

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        a__       :







  """simple docstring"""




  def __init__(			self    : List[Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : str=1_3				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3				,				UpperCAmelCase__    : Optional[Any]=4				,				UpperCAmelCase__    : Dict=[1_0, 2_0, 3_0, 4_0]				,				UpperCAmelCase__    : int=[2, 2, 3, 2]				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Dict=3_7				,				UpperCAmelCase__    : str="gelu"				,				UpperCAmelCase__    : Dict=1_0				,				UpperCAmelCase__    : List[Any]=0.02				,				UpperCAmelCase__    : Optional[int]=["stage2", "stage3", "stage4"]				,				UpperCAmelCase__    : int=[2, 3, 4]				,				UpperCAmelCase__    : List[Any]=None				,				)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   parent
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   batch_size
        SCREAMING_SNAKE_CASE :      List[str]       =   image_size
        SCREAMING_SNAKE_CASE :      Dict       =   num_channels
        SCREAMING_SNAKE_CASE :      Dict       =   num_stages
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_sizes
        SCREAMING_SNAKE_CASE :      Any       =   depths
        SCREAMING_SNAKE_CASE :      List[str]       =   is_training
        SCREAMING_SNAKE_CASE :      Dict       =   use_labels
        SCREAMING_SNAKE_CASE :      Optional[int]       =   intermediate_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_act
        SCREAMING_SNAKE_CASE :      Tuple       =   num_labels
        SCREAMING_SNAKE_CASE :      int       =   initializer_range
        SCREAMING_SNAKE_CASE :      Dict       =   out_features
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   out_indices
        SCREAMING_SNAKE_CASE :      Tuple       =   scope


  def  _lowercase      (			self    : List[str]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        SCREAMING_SNAKE_CASE :      List[Any]       =   self.get_config()
        return config, pixel_values, labels


  def  _lowercase      (			self    : Optional[int]		)					->Optional[int]:
        """simple docstring"""







        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=UpperCAmelCase__				,				initializer_range=self.initializer_range				,				out_features=self.out_features				,				out_indices=self.out_indices				,				num_labels=self.num_labels				,				)


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Union[str, Any]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   ConvNextModel(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      str       =   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 // 3_2, self.image_size // 3_2)				,				)


  def  _lowercase      (			self    : Dict				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Tuple		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   ConvNextForImageClassification(UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(UpperCAmelCase__				,				labels=UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)		)


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : List[str]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   ConvNextBackbone(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      List[str]       =   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
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   None
        SCREAMING_SNAKE_CASE :      int       =   ConvNextBackbone(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      int       =   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  _lowercase      (			self    : Dict		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Optional[Any]       =   config_and_inputs
        SCREAMING_SNAKE_CASE :      Tuple       =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class        a__       (   UpperCAmelCase	,			UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : str			       =(
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  UpperCAmelCase__      : List[str]			       =(
      {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCAmelCase__      : str			       =True
  UpperCAmelCase__      : Optional[Any]			       =False
  UpperCAmelCase__      : int			       =False
  UpperCAmelCase__      : List[Any]			       =False
  UpperCAmelCase__      : int			       =False


  def  _lowercase      (			self    : List[str]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   ConvNextModelTester(self		)
        SCREAMING_SNAKE_CASE :      Any       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				has_text_modality=UpperCAmelCase__				,				hidden_size=3_7		)


  def  _lowercase      (			self    : str		)					->Optional[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  _lowercase      (			self    : int		)					->Optional[int]:
        """simple docstring"""







        return


  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""		)
  def  _lowercase      (			self    : Optional[int]		)					->List[Any]:
        """simple docstring"""







        pass


  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""		)
  def  _lowercase      (			self    : Any		)					->Optional[Any]:
        """simple docstring"""







        pass


  @unittest.skip(reason="""ConvNext does not use feedforward chunking"""		)
  def  _lowercase      (			self    : int		)					->Any:
        """simple docstring"""







        pass


  def  _lowercase      (			self    : Union[str, Any]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE :      str       =   ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[int]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__		)


  def  _lowercase      (			self    : Dict		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]		)					->Optional[Any]:
        """simple docstring"""







        def check_hidden_states_output(UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Union[str, Any]		):
              SCREAMING_SNAKE_CASE :      str       =   model_class(UpperCAmelCase__		)
              model.to(UpperCAmelCase__		)
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(**self._prepare_for_class(UpperCAmelCase__				,				UpperCAmelCase__		)		)

              SCREAMING_SNAKE_CASE :      List[str]       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.model_tester.num_stages
              self.assertEqual(len(UpperCAmelCase__		)				,				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]				,				)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE :      Optional[int]       =   True

              check_hidden_states_output(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[Any]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)




  @slow
  def  _lowercase      (			self    : List[Any]		)					->Dict:
        """simple docstring"""







        for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ConvNextModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)






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







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







  """simple docstring"""




  @cached_property
  def  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
        """simple docstring"""







        return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""		) if is_vision_available() else None




  @slow
  def  _lowercase      (			self    : Optional[Any]		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      str       =   self.default_image_processor
        SCREAMING_SNAKE_CASE :      List[Any]       =   prepare_img()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   image_processor(images=UpperCAmelCase__				,				return_tensors="""pt"""		).to(UpperCAmelCase__		)

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

        # verify the logits
        SCREAMING_SNAKE_CASE :      int       =   torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape				,				UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Dict       =   torch.tensor([-0.02_60, -0.47_39, 0.19_11]		).to(UpperCAmelCase__		)

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







@require_torch
class        a__       (   unittest.TestCase	,			UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : int			       =(ConvNextBackbone,) if is_torch_available() else ()
  UpperCAmelCase__      : Optional[Any]			       =ConvNextConfig

  UpperCAmelCase__      : Tuple			       =False


  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   ConvNextModelTester(self		)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :     Dict				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       ="""gpt_neox_japanese"""


  def __init__(			self    : Any				,				UpperCAmelCase__    : Any=3_2_0_0_0				,				UpperCAmelCase__    : Dict=2_5_6_0				,				UpperCAmelCase__    : List[str]=3_2				,				UpperCAmelCase__    : Optional[int]=3_2				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : List[str]="gelu"				,				UpperCAmelCase__    : Optional[int]=1.00				,				UpperCAmelCase__    : List[Any]=1_0_0_0_0				,				UpperCAmelCase__    : Tuple=2_0_4_8				,				UpperCAmelCase__    : Optional[int]=0.02				,				UpperCAmelCase__    : Dict=1e-5				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Tuple=3_1_9_9_6				,				UpperCAmelCase__    : Tuple=3_1_9_9_9				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : Union[str, Any]=0.0				,				**UpperCAmelCase__    : Optional[Any]				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(bos_token_id=UpperCAmelCase__				,				eos_token_id=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[str]       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Any       =   intermediate_multiple_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_act
        SCREAMING_SNAKE_CASE :      Any       =   rotary_pct
        SCREAMING_SNAKE_CASE :      Tuple       =   rotary_emb_base
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      str       =   use_cache
        SCREAMING_SNAKE_CASE :      Any       =   attention_dropout
        SCREAMING_SNAKE_CASE :      str       =   hidden_dropout




