

'''simple docstring'''

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
						from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import PoolFormerImageProcessor







class lowerCamelCase      (						__lowerCAmelCase							):

				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					snake_case							 =				self.config_class(**self.inputs_dict   )
					self.parent.assertTrue(hasattr(lowercase_,	'hidden_sizes'   )   )
					self.parent.assertTrue(hasattr(lowercase_,	'num_encoder_blocks'   )   )







class lowerCamelCase      :

				def __init__(					self,	lowercase_,	lowercase_=13,	lowercase_=64,	lowercase_=3,	lowercase_=4,	lowercase_=[2, 2, 2, 2],	lowercase_=[8, 4, 2, 1],	lowercase_=[16, 32, 64, 128],	lowercase_=[1, 4, 8, 16],	lowercase_=False,	lowercase_=True,	lowercase_="gelu",	lowercase_=0.1,	lowercase_=0.02,	lowercase_=3,	lowercase_=None,	)						->       Tuple:
					snake_case							 =				parent
					snake_case							 =				batch_size
					snake_case							 =				image_size
					snake_case							 =				num_channels
					snake_case							 =				num_encoder_blocks
					snake_case							 =				sr_ratios
					snake_case							 =				depths
					snake_case							 =				hidden_sizes
					snake_case							 =				downsampling_rates
					snake_case							 =				is_training
					snake_case							 =				use_labels
					snake_case							 =				hidden_act
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				initializer_range
					snake_case							 =				num_labels
					snake_case							 =				scope

				def 				_lowerCamelCase						(					self   )						->       int:
					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.image_size, self.image_size],	self.num_labels   )

					snake_case							 =				PoolFormerConfig(
					    image_size=self.image_size,	num_channels=self.num_channels,	num_encoder_blocks=self.num_encoder_blocks,	depths=self.depths,	hidden_sizes=self.hidden_sizes,	hidden_act=self.hidden_act,	hidden_dropout_prob=self.hidden_dropout_prob,	initializer_range=self.initializer_range,	)

					return config, pixel_values, labels

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_   )						->       Tuple:
					snake_case							 =				PoolFormerModel(config=lowercase_   )
					model.to(lowercase_   )
					model.eval()
					snake_case							 =				model(lowercase_   )
					snake_case							 =				snake_case							 =				self.image_size // 32.0
					self.parent.assertEqual(
					    result.last_hidden_state.shape,	(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )






				def 				_lowerCamelCase						(					self   )						->       Optional[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_           =   (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
				snake_case_           =   (
				    {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

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

				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				PoolFormerModelTester(self   )
					snake_case							 =				PoolFormerConfigTester(self,	config_class=lowercase_   )

				def 				_lowerCamelCase						(					self   )						->       str:
					self.config_tester.run_common_tests()

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

				@unittest.skip('PoolFormer does not use inputs_embeds'   )
				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					pass

				@unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'   )
				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					pass

				def 				_lowerCamelCase						(					self   )						->       List[str]:
					def check_hidden_states_output(lowercase_,	lowercase_,	lowercase_   ):
						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.hidden_states

						snake_case							 =				self.model_tester.num_encoder_blocks
						self.assertEqual(len(lowercase_   ),	lowercase_   )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]   ),	[
						        self.model_tester.hidden_sizes[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ],	)

					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						snake_case							 =				True
						check_hidden_states_output(lowercase_,	lowercase_,	lowercase_   )

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

						check_hidden_states_output(lowercase_,	lowercase_,	lowercase_   )

				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					if not self.model_tester.is_training:
						return

					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							 =				True

					for model_class in self.all_model_classes:
						if model_class in get_values(lowercase_   ):
							continue
						snake_case							 =				model_class(lowercase_   )
						model.to(lowercase_   )
						model.train()
						snake_case							 =				self._prepare_for_class(lowercase_,	lowercase_,	return_labels=lowercase_   )
						snake_case							 =				model(**lowercase_   ).loss
						loss.backward()

				def 				_lowerCamelCase						(					self   )						->       Any:
					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   )						->       Optional[Any]:
					for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							 =				PoolFormerModel.from_pretrained(lowercase_   )
						self.assertIsNotNone(lowercase_   )

def     __magic_name__       ( )			->		Optional[int]:
	snake_case							 =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







@require_torch
class lowerCamelCase      (						unittest.TestCase							):

				@slow
				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				PoolFormerImageProcessor()
					snake_case							 =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'   ).to(lowercase_   )

					snake_case							 =				image_processor(images=prepare_img(),	return_tensors='pt'   ).to(lowercase_   )

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

					# verify the logits
					snake_case							 =				torch.Size((1, 1000)   )
					self.assertEqual(outputs.logits.shape,	lowercase_   )

					snake_case							 =				torch.tensor([-0.6_113, 0.1_685, -0.0_492]   ).to(lowercase_   )
					self.assertTrue(torch.allclose(outputs.logits[0, :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()





