"""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						(__UpperCAmelCase    ):







   '''simple docstring'''





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          A__					:    Optional[int]														= self.config_class(**self.inputs_dict       )
          self.parent.assertTrue(hasattr(lowercase_      ,			"""hidden_sizes"""       )       )
          self.parent.assertTrue(hasattr(lowercase_      ,			"""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      ,			):







          '''simple docstring'''


          A__					:    str														= parent
          A__					:    Optional[Any]														= batch_size
          A__					:    int														= image_size
          A__					:    str														= num_channels
          A__					:    Optional[Any]														= num_encoder_blocks
          A__					:    Dict														= sr_ratios
          A__					:    Tuple														= depths
          A__					:    Dict														= hidden_sizes
          A__					:    int														= downsampling_rates
          A__					:    str														= is_training
          A__					:    List[Any]														= use_labels
          A__					:    List[Any]														= hidden_act
          A__					:    List[str]														= hidden_dropout_prob
          A__					:    int														= initializer_range
          A__					:    List[str]														= num_labels
          A__					:    Tuple														= scope





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


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

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

          A__					:    int														= 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      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







          '''simple docstring'''


          A__					:    Optional[Any]														= PoolFormerModel(config=lowercase_       )
          model.to(lowercase_       )
          model.eval()
          A__					:    Any														= model(lowercase_       )
          A__					:    Union[str, Any]														= 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       ):







          '''simple docstring'''


          A__					:    Tuple														= self.prepare_config_and_inputs()
          A__					:    int														= config_and_inputs
          A__					:    Optional[int]														= {'''pixel_values''': pixel_values}
          return config, inputs_dict





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







   '''simple docstring'''
   _UpperCamelCase     :	Dict				    =							(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
   _UpperCamelCase     :	Optional[Any]				    =							(
       {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCamelCase     :	Optional[int]				    =							False
   _UpperCamelCase     :	List[Any]				    =							False
   _UpperCamelCase     :	List[Any]				    =							False
   _UpperCamelCase     :	Union[str, Any]				    =							False
   _UpperCamelCase     :	Union[str, Any]				    =							False





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          A__					:    int														= PoolFormerModelTester(self       )
          A__					:    int														= PoolFormerConfigTester(self      ,			config_class=lowercase_       )





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          self.config_tester.run_common_tests()





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          A__					:    List[str]														= 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       ):







          '''simple docstring'''


          pass





   @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""       )
   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          pass





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          def check_hidden_states_output(snake_case_      ,			snake_case_      ,			snake_case_       ):
                 A__					:    str														= model_class(lowercase_       )
                 model.to(lowercase_       )
                 model.eval()

                 with torch.no_grad():
                        A__					:    str														= model(**self._prepare_for_class(lowercase_      ,			lowercase_       )       )

                 A__					:    int														= outputs.hidden_states

                 A__					:    Optional[int]														= 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,
                     ]      ,			)

          A__					:    Any														= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 A__					:    Union[str, Any]														= True
                 check_hidden_states_output(lowercase_      ,			lowercase_      ,			lowercase_       )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 A__					:    Union[str, Any]														= True

                 check_hidden_states_output(lowercase_      ,			lowercase_      ,			lowercase_       )





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          if not self.model_tester.is_training:
                 return

          A__					:    Any														= self.model_tester.prepare_config_and_inputs_for_common()
          A__					:    Any														= True

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





   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          A__					:    int														= self.model_tester.prepare_config_and_inputs_for_common()

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

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





   @slow
   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 A__					:    Dict														= PoolFormerModel.from_pretrained(lowercase_       )
                 self.assertIsNotNone(lowercase_       )





def    _A(		):
       A__					:    Any														= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
       return image



@require_torch
class 	__UpperCAmelCase						(unittest.TestCase    ):







   '''simple docstring'''





   @slow
   def 		lowerCamelCase				( self       ):







          '''simple docstring'''


          A__					:    Optional[int]														= PoolFormerImageProcessor()
          A__					:    List[str]														= PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""       ).to(lowercase_       )

          A__					:    Tuple														= image_processor(images=prepare_img()      ,			return_tensors="""pt"""       ).to(lowercase_       )

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

          # verify the logits
          A__					:    Optional[Any]														= torch.Size((1, 1_000)       )
          self.assertEqual(outputs.logits.shape      ,			lowercase_       )

          A__					:    Any														= torch.tensor([-0.61_13, 0.16_85, -0.04_92]       ).to(lowercase_       )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,			lowercase_      ,			atol=1E-4       )       )






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


if __name__ == "__main__":
				_UpperCamelCase         =		input("Enter the string: ").strip()
				_UpperCamelCase         =		find_minimum_partitions(s)
				print(F'Minimum number of partitions required for the \'{s}\' is {ans}')





