

import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
      from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class      _a				:




      """simple docstring"""

      def __init__(      self       :							Any	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							str=13	, UpperCAmelCase       :							Union[str, Any]=32	, UpperCAmelCase       :							Dict=2	, UpperCAmelCase       :							Union[str, Any]=3	, UpperCAmelCase       :							Optional[Any]=16	, UpperCAmelCase       :							int=[1, 2, 1]	, UpperCAmelCase       :							Any=[2, 2, 4]	, UpperCAmelCase       :							List[str]=2	, UpperCAmelCase       :							Any=2.0	, UpperCAmelCase       :							Optional[int]=True	, UpperCAmelCase       :							Dict=0.0	, UpperCAmelCase       :							List[str]=0.0	, UpperCAmelCase       :							Any=0.1	, UpperCAmelCase       :							str="gelu"	, UpperCAmelCase       :							Optional[int]=False	, UpperCAmelCase       :							List[Any]=True	, UpperCAmelCase       :							Optional[int]=0.02	, UpperCAmelCase       :							Optional[Any]=1E-5	, UpperCAmelCase       :							Optional[Any]=True	, UpperCAmelCase       :							Dict=None	, UpperCAmelCase       :							Dict=True	, UpperCAmelCase       :							List[str]=10	, UpperCAmelCase       :							List[Any]=8	, UpperCAmelCase       :							Any=["stage1", "stage2"]	, UpperCAmelCase       :							str=[1, 2]	, ):
            A_	     =							parent
            A_	     =							batch_size
            A_	     =							image_size
            A_	     =							patch_size
            A_	     =							num_channels
            A_	     =							embed_dim
            A_	     =							depths
            A_	     =							num_heads
            A_	     =							window_size
            A_	     =							mlp_ratio
            A_	     =							qkv_bias
            A_	     =							hidden_dropout_prob
            A_	     =							attention_probs_dropout_prob
            A_	     =							drop_path_rate
            A_	     =							hidden_act
            A_	     =							use_absolute_embeddings
            A_	     =							patch_norm
            A_	     =							layer_norm_eps
            A_	     =							initializer_range
            A_	     =							is_training
            A_	     =							scope
            A_	     =							use_labels
            A_	     =							type_sequence_label_size
            A_	     =							encoder_stride
            A_	     =							out_features
            A_	     =							out_indices





      def 		__A  (      self       :							Any  ):
            A_	     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

            A_	     =							self.get_config()

            return config, pixel_values, labels





      def 		__A  (      self       :							str  ):
            return SwinConfig(
                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	, encoder_stride=self.encoder_stride	, out_features=self.out_features	, out_indices=self.out_indices	, )





      def 		__A  (      self       :							Any	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							int  ):
            A_	     =							SwinModel(config=snake_case_  )
            model.to(snake_case_  )
            model.eval()
            A_	     =							model(snake_case_  )

            A_	     =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
            A_	     =							int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, expected_seq_len, expected_dim)  )





      def 		__A  (      self       :							Tuple	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							List[str]  ):
            A_	     =							SwinBackbone(config=snake_case_  )
            model.to(snake_case_  )
            model.eval()
            A_	     =							model(snake_case_  )

            # verify hidden states
            self.parent.assertEqual(len(result.feature_maps  )	, len(config.out_features  )  )
            self.parent.assertListEqual(list(result.feature_maps[0].shape  )	, [self.batch_size, model.channels[0], 16, 16]  )

            # verify channels
            self.parent.assertEqual(len(model.channels  )	, len(config.out_features  )  )

            # verify backbone works with out_features=None
            A_	     =							None
            A_	     =							SwinBackbone(config=snake_case_  )
            model.to(snake_case_  )
            model.eval()
            A_	     =							model(snake_case_  )

            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps  )	, 1  )
            self.parent.assertListEqual(list(result.feature_maps[0].shape  )	, [self.batch_size, model.channels[-1], 4, 4]  )

            # verify channels
            self.parent.assertEqual(len(model.channels  )	, 1  )





      def 		__A  (      self       :							str	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							Dict  ):
            A_	     =							SwinForMaskedImageModeling(config=snake_case_  )
            model.to(snake_case_  )
            model.eval()
            A_	     =							model(snake_case_  )
            self.parent.assertEqual(
                result.logits.shape	, (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

