


'''simple docstring'''
import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _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 (
      MODEL_MAPPING,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
  )
  from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  import PIL
  from PIL import Image

  from transformers import BeitImageProcessor




class 			lowerCamelCase_   :






      def __init__(					self				:    Dict						,						_A				:    List[str]						,						_A				:    Dict=100						,						_A				:    Tuple=13						,						_A				:    str=30						,						_A				:    Optional[Any]=2						,						_A				:    List[str]=3						,						_A				:    str=True						,						_A				:    Tuple=True						,						_A				:    Any=32						,						_A				:    Dict=4						,						_A				:    Union[str, Any]=4						,						_A				:    Dict=37						,						_A				:    int="gelu"						,						_A				:    List[Any]=0.1						,						_A				:    List[Any]=0.1						,						_A				:    Union[str, Any]=10						,						_A				:    int=0.0_2						,						_A				:    Dict=3						,						_A				:    Optional[Any]=None						,						_A				:    Dict=[0, 1, 2, 3]						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				parent
          UpperCAmelCase__			:		List[str]													=				100
          UpperCAmelCase__			:		Union[str, Any]													=				batch_size
          UpperCAmelCase__			:		int													=				image_size
          UpperCAmelCase__			:		str													=				patch_size
          UpperCAmelCase__			:		Optional[int]													=				num_channels
          UpperCAmelCase__			:		List[str]													=				is_training
          UpperCAmelCase__			:		str													=				use_labels
          UpperCAmelCase__			:		List[str]													=				hidden_size
          UpperCAmelCase__			:		List[str]													=				num_hidden_layers
          UpperCAmelCase__			:		List[str]													=				num_attention_heads
          UpperCAmelCase__			:		List[str]													=				intermediate_size
          UpperCAmelCase__			:		Optional[Any]													=				hidden_act
          UpperCAmelCase__			:		Optional[Any]													=				hidden_dropout_prob
          UpperCAmelCase__			:		Optional[int]													=				attention_probs_dropout_prob
          UpperCAmelCase__			:		List[str]													=				type_sequence_label_size
          UpperCAmelCase__			:		List[str]													=				initializer_range
          UpperCAmelCase__			:		List[Any]													=				scope
          UpperCAmelCase__			:		int													=				out_indices
          UpperCAmelCase__			:		Any													=				num_labels

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase__			:		Tuple													=				(image_size // patch_size) ** 2
          UpperCAmelCase__			:		Optional[int]													=				num_patches + 1






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

          UpperCAmelCase__			:		Optional[int]													=				None
          UpperCAmelCase__			:		Optional[int]													=				None
          if self.use_labels:
              UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )
              UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size, self.image_size, self.image_size]						,						self.num_labels )

          UpperCAmelCase__			:		int													=				self.get_config()

          return config, pixel_values, labels, pixel_labels






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return BeitConfig(
              vocab_size=self.vocab_size						,						image_size=self.image_size						,						patch_size=self.patch_size						,						num_channels=self.num_channels						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						is_decoder=_A						,						initializer_range=self.initializer_range						,						out_indices=self.out_indices						,						)






      def lowercase_							(					self				:    Tuple						,						_A				:    Any						,						_A				:    Dict						,						_A				:    Tuple						,						_A				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				BeitModel(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Dict													=				model(_A )
          self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size) )






      def lowercase_							(					self				:    str						,						_A				:    Optional[int]						,						_A				:    str						,						_A				:    Optional[Any]						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				BeitForMaskedImageModeling(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Union[str, Any]													=				model(_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length - 1, self.vocab_size) )






      def lowercase_							(					self				:    List[str]						,						_A				:    Tuple						,						_A				:    Any						,						_A				:    Tuple						,						_A				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				self.type_sequence_label_size
          UpperCAmelCase__			:		Any													=				BeitForImageClassification(_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Union[str, Any]													=				model(_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size) )

          # test greyscale images
          UpperCAmelCase__			:		str													=				1
          UpperCAmelCase__			:		Dict													=				BeitForImageClassification(_A )
          model.to(_A )
          model.eval()

