"""simple docstring"""



import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import (
       FocalNetBackbone,
       FocalNetForImageClassification,
       FocalNetForMaskedImageModeling,
       FocalNetModel,
   )
   from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=13  ,						lowerCamelCase=32  ,						lowerCamelCase=2  ,						lowerCamelCase=3  ,						lowerCamelCase=16  ,						lowerCamelCase=[32, 64, 128]  ,						lowerCamelCase=[1, 2, 1]  ,						lowerCamelCase=[2, 2, 4]  ,						lowerCamelCase=2  ,						lowerCamelCase=2.0  ,						lowerCamelCase=True  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=False  ,						lowerCamelCase=True  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-5  ,						lowerCamelCase=True  ,						lowerCamelCase=None  ,						lowerCamelCase=True  ,						lowerCamelCase=10  ,						lowerCamelCase=8  ,						lowerCamelCase=["stage1", "stage2"]  ,						lowerCamelCase=[1, 2]  ,						):
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  image_size
        __a     				=  patch_size
        __a     				=  num_channels
        __a     				=  embed_dim
        __a     				=  hidden_sizes
        __a     				=  depths
        __a     				=  num_heads
        __a     				=  window_size
        __a     				=  mlp_ratio
        __a     				=  qkv_bias
        __a     				=  hidden_dropout_prob
        __a     				=  attention_probs_dropout_prob
        __a     				=  drop_path_rate
        __a     				=  hidden_act
        __a     				=  use_absolute_embeddings
        __a     				=  patch_norm
        __a     				=  layer_norm_eps
        __a     				=  initializer_range
        __a     				=  is_training
        __a     				=  scope
        __a     				=  use_labels
        __a     				=  type_sequence_label_size
        __a     				=  encoder_stride
        __a     				=  out_features
        __a     				=  out_indices


 def 		a__ (				self		):
        __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.type_sequence_label_size		)

        __a     				=  self.get_config()

        return config, pixel_values, labels


 def 		a__ (				self		):
        return FocalNetConfig(
            image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						embed_dim=self.embed_dim  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						num_heads=self.num_heads  ,						window_size=self.window_size  ,						mlp_ratio=self.mlp_ratio  ,						qkv_bias=self.qkv_bias  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						drop_path_rate=self.drop_path_rate  ,						hidden_act=self.hidden_act  ,						use_absolute_embeddings=self.use_absolute_embeddings  ,						path_norm=self.patch_norm  ,						layer_norm_eps=self.layer_norm_eps  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						out_features=self.out_features  ,						out_indices=self.out_indices  ,						)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  FocalNetModel(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(lowerCamelCase		)

        __a     				=  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
        __a     				=  int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

        self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, expected_seq_len, expected_dim)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  FocalNetBackbone(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(lowerCamelCase		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		)  ,						len(config.out_features		)		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		)  ,						[self.batch_size, self.image_size, 8, 8]		)

        # 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     				=  FocalNetBackbone(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(lowerCamelCase		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		)  ,						1		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		)  ,						[self.batch_size, self.image_size * 2, 4, 4]		)

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  FocalNetForMaskedImageModeling(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(lowerCamelCase		)
        self.parent.assertEqual(
            result.reconstruction.shape  ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

        # test greyscale images
        __a     				=  1
        __a     				=  FocalNetForMaskedImageModeling(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        __a     				=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        __a     				=  model(lowerCamelCase		)
        self.parent.assertEqual(result.reconstruction.shape  ,						(self.batch_size, 1, self.image_size, self.image_size)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.type_sequence_label_size
        __a     				=  FocalNetForImageClassification(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(lowerCamelCase  ,						labels=lowerCamelCase		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        __a     				=  1
        __a     				=  FocalNetForImageClassification(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        __a     				=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        __a     				=  model(lowerCamelCase		)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)		)






 def 		a__ (				self		):
        __a     				=  self.prepare_config_and_inputs()

        __a				, __a				, __a     				=  config_and_inputs
        __a     				=  {"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			Union[str, Any]           =		(
     (
         FocalNetModel,
         FocalNetForImageClassification,
         FocalNetForMaskedImageModeling,
         FocalNetBackbone,
     )
     if is_torch_available()
     else ()
 )
 _snake_case   :			Tuple           =		(
     {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
     if is_torch_available()
     else {}
 )
 _snake_case   :			str           =		False

