




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
  from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[Any]=13 ,			UpperCAmelCase__				:     Optional[int]=32 ,			UpperCAmelCase__				:     Dict=3 ,			UpperCAmelCase__				:     Optional[int]=4 ,			UpperCAmelCase__				:     List[Any]=[10, 20, 30, 40] ,			UpperCAmelCase__				:     int=[2, 2, 3, 2] ,			UpperCAmelCase__				:     int=True ,			UpperCAmelCase__				:     str=True ,			UpperCAmelCase__				:     str=37 ,			UpperCAmelCase__				:     Dict="gelu" ,			UpperCAmelCase__				:     Optional[int]=10 ,			UpperCAmelCase__				:     Dict=0.02 ,			UpperCAmelCase__				:     Any=["stage2", "stage3", "stage4"] ,			UpperCAmelCase__				:     Dict=[2, 3, 4] ,			UpperCAmelCase__				:     List[str]=None ,			)						->List[str]:





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   image_size
           A__							=   num_channels
           A__							=   num_stages
           A__							=   hidden_sizes
           A__							=   depths
           A__							=   is_training
           A__							=   use_labels
           A__							=   intermediate_size
           A__							=   hidden_act
           A__							=   num_labels
           A__							=   initializer_range
           A__							=   out_features
           A__							=   out_indices
           A__							=   scope


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->str:





           '''simple docstring'''

           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.num_labels)

           A__							=   self.get_config()

           return config, pixel_values, labels


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->int:





           '''simple docstring'''

           return ConvNextVaConfig(
               num_channels=self.num_channels ,			hidden_sizes=self.hidden_sizes ,			depths=self.depths ,			num_stages=self.num_stages ,			hidden_act=self.hidden_act ,			is_decoder=UpperCAmelCase__ ,			initializer_range=self.initializer_range ,			out_features=self.out_features ,			out_indices=self.out_indices ,			num_labels=self.num_labels ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Optional[int])						->Tuple:





           '''simple docstring'''

           A__							=   ConvNextVaModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           A__							=   ConvNextVaForImageClassification(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__ ,			labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels))


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     str)						->Optional[int]:





           '''simple docstring'''

           A__							=   ConvNextVaBackbone(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__)

           # 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, self.hidden_sizes[1], 4, 4])

           # verify channels
           self.parent.assertEqual(len(model.channels) ,			len(config.out_features))
           self.parent.assertListEqual(model.channels ,			config.hidden_sizes[1:])

           # verify backbone works with out_features=None
           A__							=   None
           A__							=   ConvNextVaBackbone(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__)

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

           # verify channels
           self.parent.assertEqual(len(model.channels) ,			1)
           self.parent.assertListEqual(model.channels ,			[config.hidden_sizes[-1]])


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           A__							=   self.prepare_config_and_inputs()
           A__			,							A__			,							A__							=   config_and_inputs
           A__							=   {'''pixel_values''': pixel_values}
           return config, inputs_dict





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[Any]:





           '''simple docstring'''

           A__							=   self.prepare_config_and_inputs()
           A__			,							A__			,							A__							=   config_and_inputs
           A__							=   {'''pixel_values''': pixel_values, '''labels''': labels}
           return config, inputs_dict


@require_torch
class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(
        (
            ConvNextVaModel,
            ConvNextVaForImageClassification,
            ConvNextVaBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__							  =				(
        {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

           A__							=   ConvNextVaModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__ ,			has_text_modality=UpperCAmelCase__ ,			hidden_size=37)


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Any:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           return


    @unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Dict:





           '''simple docstring'''

           pass


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->List[str]:





           '''simple docstring'''

           if not self.model_tester.is_training:
                  return

           for model_class in self.all_model_classes:
                  A__			,							A__							=   self.model_tester.prepare_config_and_inputs_with_labels()
                  A__							=   True

                  if model_class.__name__ in [
                      *get_values(UpperCAmelCase__),
                      *get_values(UpperCAmelCase__),
                  ]:
                         continue

                  A__							=   model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.train()
                  A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)
                  A__							=   model(**UpperCAmelCase__).loss
                  loss.backward()


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[Any]:





           '''simple docstring'''

           if not self.model_tester.is_training:
                  return

           for model_class in self.all_model_classes:
                  A__			,							A__							=   self.model_tester.prepare_config_and_inputs_with_labels()
                  A__							=   False
                  A__							=   True

                  if (
                      model_class.__name__
                      in [*get_values(UpperCAmelCase__), *get_values(UpperCAmelCase__)]
                      or not model_class.supports_gradient_checkpointing
                  ):
                         continue

                  A__							=   model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.gradient_checkpointing_enable()
                  model.train()
                  A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__ ,			return_labels=UpperCAmelCase__)
                  A__							=   model(**UpperCAmelCase__).loss
                  loss.backward()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->int:





           '''simple docstring'''

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__							=   model_class(UpperCAmelCase__)
                  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] ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->List[Any]:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->str:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Tuple):
                  A__							=   model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.eval()

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

                  A__							=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  A__							=   self.model_tester.num_stages
                  self.assertEqual(len(UpperCAmelCase__) ,			expected_num_stages + 1)

                  # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:]) ,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,			)

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__							=   True
                  check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

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

                  check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Dict:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

           for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__							=   ConvNextVaModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE    (	)       ->	List[str]:



       """simple docstring"""

       A__							=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       return image


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

    '''simple docstring'''






    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->str:





           '''simple docstring'''

           return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''') if is_vision_available() else None





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''').to(UpperCAmelCase__)

           A__							=   self.default_image_processor
           A__							=   prepare_img()
           A__							=   preprocessor(images=UpperCAmelCase__ ,			return_tensors='''pt''').to(UpperCAmelCase__)

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

           # verify the logits
           A__							=   torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape ,			UpperCAmelCase__)

           A__							=   torch.tensor([0.9996, 0.1966, -0.4386]).to(UpperCAmelCase__)
           self.assertTrue(torch.allclose(outputs.logits[0, :3] ,			UpperCAmelCase__ ,			atol=1e-4))








def SCREAMING_SNAKE_CASE    (	lowercase_ = 50						)       ->	int:



       """simple docstring"""

       A__							=   [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() = }''')