          UpperCAmelCase__			:		Any													=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
          UpperCAmelCase__			:		Union[str, Any]													=				model(_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size) )






      def lowercase_							(					self				:    Any						,						_A				:    Union[str, Any]						,						_A				:    Optional[int]						,						_A				:    Dict						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.num_labels
          UpperCAmelCase__			:		Any													=				BeitForSemanticSegmentation(_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				model(_A )
          self.parent.assertEqual(
              result.logits.shape						,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
          UpperCAmelCase__			:		Dict													=				model(_A						,						labels=_A )
          self.parent.assertEqual(
              result.logits.shape						,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.prepare_config_and_inputs()
          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				config_and_inputs
          UpperCAmelCase__			:		int													=				{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_torch
class 			lowerCamelCase_   (	__a     ,       __a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     (
          (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__		     =     (
          {
              'feature-extraction': BeitModel,
              'image-classification': BeitForImageClassification,
              'image-segmentation': BeitForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				BeitModelTester(self )
          UpperCAmelCase__			:		Union[str, Any]													=				ConfigTester(self						,						config_class=_A						,						has_text_modality=_A						,						hidden_size=37 )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      @unittest.skip(reason='''BEiT does not use inputs_embeds''' )
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          pass






      @require_torch_multi_gpu
      @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__			:		List[Any]													=				model_class(_A )
              self.assertIsInstance(model.get_input_embeddings()						,						(nn.Module) )
              UpperCAmelCase__			:		int													=				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_A						,						nn.Linear ) )






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase__			:		List[Any]													=				['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,						_A )






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_A )






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_A )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_A )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if not self.model_tester.is_training:
              return

          UpperCAmelCase__						,						UpperCAmelCase__			:		List[Any]													=				self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__			:		List[Any]													=				True

          for model_class in self.all_model_classes:
              # we don't test BeitForMaskedImageModeling
              if model_class in [*get_values(_A ), BeitForMaskedImageModeling]:
                  continue

              UpperCAmelCase__			:		List[Any]													=				model_class(_A )
              model.to(_A )
              model.train()
              UpperCAmelCase__			:		List[str]													=				self._prepare_for_class(_A						,						_A						,						return_labels=_A )
              UpperCAmelCase__			:		int													=				model(**_A ).loss
              loss.backward()






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
              return

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

          for model_class in self.all_model_classes:
              # we don't test BeitForMaskedImageModeling
              if (
                  model_class in [*get_values(_A ), BeitForMaskedImageModeling]
                  or not model_class.supports_gradient_checkpointing
              ):
                  continue

              UpperCAmelCase__			:		Tuple													=				model_class(_A )
              model.gradient_checkpointing_enable()
              model.to(_A )
              model.train()
              UpperCAmelCase__			:		str													=				self._prepare_for_class(_A						,						_A						,						return_labels=_A )
              UpperCAmelCase__			:		str													=				model(**_A ).loss
              loss.backward()






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase__			:		Any													=				_config_zero_init(_A )
          for model_class in self.all_model_classes:
              UpperCAmelCase__			:		Optional[int]													=				model_class(config=_A )
              for name, param in model.named_parameters():
                  # we skip lambda parameters as these require special initial values
                  # determined by config.layer_scale_init_value
                  if "lambda" in name:
                      continue
                  if 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"""						,						)






      @slow
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:		Dict													=				BeitModel.from_pretrained(_A )
              self.assertIsNotNone(_A )





def 					a__       (					)    ->	int:
    UpperCAmelCase__			:		Optional[Any]													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image







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






      @cached_property
      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' ).to(_A )

          UpperCAmelCase__			:		Tuple													=				self.default_image_processor
          UpperCAmelCase__			:		List[Any]													=				prepare_img()
          UpperCAmelCase__			:		Union[str, Any]													=				image_processor(images=_A						,						return_tensors='''pt''' ).pixel_values.to(_A )

          # prepare bool_masked_pos
          UpperCAmelCase__			:		Optional[int]													=				torch.ones((1, 196)						,						dtype=torch.bool ).to(_A )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__			:		List[str]													=				model(pixel_values=_A						,						bool_masked_pos=_A )
          UpperCAmelCase__			:		Optional[Any]													=				outputs.logits

          # verify the logits
          UpperCAmelCase__			:		List[Any]													=				torch.Size((1, 196, 8_192) )
          self.assertEqual(logits.shape						,						_A )

          UpperCAmelCase__			:		int													=				torch.tensor(
              [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]] ).to(_A )

          self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]						,						_A						,						atol=1e-2 ) )






      @slow
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' ).to(_A )

          UpperCAmelCase__			:		str													=				self.default_image_processor
          UpperCAmelCase__			:		Tuple													=				prepare_img()
          UpperCAmelCase__			:		List[str]													=				image_processor(images=_A						,						return_tensors='''pt''' ).to(_A )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__			:		List[str]													=				model(**_A )
          UpperCAmelCase__			:		List[str]													=				outputs.logits