 _snake_case   :			List[str]           =		False
 _snake_case   :			Tuple           =		False
 _snake_case   :			Dict           =		False
 _snake_case   :			int           =		False


 def 		a__ (				self		):
        __a     				=  FocalNetModelTester(self		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase  ,						embed_dim=37  ,						has_text_modality=lowerCamelCase		)


 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


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


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


 @unittest.skip(reason="FocalNet does not use feedforward chunking"		)
 def 		a__ (				self		):
        pass


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
               __a     				=  model_class(lowerCamelCase		)
               self.assertIsInstance(model.get_input_embeddings()  ,						(nn.Module)		)
               __a     				=  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase  ,						nn.Linear		)		)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
               __a     				=  model_class(lowerCamelCase		)
               __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]  ,						lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        with torch.no_grad():
               __a     				=  model(**self._prepare_for_class(lowerCamelCase  ,						lowerCamelCase		)		)

        __a     				=  outputs.hidden_states

        __a     				=  getattr(
            self.model_tester  ,						"expected_num_hidden_layers"  ,						len(self.model_tester.depths		) + 1		)
        self.assertEqual(len(lowerCamelCase		)  ,						lowerCamelCase		)

        # FocalNet has a different seq_length
        __a     				=  (
            config.patch_size
            if isinstance(config.patch_size  ,						collections.abc.Iterable		)
            else (config.patch_size, config.patch_size)
        )

        __a     				=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]		)  ,						[num_patches, self.model_tester.embed_dim]  ,						)

        __a     				=  outputs.reshaped_hidden_states
        self.assertEqual(len(lowerCamelCase		)  ,						lowerCamelCase		)

        __a				, __a				, __a				, __a     				=  reshaped_hidden_states[0].shape
        __a     				=  (
            reshaped_hidden_states[0].view(lowerCamelCase  ,						lowerCamelCase  ,						height * width		).permute(0  ,						2  ,						1		)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]		)  ,						[num_patches, self.model_tester.embed_dim]  ,						)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        __a     				=  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,						collections.abc.Iterable		)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes[:-1]:
               __a     				=  True
               self.check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

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

               self.check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()
        __a     				=  3

        __a     				=  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,						collections.abc.Iterable		)
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        __a     				=  (
            config.patch_size
            if isinstance(config.patch_size  ,						collections.abc.Iterable		)
            else (config.patch_size, config.patch_size)
        )

        __a     				=  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        __a     				=  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes[:-1]:
               __a     				=  True
               self.check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						(padded_height, padded_width)		)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a     				=  True
               self.check_hidden_states_output(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						(padded_height, padded_width)		)


 @slow
 def 		a__ (				self		):
        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a     				=  FocalNetModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)






 def 		a__ (				self		):
        __a				, __a     				=  self.model_tester.prepare_config_and_inputs_for_common()

        __a     				=  _config_zero_init(lowerCamelCase		)
        for model_class in self.all_model_classes:
               __a     				=  model_class(config=lowerCamelCase		)
               for name, param in model.named_parameters():
                      if "embeddings" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()  ,						[0.0, 1.0]  ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"  ,						)






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


 @cached_property
 def 		a__ (				self		):
        # TODO update organization
        return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"		) if is_vision_available() else None






 @slow
 def 		a__ (				self		):
        __a     				=  FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"		).to(lowerCamelCase		)
        __a     				=  self.default_image_processor

        __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
        __a     				=  image_processor(images=lowerCamelCase  ,						return_tensors="pt"		).to(lowerCamelCase		)

        # forward pass
        with torch.no_grad():
               __a     				=  model(**lowerCamelCase		)

        # verify the logits
        __a     				=  torch.Size((1, 1000)		)
        self.assertEqual(outputs.logits.shape  ,						lowerCamelCase		)
        __a     				=  torch.tensor([0.2166, -0.4368, 0.2191]		).to(lowerCamelCase		)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)
        self.assertTrue(outputs.logits.argmax(dim=-1		).item()  ,						281		)






@require_torch
class 				snake_case__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			Union[str, Any]           =		(FocalNetBackbone,) if is_torch_available() else ()
 _snake_case   :			int           =		FocalNetConfig

 _snake_case   :			int           =		False


 def 		a__ (				self		):
        __a     				=  FocalNetModelTester(self		)

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

        if not valid_images(lowerCamelCase		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)

        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        __a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

        if do_rescale:
               __a     				=  [self.rescale(image=lowerCamelCase  ,						scale=lowerCamelCase		) for image in images]

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
