

'''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 lowerCamelCase      :

				def __init__(					self,	lowercase_,	lowercase_=13,	lowercase_=32,	lowercase_=1,	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_=False,	lowercase_=2,	)						->       Union[str, Any]:
					snake_case							 =				parent
					snake_case							 =				batch_size
					snake_case							 =				image_size
					snake_case							 =				patch_size
					snake_case							 =				num_channels
					snake_case							 =				embed_dim
					snake_case							 =				depths
					snake_case							 =				num_heads
					snake_case							 =				window_size
					snake_case							 =				mlp_ratio
					snake_case							 =				qkv_bias
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				attention_probs_dropout_prob
					snake_case							 =				drop_path_rate
					snake_case							 =				hidden_act
					snake_case							 =				use_absolute_embeddings
					snake_case							 =				patch_norm
					snake_case							 =				layer_norm_eps
					snake_case							 =				initializer_range
					snake_case							 =				is_training
					snake_case							 =				scope
					snake_case							 =				use_labels
					snake_case							 =				upscale

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

				def 				_lowerCamelCase						(					self   )						->       Any:
					snake_case							 =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

					snake_case							 =				self.get_config()

					return config, pixel_values, labels

				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					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,	lowercase_,	lowercase_,	lowercase_   )						->       List[str]:
					snake_case							 =				SwinaSRModel(config=lowercase_   )
					model.to(lowercase_   )
					model.eval()
					snake_case							 =				model(lowercase_   )

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

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_   )						->       Union[str, Any]:
					snake_case							 =				SwinaSRForImageSuperResolution(lowercase_   )
					model.to(lowercase_   )
					model.eval()
					snake_case							 =				model(lowercase_   )

					snake_case							 =				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   )						->       List[Any]:
					snake_case							 =				self.prepare_config_and_inputs()
					snake_case , snake_case , snake_case							 =				config_and_inputs
					snake_case							 =				{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class lowerCamelCase      (						__lowerCAmelCase							,							__lowerCAmelCase							,							unittest.TestCase							):
				snake_case_           =   (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
				snake_case_           =   {'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

				snake_case_           =   False
				snake_case_           =   False
				snake_case_           =   False
				snake_case_           =   False
				snake_case_           =   False

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					snake_case							 =				SwinaSRModelTester(self   )
					snake_case							 =				ConfigTester(self,	config_class=lowercase_,	embed_dim=37   )

				def 				_lowerCamelCase						(					self   )						->       str:
					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   )						->       Dict:
					snake_case							 =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_   )

				def 				_lowerCamelCase						(					self   )						->       List[str]:
					snake_case							 =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_super_resolution(*lowercase_   )

				@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'   )
				def 				_lowerCamelCase						(					self   )						->       Dict:
					pass

				@unittest.skip(reason='Swin2SR does not use inputs_embeds'   )
				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					pass

				@unittest.skip(reason='Swin2SR does not support training yet'   )
				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					pass

				@unittest.skip(reason='Swin2SR does not support training yet'   )
				def 				_lowerCamelCase						(					self   )						->       int:
					pass

				def 				_lowerCamelCase						(					self   )						->       int:
					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()

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

				def 				_lowerCamelCase						(					self   )						->       str:
					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()

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

						snake_case							 =				['pixel_values']
						self.assertListEqual(arg_names[:1],	lowercase_   )

				@slow
				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							 =				SwinaSRModel.from_pretrained(lowercase_   )
						self.assertIsNotNone(lowercase_   )

				def 				_lowerCamelCase						(					self   )						->       str:
					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							 =				_config_zero_init(lowercase_   )
					for model_class in self.all_model_classes:
						snake_case							 =				model_class(config=lowercase_   )
						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   )						->       Tuple:
					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							 =				True

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

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

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

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

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

						snake_case							 =				outputs.attentions

						self.assertEqual(len(lowercase_   ),	lowercase_   )

						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 lowerCamelCase      (						unittest.TestCase							):

				def 				_lowerCamelCase						(					self   )						->       List[str]:
					snake_case							 =				SwinaSRImageProcessor()
					snake_case							 =				SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'   ).to(lowercase_   )

					snake_case							 =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
					snake_case							 =				processor(images=lowercase_,	return_tensors='pt'   ).to(lowercase_   )

					# forward pass
					with torch.no_grad():
						snake_case							 =				model(**lowercase_   )

					# verify the logits
					snake_case							 =				torch.Size([1, 3, 976, 1296]   )
					self.assertEqual(outputs.reconstruction.shape,	lowercase_   )
					snake_case							 =				torch.tensor(
					    [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]   ).to(lowercase_   )
					self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3],	lowercase_,	atol=1E-4   )   )








'''simple docstring'''

lowerCAmelCase_						   =			{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCAmelCase_						   =			{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}

def     __magic_name__       ( A					,   A					,   A							)			->		list:
	snake_case							 =				True
	snake_case							 =				[]

	for neighbour in graph[vert]:
		if not visited[neighbour]:
			connected_verts += dfs(A					,   A					,   A							)

	return [vert, *connected_verts]

def     __magic_name__       ( A							)			->		list:
	snake_case							 =				len(A							)
	snake_case							 =				graph_size * [False]
	snake_case							 =				[]

	for i in range(A							):
		if not visited[i]:
			snake_case							 =				dfs(A					,   A					,   A							)
			components_list.append(A							)

	return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()





