"""simple docstring"""




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, 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_FOR_IMAGE_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         LevitForImageClassification,
         LevitForImageClassificationWithTeacher,
         LevitModel,
     )
     from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.config_class(**self.inputs_dict  )
       self.parent.assertTrue(hasattr(a__				,     """hidden_sizes"""  )  )
       self.parent.assertTrue(hasattr(a__				,     """num_attention_heads"""  )  )







class 						__A							:






     def __init__(		self				,     a__				,     a__=13				,     a__=64				,     a__=3				,     a__=3				,     a__=2				,     a__=1				,     a__=16				,     a__=[128, 256, 384]				,     a__=[4, 6, 8]				,     a__=[2, 3, 4]				,     a__=[16, 16, 16]				,     a__=0				,     a__=[2, 2, 2]				,     a__=[2, 2, 2]				,     a__=0.0_2				,     a__=True				,     a__=True				,     a__=2				,     ):
       _lowerCAmelCase						:       int  			=     parent
       _lowerCAmelCase						:       List[str]  			=     batch_size
       _lowerCAmelCase						:       Optional[Any]  			=     image_size
       _lowerCAmelCase						:       Optional[int]  			=     num_channels
       _lowerCAmelCase						:       Optional[Any]  			=     kernel_size
       _lowerCAmelCase						:       str  			=     stride
       _lowerCAmelCase						:       List[Any]  			=     padding
       _lowerCAmelCase						:       Tuple  			=     hidden_sizes
       _lowerCAmelCase						:       Tuple  			=     num_attention_heads
       _lowerCAmelCase						:       Any  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     key_dim
       _lowerCAmelCase						:       Any  			=     drop_path_rate
       _lowerCAmelCase						:       Dict  			=     patch_size
       _lowerCAmelCase						:       List[Any]  			=     attention_ratio
       _lowerCAmelCase						:       Any  			=     mlp_ratio
       _lowerCAmelCase						:       Tuple  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     [
           ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       _lowerCAmelCase						:       Optional[Any]  			=     is_training
       _lowerCAmelCase						:       Dict  			=     use_labels
       _lowerCAmelCase						:       Union[str, Any]  			=     num_labels
       _lowerCAmelCase						:       List[Any]  			=     initializer_range






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       _lowerCAmelCase						:       List[str]  			=     self.get_config()
       return config, pixel_values, labels






     def     __A  (		self  ):
       return LevitConfig(
           image_size=self.image_size				,     num_channels=self.num_channels				,     kernel_size=self.kernel_size				,     stride=self.stride				,     padding=self.padding				,     patch_size=self.patch_size				,     hidden_sizes=self.hidden_sizes				,     num_attention_heads=self.num_attention_heads				,     depths=self.depths				,     key_dim=self.key_dim				,     drop_path_rate=self.drop_path_rate				,     mlp_ratio=self.mlp_ratio				,     attention_ratio=self.attention_ratio				,     initializer_range=self.initializer_range				,     down_ops=self.down_ops				,     )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     LevitModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[Any]  			=     model(a__  )
       _lowerCAmelCase						:       List[Any]  			=     (self.image_size, self.image_size)
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     image_size[0], image_size[1]
       for _ in range(4  ):
         _lowerCAmelCase						:       Tuple  			=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1  )
         _lowerCAmelCase						:       Tuple  			=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1  )
       self.parent.assertEqual(
           result.last_hidden_state.shape				,     (self.batch_size, ceil(height / 4  ) * ceil(width / 4  ), self.hidden_sizes[-1])				,     )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.num_labels
       _lowerCAmelCase						:       str  			=     LevitForImageClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     model(a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.prepare_config_and_inputs()
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     config_and_inputs
       _lowerCAmelCase						:       Tuple  			=     {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			Optional[Any]							=		(
         {
             "feature-extraction": LevitModel,
             "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     LevitModelTester(self  )
       _lowerCAmelCase						:       str  			=     ConfigTester(self				,     config_class=a__				,     has_text_modality=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       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  ):
       return






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






     @unittest.skip(reason="""Levit does not support input and output embeddings"""  )
     def     __A  (		self  ):
       pass






     @unittest.skip(reason="""Levit does not output attentions"""  )
     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       int  			=     self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase						:       Tuple  			=     ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]				,     a__  )






     def     __A  (		self  ):
       def check_hidden_states_output(a__				,     a__				,     a__  ):
         _lowerCAmelCase						:       Any  			=     model_class(a__  )
         model.to(a__  )
         model.eval()

         with torch.no_grad():
           _lowerCAmelCase						:       int  			=     model(**self._prepare_for_class(a__				,     a__  )  )

         _lowerCAmelCase						:       Optional[Any]  			=     outputs.hidden_states

         _lowerCAmelCase						:       List[str]  			=     len(self.model_tester.depths  ) + 1
         self.assertEqual(len(a__  )				,     a__  )

