




import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[int]=3 ,			UpperCAmelCase__				:     List[Any]=32 ,			UpperCAmelCase__				:     int=3 ,			UpperCAmelCase__				:     Union[str, Any]=10 ,			UpperCAmelCase__				:     Optional[int]=[10, 20, 30, 40] ,			UpperCAmelCase__				:     Union[str, Any]=[1, 1, 2, 1] ,			UpperCAmelCase__				:     str=True ,			UpperCAmelCase__				:     Optional[int]=True ,			UpperCAmelCase__				:     List[str]="relu" ,			UpperCAmelCase__				:     Optional[int]=3 ,			UpperCAmelCase__				:     Optional[int]=None ,			)						->Optional[int]:





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   image_size
           A__							=   num_channels
           A__							=   embeddings_size
           A__							=   hidden_sizes
           A__							=   depths
           A__							=   is_training
           A__							=   use_labels
           A__							=   hidden_act
           A__							=   num_labels
           A__							=   scope
           A__							=   len(UpperCAmelCase__)


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





           '''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				:     Tuple)						->List[Any]:





           '''simple docstring'''

           return RegNetConfig(
               num_channels=self.num_channels ,			embeddings_size=self.embeddings_size ,			hidden_sizes=self.hidden_sizes ,			depths=self.depths ,			hidden_act=self.hidden_act ,			num_labels=self.num_labels ,			)


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





           '''simple docstring'''

           A__							=   RegNetModel(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				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     Union[str, Any])						->List[Any]:





           '''simple docstring'''

           A__							=   self.num_labels
           A__							=   RegNetForImageClassification(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[Any])						->Optional[Any]:





           '''simple docstring'''

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


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

    '''simple docstring'''






    UpperCAmelCase__							  =				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    UpperCAmelCase__							  =				(
        {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

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


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





           '''simple docstring'''

           A__							=   RegNetModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__ ,			has_text_modality=UpperCAmelCase__)


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





           '''simple docstring'''

           return


    @unittest.skip(reason='''RegNet does not use inputs_embeds''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Optional[Any]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''RegNet does not support input and output embeddings''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->List[str]:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           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				:     Union[str, Any])						->Tuple:





           '''simple docstring'''

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


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





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__							=   model_class(config=UpperCAmelCase__)
                  for name, module in model.named_modules():
                         if isinstance(UpperCAmelCase__ ,			(nn.BatchNormad, nn.GroupNorm)):
                                self.assertTrue(
                                    torch.all(module.weight == 1) ,			msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,			)
                                self.assertTrue(
                                    torch.all(module.bias == 0) ,			msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,			)


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





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str):
                  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)

                  # RegNet'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 // 2, self.model_tester.image_size // 2] ,			)

           A__							=   self.model_tester.prepare_config_and_inputs_for_common()
           A__							=   ["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         A__							=   layer_type
                         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				:     str)						->Union[str, Any]:





           '''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				:     Dict)						->str:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE    (	)       ->	Optional[Any]:



       """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[int])						->Tuple:





           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Any:





           '''simple docstring'''

           A__							=   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(UpperCAmelCase__)

           A__							=   self.default_image_processor
           A__							=   prepare_img()
           A__							=   image_processor(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([-0.4180, -1.5051, -3.4836]).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() = }''')