            # test greyscale images
            A_	     =							1
            A_	     =							SwinForMaskedImageModeling(snake_case_  )
            model.to(snake_case_  )
            model.eval()

            A_	     =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
            A_	     =							model(snake_case_  )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, 1, self.image_size, self.image_size)  )





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Union[str, Any]  ):
            A_	     =							self.type_sequence_label_size
            A_	     =							SwinForImageClassification(snake_case_  )
            model.to(snake_case_  )
            model.eval()
            A_	     =							model(snake_case_	, labels=snake_case_  )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)  )

            # test greyscale images
            A_	     =							1
            A_	     =							SwinForImageClassification(snake_case_  )
            model.to(snake_case_  )
            model.eval()

            A_	     =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
            A_	     =							model(snake_case_  )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)  )







      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							self.prepare_config_and_inputs()
            (
                A_							
            )	     =							config_and_inputs
            A_	     =							{'''pixel_values''': pixel_values}
            return config, inputs_dict




@require_torch
class      _a				( UpperCamelCase__    ,							UpperCamelCase__    ,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					(
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      _lowerCamelCase					: Optional[Any]					=					(
          {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      _lowerCamelCase					: Any					=					True

      _lowerCamelCase					: List[Any]					=					False
      _lowerCamelCase					: int					=					False
      _lowerCamelCase					: List[Any]					=					False





      def 		__A  (      self       :							List[Any]  ):
            A_	     =							SwinModelTester(self  )
            A_	     =							ConfigTester(self	, config_class=snake_case_	, embed_dim=37  )





      def 		__A  (      self       :							str  ):
            self.create_and_test_config_common_properties()
            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 		__A  (      self       :							Dict  ):
            return





      def 		__A  (      self       :							Tuple  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case_  )





      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."  )
      def 		__A  (      self       :							Optional[Any]  ):
            pass





      def 		__A  (      self       :							Optional[int]  ):
            super().test_training_gradient_checkpointing()





      def 		__A  (      self       :							Tuple  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*snake_case_  )





      def 		__A  (      self       :							Tuple  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_  )





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*snake_case_  )





      @unittest.skip(reason="Swin does not use inputs_embeds"  )
      def 		__A  (      self       :							int  ):
            pass





      @unittest.skip(reason="Swin Transformer does not use feedforward chunking"  )
      def 		__A  (      self       :							List[str]  ):
            pass





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							self.model_tester.prepare_config_and_inputs_for_common()

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





      def 		__A  (      self       :							int  ):
            A_	     =							self.model_tester.prepare_config_and_inputs_for_common()

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

                  A_	     =							['''pixel_values''']
                  self.assertListEqual(arg_names[:1]	, snake_case_  )





      def 		__A  (      self       :							Any  ):
            A_	     =							self.model_tester.prepare_config_and_inputs_for_common()
            A_	     =							True

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

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

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

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

                  # also another +1 for reshaped_hidden_states
                  A_	     =							1 if model_class.__name__ == '''SwinBackbone''' else 2
                  self.assertEqual(out_len + added_hidden_states	, len(snake_case_  )  )

                  A_	     =							outputs.attentions

                  self.assertEqual(len(snake_case_  )	, snake_case_  )

                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]  )	, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	, )





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							List[str]  ):
            A_	     =							model_class(snake_case_  )
            model.to(snake_case_  )
            model.eval()

            with torch.no_grad():
                  A_	     =							model(**self._prepare_for_class(snake_case_	, snake_case_  )  )

            A_	     =							outputs.hidden_states

            A_	     =							getattr(
                self.model_tester	, "expected_num_hidden_layers"	, len(self.model_tester.depths  ) + 1  )
            self.assertEqual(len(snake_case_  )	, snake_case_  )

            # Swin has a different seq_length
            A_	     =							(
                config.patch_size
                if isinstance(config.patch_size	, collections.abc.Iterable  )
                else (config.patch_size, config.patch_size)
            )

            A_	     =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]  )	, [num_patches, self.model_tester.embed_dim]	, )

            if not model_class.__name__ == "SwinBackbone":
                  A_	     =							outputs.reshaped_hidden_states
                  self.assertEqual(len(snake_case_  )	, snake_case_  )