         _lowerCAmelCase						:       Dict  			=     (self.model_tester.image_size, self.model_tester.image_size)
         _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     image_size[0], image_size[1]
         for _ in range(4  ):
           _lowerCAmelCase						:       Union[str, Any]  			=     floor(
               (
                   (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1  )
           _lowerCAmelCase						:       Optional[Any]  			=     floor(
               (
                   (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1  )
         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]  )				,     [
                 height * width,
                 self.model_tester.hidden_sizes[0],
             ]				,     )

       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       List[str]  			=     True
         check_hidden_states_output(a__				,     a__				,     a__  )

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

         check_hidden_states_output(a__				,     a__				,     a__  )






     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
     def     __A  (		self  ):
       pass






     def     __A  (		self				,     a__				,     a__				,     a__=False  ):
       _lowerCAmelCase						:       Dict  			=     super()._prepare_for_class(a__				,     a__				,     return_labels=a__  )

       if return_labels:
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           del inputs_dict["labels"]

       return inputs_dict






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     def     __A  (		self  ):
       if not self.model_tester.is_training:
         return

       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase						:       int  			=     True

       for model_class in self.all_model_classes:
         # LevitForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(a__  )
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
           continue
         _lowerCAmelCase						:       Dict  			=     model_class(a__  )
         model.to(a__  )
         model.train()
         _lowerCAmelCase						:       Optional[Any]  			=     self._prepare_for_class(a__				,     a__				,     return_labels=a__  )
         _lowerCAmelCase						:       Optional[Any]  			=     model(**a__  ).loss
         loss.backward()






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
         return

       _lowerCAmelCase						:       List[Any]  			=     False
       _lowerCAmelCase						:       Tuple  			=     True

       for model_class in self.all_model_classes:
         if model_class in get_values(a__  ) or not model_class.supports_gradient_checkpointing:
           continue
         # LevitForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           continue
         _lowerCAmelCase						:       List[str]  			=     model_class(a__  )
         model.gradient_checkpointing_enable()
         model.to(a__  )
         model.train()
         _lowerCAmelCase						:       Optional[Any]  			=     self._prepare_for_class(a__				,     a__				,     return_labels=a__  )
         _lowerCAmelCase						:       Tuple  			=     model(**a__  ).loss
         loss.backward()






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase						:       Optional[Any]  			=     [
           {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
           {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
           {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
       ]

       for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(a__  ),
             ]
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
           continue

         for problem_type in problem_types:
           with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"  ):
             _lowerCAmelCase						:       Optional[int]  			=     problem_type["""title"""]
             _lowerCAmelCase						:       List[Any]  			=     problem_type["""num_labels"""]

             _lowerCAmelCase						:       Optional[int]  			=     model_class(a__  )
             model.to(a__  )
             model.train()

             _lowerCAmelCase						:       Optional[int]  			=     self._prepare_for_class(a__				,     a__				,     return_labels=a__  )

             if problem_type["num_labels"] > 1:
               _lowerCAmelCase						:       str  			=     inputs["""labels"""].unsqueeze(1  ).repeat(1				,     problem_type["""num_labels"""]  )

             _lowerCAmelCase						:       Any  			=     inputs["""labels"""].to(problem_type["""dtype"""]  )

             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
             # they have the same size." which is a symptom something in wrong for the regression problem.
             # See https://github.com/huggingface/transformers/issues/11780
             with warnings.catch_warnings(record=a__  ) as warning_list:
               _lowerCAmelCase						:       Optional[int]  			=     model(**a__  ).loss
             for w in warning_list:
               if "Using a target size that is different to the input size" in str(w.message  ):
                 raise ValueError(
                     F"Something is going wrong in the regression problem: intercepted {w.message}"  )

             loss.backward()






     @slow
     def     __A  (		self  ):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Optional[Any]  			=     LevitModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE     (			)       ->      Tuple:
  _lowerCAmelCase						:       str  			=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image






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






     @cached_property
     def     __A  (		self  ):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(
           a__  )

       _lowerCAmelCase						:       Tuple  			=     self.default_image_processor
       _lowerCAmelCase						:       List[str]  			=     prepare_img()
       _lowerCAmelCase						:       int  			=     image_processor(images=a__				,     return_tensors="""pt"""  ).to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       Any  			=     model(**a__  )

       # verify the logits
       _lowerCAmelCase						:       int  			=     torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape				,     a__  )

       _lowerCAmelCase						:       Dict  			=     torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]  ).to(a__  )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     a__				,     atol=1e-4  )  )





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 50	)       ->      int:
  _lowerCAmelCase						:       int  			=     [1] * (length + 1)

  for row_length in range(3			,length + 1	):
    for block_length in range(3			,row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
     print(F"""{solution() = }""")




