
'''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 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		def _lowercase  (    self     )				->    Union[str, Any]:
				_UpperCamelCase       : Optional[int]    =      self.config_class(**self.inputs_dict     )
				self.parent.assertTrue(hasattr(_snake_case  ,					'''hidden_sizes'''     )     )
				self.parent.assertTrue(hasattr(_snake_case  ,					'''num_encoder_blocks'''     )     )







class 			UpperCAmelCase				:





		"""simple docstring"""





		def __init__(    self  ,					_snake_case  ,					_snake_case=13  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=4  ,					_snake_case=[2, 2, 2, 2]  ,					_snake_case=[8, 4, 2, 1]  ,					_snake_case=[16, 32, 64, 128]  ,					_snake_case=[1, 4, 8, 16]  ,					_snake_case=False  ,					_snake_case=True  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.02  ,					_snake_case=3  ,					_snake_case=None  ,					)				->    List[Any]:
				_UpperCamelCase       : Union[str, Any]    =      parent
				_UpperCamelCase       : Optional[int]    =      batch_size
				_UpperCamelCase       : int    =      image_size
				_UpperCamelCase       : List[Any]    =      num_channels
				_UpperCamelCase       : Optional[Any]    =      num_encoder_blocks
				_UpperCamelCase       : Any    =      sr_ratios
				_UpperCamelCase       : Any    =      depths
				_UpperCamelCase       : str    =      hidden_sizes
				_UpperCamelCase       : Dict    =      downsampling_rates
				_UpperCamelCase       : Union[str, Any]    =      is_training
				_UpperCamelCase       : Optional[int]    =      use_labels
				_UpperCamelCase       : str    =      hidden_act
				_UpperCamelCase       : Dict    =      hidden_dropout_prob
				_UpperCamelCase       : Any    =      initializer_range
				_UpperCamelCase       : Union[str, Any]    =      num_labels
				_UpperCamelCase       : Dict    =      scope
		def _lowercase  (    self     )				->    Tuple:
				_UpperCamelCase       : Dict    =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

				_UpperCamelCase       : List[Any]    =      None
				if self.use_labels:
						_UpperCamelCase       : Optional[int]    =      ids_tensor([self.batch_size, self.image_size, self.image_size]  ,					self.num_labels     )

				_UpperCamelCase       : str    =      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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Optional[Any]:
				_UpperCamelCase       : int    =      PoolFormerModel(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				_UpperCamelCase       : Tuple    =      model(_snake_case     )
				_UpperCamelCase       : str    =      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 _lowercase  (    self     )				->    Optional[Any]:
				_UpperCamelCase       : List[str]    =      self.prepare_config_and_inputs()
				_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : Tuple    =      config_and_inputs
				_UpperCamelCase       : List[Any]    =      {'''pixel_values''': pixel_values}
				return config, inputs_dict







@require_torch
class 			UpperCAmelCase				(					a_  ,		a_  ,		unittest.TestCase ):





		"""simple docstring"""





		A__  :   Optional[Any]          =    (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
		A__  :   List[Any]          =    (
		    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		A__  :   Tuple          =    False
		A__  :   Tuple          =    False
		A__  :   Optional[Any]          =    False
		A__  :   Optional[Any]          =    False
		A__  :   Optional[Any]          =    False
		def _lowercase  (    self     )				->    List[str]:
				_UpperCamelCase       : Union[str, Any]    =      PoolFormerModelTester(self     )
				_UpperCamelCase       : Any    =      PoolFormerConfigTester(self  ,					config_class=_snake_case     )
		def _lowercase  (    self     )				->    Any:
				self.config_tester.run_common_tests()
		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Optional[Any]    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )
		@unittest.skip('''PoolFormer does not use inputs_embeds'''     )
		def _lowercase  (    self     )				->    Optional[int]:
				pass
		@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''     )
		def _lowercase  (    self     )				->    Dict:
				pass
		def _lowercase  (    self     )				->    str:







				def check_hidden_states_output(_snake_case  ,					_snake_case  ,					_snake_case     ):
						_UpperCamelCase       : Any    =      model_class(_snake_case     )
						model.to(_snake_case     )
						model.eval()

						with torch.no_grad():
								_UpperCamelCase       : List[Any]    =      model(**self._prepare_for_class(_snake_case  ,					_snake_case     )     )