                  A_	     =							reshaped_hidden_states[0].shape
                  A_	     =							(
                      reshaped_hidden_states[0].view(snake_case_	, snake_case_	, height * width  ).permute(0	, 2	, 1  )
                  )
                  self.assertListEqual(
                      list(reshaped_hidden_states.shape[-2:]  )	, [num_patches, self.model_tester.embed_dim]	, )





      def 		__A  (      self       :							Tuple  ):
            A_	     =							self.model_tester.prepare_config_and_inputs_for_common()

            A_	     =							(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size	, collections.abc.Iterable  )
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                  A_	     =							True
                  self.check_hidden_states_output(snake_case_	, snake_case_	, snake_case_	, snake_case_  )

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

                  self.check_hidden_states_output(snake_case_	, snake_case_	, snake_case_	, snake_case_  )





      def 		__A  (      self       :							List[str]  ):
            A_	     =							self.model_tester.prepare_config_and_inputs_for_common()
            A_	     =							3

            A_	     =							(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size	, collections.abc.Iterable  )
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            A_	     =							(
                config.patch_size
                if isinstance(config.patch_size	, collections.abc.Iterable  )
                else (config.patch_size, config.patch_size)
            )

            A_	     =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            A_	     =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                  A_	     =							True
                  self.check_hidden_states_output(snake_case_	, snake_case_	, snake_case_	, (padded_height, padded_width)  )

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  A_	     =							True
                  self.check_hidden_states_output(snake_case_	, snake_case_	, snake_case_	, (padded_height, padded_width)  )





      @slow
      def 		__A  (      self       :							Union[str, Any]  ):
            for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_	     =							SwinModel.from_pretrained(snake_case_  )
                  self.assertIsNotNone(snake_case_  )







      def 		__A  (      self       :							int  ):
            A_	     =							self.model_tester.prepare_config_and_inputs_for_common()

            A_	     =							_config_zero_init(snake_case_  )
            for model_class in self.all_model_classes:
                  A_	     =							model_class(config=snake_case_  )
                  for name, param in model.named_parameters():
                        if "embeddings" not in name and 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'''	, )




@require_vision
@require_torch
class      _a				( unittest.TestCase							):




      """simple docstring"""

      @cached_property
      def 		__A  (      self       :							List[Any]  ):
            return (
                AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"  )
                if is_vision_available()
                else None
            )







      @slow
      def 		__A  (      self       :							Dict  ):
            A_	     =							SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"  ).to(snake_case_  )
            A_	     =							self.default_image_processor

            A_	     =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
            A_	     =							image_processor(images=snake_case_	, return_tensors="pt"  ).to(snake_case_  )

            # forward pass
            with torch.no_grad():
                  A_	     =							model(**snake_case_  )

            # verify the logits
            A_	     =							torch.Size((1, 1000)  )
            self.assertEqual(outputs.logits.shape	, snake_case_  )
            A_	     =							torch.tensor([-0.0_948, -0.6_454, -0.0_921]  ).to(snake_case_  )
            self.assertTrue(torch.allclose(outputs.logits[0, :3]	, snake_case_	, atol=1E-4  )  )




@require_torch
class      _a				( unittest.TestCase    ,							UpperCamelCase__							):




      """simple docstring"""

      _lowerCamelCase					: Any					=					(SwinBackbone,) if is_torch_available() else ()
      _lowerCamelCase					: Optional[Any]					=					SwinConfig





      def 		__A  (      self       :							Optional[Any]  ):
            A_	     =							SwinModelTester(self  )


def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							len(__UpperCamelCase						)
      A_	     =							[0] * length
      A_	     =							[[False for i in range(__UpperCamelCase						)] for j in range(__UpperCamelCase						)]
      for i, c in enumerate(__UpperCamelCase						):
            A_	     =							i
            for j in range(i + 1						):
                  if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
                        A_	     =							True
                        A_	     =							min(__UpperCamelCase							,0 if j == 0 else (cut[j - 1] + 1)						)
            A_	     =							mincut
      return cut[length - 1]


if __name__ == "__main__":
      __a						:Union[str, Any]            = input('Enter the string: ').strip()
      __a						:Tuple            = find_minimum_partitions(s)
      print(F"Minimum number of partitions required for the '{s}' is {ans}")