torch.no_grad():
									lowerCAmelCase    =	model(**self._prepare_for_class(A_	,       A_      )      )

								self.assertEqual(out_len + 1	,       len(A_      )      )

								lowerCAmelCase    =	outputs.attentions

								self.assertEqual(len(A_      )	,       A_      )

								self.assertListEqual(
								    list(self_attentions[0].shape[-3:]      )	,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,       )







@require_vision
@require_torch
@slow
class __snake_case(      unittest.TestCase					):


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	Any:
							lowerCAmelCase    =	SwinaSRImageProcessor()
							lowerCAmelCase    =	SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""      ).to(A_      )

							lowerCAmelCase    =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							lowerCAmelCase    =	processor(images=A_	,       return_tensors="""pt"""      ).to(A_      )

							# forward pass
							with torch.no_grad():
								lowerCAmelCase    =	model(**A_      )

							# verify the logits
							lowerCAmelCase    =	torch.Size([1, 3, 976, 1296]      )
							self.assertEqual(outputs.reconstruction.shape	,       A_      )
							lowerCAmelCase    =	torch.tensor(
							    [[0.5_4_5_8, 0.5_5_4_6, 0.5_6_3_8], [0.5_5_2_6, 0.5_5_6_5, 0.5_6_5_1], [0.5_3_9_6, 0.5_4_2_6, 0.5_6_2_1]]      ).to(A_      )
							self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]	,       A_	,       atol=1e-4      )      )




'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCAmelCase			=    TypeVar('T', bound=bool)

class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	None:
							lowerCAmelCase    =	value
							lowerCAmelCase    =	None
							lowerCAmelCase    =	None


						@property
						def 				__snake_case    (					self      )	->	T:
							return self._value




						@staticmethod
						def 				__snake_case    (					A_	,       A_      )	->	RandomizedHeapNode[T] | None:
							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								lowerCAmelCase,   lowerCAmelCase    =	roota, roota

							if random.choice([True, False]      ):
								lowerCAmelCase,   lowerCAmelCase    =	roota.right, roota.left

							lowerCAmelCase    =	RandomizedHeapNode.merge(roota.left	,       A_      )

							return roota







class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_ = ()      )	->	None:
							lowerCAmelCase    =	None

							if data:
								for item in data:
									self.insert(A_      )


						def 				__snake_case    (					self	,       A_      )	->	None:
							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root	,       RandomizedHeapNode(A_      )      )


						def 				__snake_case    (					self      )	->	T | None:
							lowerCAmelCase    =	self.top()

							if self._root is None:
								return None

							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root.left	,       self._root.right      )

							return result


						def 				__snake_case    (					self      )	->	T:
							if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""      )
							return self._root.value


						def 				__snake_case    (					self      )	->	None:
							lowerCAmelCase    =	None


						def 				__snake_case    (					self      )	->	list[Any]:
							lowerCAmelCase    =	[]
							while self:
								result.append(self.pop()      )

							return result




						def __bool__(					self      )	->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()