						_UpperCamelCase       : Optional[Any]    =      outputs.hidden_states

						_UpperCamelCase       : List[Any]    =      self.model_tester.num_encoder_blocks
						self.assertEqual(len(_snake_case     )  ,					_snake_case     )

						# 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,
						    ]  ,					)

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

				for model_class in self.all_model_classes:
						_UpperCamelCase       : Optional[Any]    =      True
						check_hidden_states_output(_snake_case  ,					_snake_case  ,					_snake_case     )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						_UpperCamelCase       : str    =      True

						check_hidden_states_output(_snake_case  ,					_snake_case  ,					_snake_case     )
		def _lowercase  (    self     )				->    List[str]:







				if not self.model_tester.is_training:
						return

				_UpperCamelCase,							_UpperCamelCase       : Dict    =      self.model_tester.prepare_config_and_inputs_for_common()
				_UpperCamelCase       : List[str]    =      True

				for model_class in self.all_model_classes:
						if model_class in get_values(_snake_case     ):
								continue
						_UpperCamelCase       : Optional[Any]    =      model_class(_snake_case     )
						model.to(_snake_case     )
						model.train()
						_UpperCamelCase       : Union[str, Any]    =      self._prepare_for_class(_snake_case  ,					_snake_case  ,					return_labels=_snake_case     )
						_UpperCamelCase       : List[Any]    =      model(**_snake_case     ).loss
						loss.backward()
		def _lowercase  (    self     )				->    Any:
				_UpperCamelCase,							_UpperCamelCase       : Optional[int]    =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_UpperCamelCase       : str    =      model_class(_snake_case     )
						_UpperCamelCase       : Union[str, Any]    =      inspect.signature(model.forward     )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_UpperCamelCase       : Optional[Any]    =      [*signature.parameters.keys()]

						_UpperCamelCase       : Any    =      ['''pixel_values''']
						self.assertListEqual(arg_names[:1]  ,					_snake_case     )




		@slow
		def _lowercase  (    self     )				->    List[str]:







				for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_UpperCamelCase       : Optional[Any]    =      PoolFormerModel.from_pretrained(_snake_case     )
						self.assertIsNotNone(_snake_case     )







def 						snake_case__	(      ) ->      Dict:
		_UpperCamelCase       : str    =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image


@require_torch
class 			UpperCAmelCase				(					unittest.TestCase ):





		"""simple docstring"""





		@slow
		def _lowercase  (    self     )				->    Dict:
				_UpperCamelCase       : Tuple    =      PoolFormerImageProcessor()
				_UpperCamelCase       : Tuple    =      PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''     ).to(_snake_case     )

				_UpperCamelCase       : Optional[int]    =      image_processor(images=prepare_img()  ,					return_tensors='''pt'''     ).to(_snake_case     )

				# forward pass
				with torch.no_grad():
						_UpperCamelCase       : int    =      model(**_snake_case     )

				# verify the logits
				_UpperCamelCase       : List[Any]    =      torch.Size((1, 1000)     )
				self.assertEqual(outputs.logits.shape  ,					_snake_case     )

				_UpperCamelCase       : List[Any]    =      torch.tensor([-0.6_113, 0.1_685, -0.0_492]     ).to(_snake_case     )
				self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,					_snake_case  ,					atol=1E-4     )     )







'''simple docstring'''

import os







def 						snake_case__	(      ) ->      Tuple:
		_UpperCamelCase       : Dict    =      os.path.dirname(os.path.realpath(UpperCamelCase	)	)
		_UpperCamelCase       : str    =      os.path.join(UpperCamelCase			,'''triangle.txt'''	)

		with open(UpperCamelCase	) as f:
				_UpperCamelCase       : Optional[Any]    =      f.readlines()

		_UpperCamelCase       : List[str]    =      [[int(UpperCamelCase	) for y in x.rstrip('''\r\n'''	).split(''' '''	)] for x in triangle]

		for i in range(1			,len(UpperCamelCase	)	):
				for j in range(len(a[i]	)	):
						_UpperCamelCase       : str    =      a[i - 1][j] if j != len(a[i - 1]	) else 0
						_UpperCamelCase       : Any    =      a[i - 1][j - 1] if j > 0 else 0
						a[i][j] += max(UpperCamelCase			,UpperCamelCase	)
		return max(a[-1]	)


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





