"""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 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=13      ,			snake_case_=32      ,			snake_case_=1      ,			snake_case_=3      ,			snake_case_=16      ,			snake_case_=[1, 2, 1]      ,			snake_case_=[2, 2, 4]      ,			snake_case_=2      ,			snake_case_=2.0      ,			snake_case_=True      ,			snake_case_=0.0      ,			snake_case_=0.0      ,			snake_case_=0.1      ,			snake_case_="gelu"      ,			snake_case_=False      ,			snake_case_=True      ,			snake_case_=0.02      ,			snake_case_=1E-5      ,			snake_case_=True      ,			snake_case_=None      ,			snake_case_=False      ,			snake_case_=2      ,			):







										'''simple docstring'''


										A__					:    Any														= parent
										A__					:    Tuple														= batch_size
										A__					:    Dict														= image_size
										A__					:    int														= patch_size
										A__					:    Any														= num_channels
										A__					:    List[str]														= embed_dim
										A__					:    Union[str, Any]														= depths
										A__					:    int														= num_heads
										A__					:    List[str]														= window_size
										A__					:    int														= mlp_ratio
										A__					:    Union[str, Any]														= qkv_bias
										A__					:    Dict														= hidden_dropout_prob
										A__					:    List[Any]														= attention_probs_dropout_prob
										A__					:    Optional[Any]														= drop_path_rate
										A__					:    Dict														= hidden_act
										A__					:    Tuple														= use_absolute_embeddings
										A__					:    str														= patch_norm
										A__					:    int														= layer_norm_eps
										A__					:    Union[str, Any]														= initializer_range
										A__					:    Optional[Any]														= is_training
										A__					:    Union[str, Any]														= scope
										A__					:    Dict														= use_labels
										A__					:    Optional[Any]														= upscale

										# here we set some attributes to make tests pass
										A__					:    List[str]														= len(lowerCamelCase__       )
										A__					:    List[str]														= embed_dim
										A__					:    Optional[int]														= (image_size // patch_size) ** 2





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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

										A__					:    Optional[int]														= None
										if self.use_labels:
																	A__					:    Union[str, Any]														= ids_tensor([self.batch_size]      ,			self.type_sequence_label_size       )

										A__					:    Union[str, Any]														= self.get_config()

										return config, pixel_values, labels





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= SwinaSRModel(config=lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()
										A__					:    Tuple														= model(lowerCamelCase__       )

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= SwinaSRForImageSuperResolution(lowerCamelCase__       )
										model.to(lowerCamelCase__       )
										model.eval()
										A__					:    str														= model(lowerCamelCase__       )

										A__					:    List[str]														= self.image_size * self.upscale
										self.parent.assertEqual(
										    result.reconstruction.shape      ,			(self.batch_size, self.num_channels, expected_image_size, expected_image_size)       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    str														= self.prepare_config_and_inputs()
										A__					:    Optional[Any]														= config_and_inputs
										A__					:    Union[str, Any]														= {"pixel_values": pixel_values}
										return config, inputs_dict





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







			'''simple docstring'''
			_UpperCamelCase     :	int				    =							(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
			_UpperCamelCase     :	List[str]				    =							{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

			_UpperCamelCase     :	Tuple				    =							False
			_UpperCamelCase     :	Any				    =							False
			_UpperCamelCase     :	List[str]				    =							False
			_UpperCamelCase     :	Tuple				    =							False
			_UpperCamelCase     :	Any				    =							False





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= SwinaSRModelTester(self       )
										A__					:    Tuple														= ConfigTester(self      ,			config_class=lowerCamelCase__      ,			embed_dim=37       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										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'''


										A__					:    List[str]														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase__       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_super_resolution(*lowerCamelCase__       )





			@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""       )
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										pass





			@unittest.skip(reason="""Swin2SR does not use inputs_embeds"""       )
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										pass





			@unittest.skip(reason="""Swin2SR does not support training yet"""       )
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										pass





			@unittest.skip(reason="""Swin2SR does not support training yet"""       )
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										pass





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= self.model_tester.prepare_config_and_inputs_for_common()

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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= self.model_tester.prepare_config_and_inputs_for_common()

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

																	A__					:    Optional[int]														= ["pixel_values"]
																	self.assertListEqual(arg_names[:1]      ,			lowerCamelCase__       )





