




import inspect
import unittest

from transformers import ConvNextConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
 from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Tuple ,       snake_case					: Optional[int] ,       snake_case					: Optional[int]=13 ,       snake_case					: Optional[Any]=32 ,       snake_case					: Any=3 ,       snake_case					: List[str]=4 ,       snake_case					: Any=[10, 20, 30, 40] ,       snake_case					: Dict=[2, 2, 3, 2] ,       snake_case					: str=True ,       snake_case					: int=True ,       snake_case					: List[str]=37 ,       snake_case					: List[str]="gelu" ,       snake_case					: List[Any]=10 ,       snake_case					: Optional[Any]=0.02 ,       snake_case					: Dict=["stage2", "stage3", "stage4"] ,       snake_case					: Optional[Any]=[2, 3, 4] ,       snake_case					: List[str]=None ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						image_size
          __UpperCamelCase  		=						num_channels
          __UpperCamelCase  		=						num_stages
          __UpperCamelCase  		=						hidden_sizes
          __UpperCamelCase  		=						depths
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						num_labels
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						out_features
          __UpperCamelCase  		=						out_indices
          __UpperCamelCase  		=						scope







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

          __UpperCamelCase  		=						None
          if self.use_labels:
              __UpperCamelCase  		=						ids_tensor([self.batch_size] ,       self.num_labels				)

          __UpperCamelCase  		=						self.get_config()
          return config, pixel_values, labels







      def       snake_case	(     self					: int				):
          return ConvNextConfig(
              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=a_ ,       initializer_range=self.initializer_range ,       out_features=self.out_features ,       out_indices=self.out_indices ,       num_labels=self.num_labels ,       )







      def       snake_case	(     self					: Any ,       snake_case					: Union[str, Any] ,       snake_case					: List[Any] ,       snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						ConvNextModel(config=a_				)
          model.to(a_				)
          model.eval()
          __UpperCamelCase  		=						model(a_				)
          # 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       snake_case	(     self					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: Dict ,       snake_case					: Any				):
          __UpperCamelCase  		=						ConvNextForImageClassification(a_				)
          model.to(a_				)
          model.eval()
          __UpperCamelCase  		=						model(a_ ,       labels=a_				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)				)







      def       snake_case	(     self					: str ,       snake_case					: str ,       snake_case					: Dict ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						ConvNextBackbone(config=a_				)
          model.to(a_				)
          model.eval()
          __UpperCamelCase  		=						model(a_				)

          # 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
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						ConvNextBackbone(config=a_				)
          model.to(a_				)
          model.eval()
          __UpperCamelCase  		=						model(a_				)

          # 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       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			__lowerCamelCase							,			__lowerCamelCase							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: List[str]    =					(
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: Union[str, Any]    =					(
          {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__			: int    =					True
      lowerCAmelCase__			: List[str]    =					False
      lowerCAmelCase__			: Optional[int]    =					False
      lowerCAmelCase__			: Tuple    =					False
      lowerCAmelCase__			: Optional[Any]    =					False







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						ConvNextModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=a_ ,       has_text_modality=a_ ,       hidden_size=37				)







      def       snake_case	(     self					: List[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       snake_case	(     self					: Any				):
          return







      @unittest.skip(reason='''ConvNext does not use inputs_embeds'''				)
      def       snake_case	(     self					: Any				):
          pass







      @unittest.skip(reason='''ConvNext does not support input and output embeddings'''				)
      def       snake_case	(     self					: List[str]				):
          pass







      @unittest.skip(reason='''ConvNext does not use feedforward chunking'''				)
      def       snake_case	(     self					: Any				):
          pass







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase  		=						["pixel_values"]
              self.assertListEqual(arg_names[:1] ,       a_				)







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a_				)







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*a_				)







      def       snake_case	(     self					: List[Any]				):
          def check_hidden_states_output(snake_case					: str ,       snake_case					: List[str] ,       snake_case					: Optional[int]				):
              __UpperCamelCase  		=						model_class(a_				)
              model.to(a_				)
              model.eval()

              with torch.no_grad():
                  __UpperCamelCase  		=						model(**self._prepare_for_class(a_ ,       a_				)				)

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

              __UpperCamelCase  		=						self.model_tester.num_stages
              self.assertEqual(len(a_				) ,       expected_num_stages + 1				)

              # ConvNext'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] ,       )

          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						True
              check_hidden_states_output(a_ ,       a_ ,       a_				)

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

              check_hidden_states_output(a_ ,       a_ ,       a_				)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*a_				)







      @slow
      def       snake_case	(     self					: int				):
          for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						ConvNextModel.from_pretrained(a_				)
              self.assertIsNotNone(a_				)







def 					__SCREAMING_SNAKE_CASE   (  )       ->	int:

    """simple docstring"""


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

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

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: Optional[int]				):
          return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''				).to(a_				)

          __UpperCamelCase  		=						self.default_image_processor
          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=a_ ,       return_tensors='''pt'''				).to(a_				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**a_				)

          # verify the logits
          __UpperCamelCase  		=						torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape ,       a_				)

          __UpperCamelCase  		=						torch.tensor([-0.02_60, -0.47_39, 0.19_11]				).to(a_				)

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







@require_torch
class  _lowerCamelCase						(			unittest.TestCase							,			__lowerCamelCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(ConvNextBackbone,) if is_torch_available() else ()
      lowerCAmelCase__			: Dict    =					ConvNextConfig

      lowerCAmelCase__			: List[Any]    =					False







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						ConvNextModelTester(self				)











def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = False      )       ->	str:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected string as input, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )
    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected boolean as use_pascal parameter, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )

    __UpperCamelCase  		=						input_str.split('''_'''      )

    __UpperCamelCase  		=						0 if use_pascal else 1

    __UpperCamelCase  		=						words[start_index:]

    __UpperCamelCase  		=						[word[0].upper() + word[1:] for word in words_to_capitalize]

    __UpperCamelCase  		=						'''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]      )


if __name__ == "__main__":
 from doctest import testmod

 testmod()