          # verify the logits
          UpperCAmelCase__			:		Tuple													=				torch.Size((1, 1_000) )
          self.assertEqual(logits.shape						,						_A )

          UpperCAmelCase__			:		Any													=				torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] ).to(_A )

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

          UpperCAmelCase__			:		Any													=				281
          self.assertEqual(logits.argmax(-1 ).item()						,						_A )






      @slow
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' ).to(
              _A )

          UpperCAmelCase__			:		Optional[Any]													=				self.default_image_processor
          UpperCAmelCase__			:		Tuple													=				prepare_img()
          UpperCAmelCase__			:		Optional[Any]													=				image_processor(images=_A						,						return_tensors='''pt''' ).to(_A )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__			:		Union[str, Any]													=				model(**_A )
          UpperCAmelCase__			:		Any													=				outputs.logits

          # verify the logits
          UpperCAmelCase__			:		int													=				torch.Size((1, 21_841) )
          self.assertEqual(logits.shape						,						_A )

          UpperCAmelCase__			:		Tuple													=				torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] ).to(_A )

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

          UpperCAmelCase__			:		Any													=				2_396
          self.assertEqual(logits.argmax(-1 ).item()						,						_A )






      @slow
      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
          UpperCAmelCase__			:		int													=				model.to(_A )

          UpperCAmelCase__			:		List[str]													=				BeitImageProcessor(do_resize=_A						,						size=640						,						do_center_crop=_A )

          UpperCAmelCase__			:		Union[str, Any]													=				load_dataset('''hf-internal-testing/fixtures_ade20k'''						,						split='''test''' )
          UpperCAmelCase__			:		Optional[int]													=				Image.open(ds[0]['''file'''] )
          UpperCAmelCase__			:		Dict													=				image_processor(images=_A						,						return_tensors='''pt''' ).to(_A )

          # forward pass
          with torch.no_grad():
              UpperCAmelCase__			:		Optional[Any]													=				model(**_A )
          UpperCAmelCase__			:		Union[str, Any]													=				outputs.logits

          # verify the logits
          UpperCAmelCase__			:		Union[str, Any]													=				torch.Size((1, 150, 160, 160) )
          self.assertEqual(logits.shape						,						_A )

          UpperCAmelCase__			:		Tuple													=				version.parse(PIL.__version__ ) < version.parse('''9.0.0''' )

          if is_pillow_less_than_a:
              UpperCAmelCase__			:		List[Any]													=				torch.tensor(
                  [
                      [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                      [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                      [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                  ]						,						device=_A						,						)
          else:
              UpperCAmelCase__			:		List[Any]													=				torch.tensor(
                  [
                      [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                      [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                      [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
                  ]						,						device=_A						,						)

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






      @slow
      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
          UpperCAmelCase__			:		Optional[Any]													=				model.to(_A )

          UpperCAmelCase__			:		Dict													=				BeitImageProcessor(do_resize=_A						,						size=640						,						do_center_crop=_A )

          UpperCAmelCase__			:		List[Any]													=				load_dataset('''hf-internal-testing/fixtures_ade20k'''						,						split='''test''' )
          UpperCAmelCase__			:		str													=				Image.open(ds[0]['''file'''] )
          UpperCAmelCase__			:		str													=				image_processor(images=_A						,						return_tensors='''pt''' ).to(_A )

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

          UpperCAmelCase__			:		Any													=				outputs.logits.detach().cpu()

          UpperCAmelCase__			:		int													=				image_processor.post_process_semantic_segmentation(outputs=_A						,						target_sizes=[(500, 300)] )
          UpperCAmelCase__			:		Union[str, Any]													=				torch.Size((500, 300) )
          self.assertEqual(segmentation[0].shape						,						_A )

          UpperCAmelCase__			:		List[str]													=				image_processor.post_process_semantic_segmentation(outputs=_A )
          UpperCAmelCase__			:		Any													=				torch.Size((160, 160) )
          self.assertEqual(segmentation[0].shape						,						_A )




'''simple docstring'''
import pprint

import requests

UpperCamelCase__    					=      '''https://zenquotes.io/api'''





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/today'''						).json()





def 					a__       (					)    ->	list:
    return requests.get(API_ENDPOINT_URL + '''/random'''						).json()


if __name__ == "__main__":
  UpperCamelCase__    					=      random_quotes()
  pprint.pprint(response)