			@slow
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__					:    str														= SwinaSRModel.from_pretrained(lowerCamelCase__       )
																	self.assertIsNotNone(lowerCamelCase__       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.model_tester.prepare_config_and_inputs_for_common()

										A__					:    List[Any]														= _config_zero_init(lowerCamelCase__       )
										for model_class in self.all_model_classes:
																	A__					:    List[str]														= model_class(config=lowerCamelCase__       )
																	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 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= self.model_tester.prepare_config_and_inputs_for_common()
										A__					:    Tuple														= True

										for model_class in self.all_model_classes:
																	A__					:    str														= True
																	A__					:    Any														= False
																	A__					:    Union[str, Any]														= True
																	A__					:    Any														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.eval()
																	with torch.no_grad():
																								A__					:    int														= model(**self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__       )       )
																	A__					:    str														= outputs.attentions
																	A__					:    Any														= len(self.model_tester.depths       )
																	self.assertEqual(len(lowerCamelCase__       )      ,			lowerCamelCase__       )

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	A__					:    Dict														= True
																	A__					:    Tuple														= config.window_size**2
																	A__					:    List[Any]														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.eval()
																	with torch.no_grad():
																								A__					:    Union[str, Any]														= model(**self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__       )       )
																	A__					:    int														= outputs.attentions
																	self.assertEqual(len(lowerCamelCase__       )      ,			lowerCamelCase__       )

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]       )      ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,			)
																	A__					:    Optional[Any]														= len(lowerCamelCase__       )

																	# Check attention is always last and order is fine
																	A__					:    Tuple														= True
																	A__					:    Optional[Any]														= True
																	A__					:    Union[str, Any]														= model_class(lowerCamelCase__       )
																	model.to(lowerCamelCase__       )
																	model.eval()
																	with torch.no_grad():
																								A__					:    List[Any]														= model(**self._prepare_for_class(lowerCamelCase__      ,			lowerCamelCase__       )       )

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

																	A__					:    Optional[Any]														= outputs.attentions

																	self.assertEqual(len(lowerCamelCase__       )      ,			lowerCamelCase__       )

																	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 	__UpperCAmelCase						(unittest.TestCase    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Any														= SwinaSRImageProcessor()
										A__					:    Union[str, Any]														= SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""       ).to(lowerCamelCase__       )

										A__					:    List[Any]														= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
										A__					:    Union[str, Any]														= processor(images=lowerCamelCase__      ,			return_tensors="""pt"""       ).to(lowerCamelCase__       )

										# forward pass
										with torch.no_grad():
																	A__					:    int														= model(**lowerCamelCase__       )

										# verify the logits
										A__					:    str														= torch.Size([1, 3, 976, 1_296]       )
										self.assertEqual(outputs.reconstruction.shape      ,			lowerCamelCase__       )
										A__					:    List[Any]														= torch.tensor(
										    [[0.54_58, 0.55_46, 0.56_38], [0.55_26, 0.55_65, 0.56_51], [0.53_96, 0.54_26, 0.56_21]]       ).to(lowerCamelCase__       )
										self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]      ,			lowerCamelCase__      ,			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 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    T														= value
										A__					:    RandomizedHeapNode[T] | None														= None
										A__					:    RandomizedHeapNode[T] | None														= None





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return self._value





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if not roota:
																	return roota

										if not roota:
																	return roota

										if roota.value > roota.value:
																	A__							,       A__					:    Union[str, Any]														= roota, roota

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

										A__					:    Dict														= RandomizedHeapNode.merge(roota.left      ,			snake_case_       )

										return roota





class 	__UpperCAmelCase						(Generic[T]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_ = ()       ):







										'''simple docstring'''


										A__					:    RandomizedHeapNode[T] | None														= None

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





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= RandomizedHeapNode.merge(self._root      ,			RandomizedHeapNode(snake_case_       )       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.top()

										if self._root is None:
																	return None

										A__					:    str														= RandomizedHeapNode.merge(self._root.left      ,			self._root.right       )

										return result





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[Any]														= None





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    int														= []
										while self:
																	result.append(self.pop()       )

										return result





			def __bool__( self       ):







										'''simple docstring'''


										return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





