

'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase      :       str      			=      get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

__UpperCamelCase      :       Optional[int]      			=      250004
__UpperCamelCase      :       Optional[Any]      			=      250020
@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=MBartTokenizer
      __a  					=MBartTokenizerFast
      __a  					=True
      __a  					=True




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         super().setUp()

         # We have a SentencePiece fixture for testing
         __a				     =    MBartTokenizer(lowerCamelCase ,   keep_accents=lowerCamelCase      )
         tokenizer.save_pretrained(self.tmpdirname      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    MBartTokenizer(lowerCamelCase ,   keep_accents=lowerCamelCase      )

         __a				     =    tokenizer.tokenize('This is a test'      )
         self.assertListEqual(lowerCamelCase ,   ['▁This', '▁is', '▁a', '▁t', 'est']      )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCamelCase      ) ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,   )

         __a				     =    tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         self.assertListEqual(
             lowerCamelCase ,   [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ] ,   )
         __a				     =    tokenizer.convert_tokens_to_ids(lowerCamelCase      )
         self.assertListEqual(
             lowerCamelCase ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ] ,   )

         __a				     =    tokenizer.convert_ids_to_tokens(lowerCamelCase      )
         self.assertListEqual(
             lowerCamelCase ,   [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ] ,   )






      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

         __a				     =    (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
               __a				     =    self.rust_tokenizer_class.from_pretrained(lowerCamelCase ,   **lowerCamelCase      )
               __a				     =    self.tokenizer_class.from_pretrained(lowerCamelCase ,   **lowerCamelCase      )

               __a				     =    tempfile.mkdtemp()

               __a				     =    tokenizer_r.save_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.save_pretrained(lowerCamelCase      )

               # Checks it save with the same files + the tokenizer.json file for the fast one
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
               __a				     =    tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
               self.assertSequenceEqual(lowerCamelCase ,   lowerCamelCase      )

               # Checks everything loads correctly in the same way
               __a				     =    tokenizer_r.from_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.from_pretrained(lowerCamelCase      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(lowerCamelCase ,   lowerCamelCase      )      )
                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

               shutil.rmtree(lowerCamelCase      )

               # Save tokenizer rust, legacy_format=True
               __a				     =    tempfile.mkdtemp()

               __a				     =    tokenizer_r.save_pretrained(lowerCamelCase ,   legacy_format=lowerCamelCase      )
               __a				     =    tokenizer_p.save_pretrained(lowerCamelCase      )

               # Checks it save with the same files
               self.assertSequenceEqual(lowerCamelCase ,   lowerCamelCase      )

               # Checks everything loads correctly in the same way
               __a				     =    tokenizer_r.from_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.from_pretrained(lowerCamelCase      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(lowerCamelCase ,   lowerCamelCase      )      )

               shutil.rmtree(lowerCamelCase      )

               # Save tokenizer rust, legacy_format=False
               __a				     =    tempfile.mkdtemp()

               __a				     =    tokenizer_r.save_pretrained(lowerCamelCase ,   legacy_format=lowerCamelCase      )
               __a				     =    tokenizer_p.save_pretrained(lowerCamelCase      )

               # Checks it saved the tokenizer.json file
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

               # Checks everything loads correctly in the same way
               __a				     =    tokenizer_r.from_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.from_pretrained(lowerCamelCase      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(lowerCamelCase ,   lowerCamelCase      )      )

               shutil.rmtree(lowerCamelCase      )







@require_torch
@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):
      __a  					="facebook/mbart-large-en-ro"
      __a  					=[
          " UN Chief Says There Is No Military Solution in Syria",
          " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
      ]
      __a  					=[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
          " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
          " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
      ]
      __a  					=[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]




      @classmethod
      def  __UpperCamelCase							(  cls      )							->List[str]:



         '''simple docstring'''
         __a				     =    MBartTokenizer.from_pretrained(
             cls.checkpoint_name ,   src_lang='en_XX' ,   tgt_lang='ro_RO'      )
         __a				     =    1
         return cls




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,   25_0001      )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,   25_0004      )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,   25_0020      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         self.assertIn(lowerCamelCase ,   self.tokenizer.all_special_ids      )
         __a				     =    [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
         __a				     =    self.tokenizer.decode(lowerCamelCase ,   skip_special_tokens=lowerCamelCase      )
         __a				     =    self.tokenizer.decode(generated_ids[1:] ,   skip_special_tokens=lowerCamelCase      )
         self.assertEqual(lowerCamelCase ,   lowerCamelCase      )
         self.assertNotIn(self.tokenizer.eos_token ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    ['this is gunna be a long sentence ' * 20]
         assert isinstance(src_text[0] ,   lowerCamelCase      )
         __a				     =    10
         __a				     =    self.tokenizer(lowerCamelCase ,   max_length=lowerCamelCase ,   truncation=lowerCamelCase      ).input_ids[0]
         self.assertEqual(ids[-2] ,   2      )
         self.assertEqual(ids[-1] ,   lowerCamelCase      )
         self.assertEqual(len(lowerCamelCase      ) ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      ) ,   [25_0026, 25_0001]      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    tempfile.mkdtemp()
         __a				     =    self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(lowerCamelCase      )
         __a				     =    MBartTokenizer.from_pretrained(lowerCamelCase      )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,   lowerCamelCase      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.tokenizer(self.src_text ,   text_target=self.tgt_text ,   padding=lowerCamelCase ,   return_tensors='pt'      )
         __a				     =    shift_tokens_right(batch['labels'] ,   self.tokenizer.pad_token_id      )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
         assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
         assert batch.decoder_input_ids[1][-1] == 2
         assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




      @require_torch
      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.tokenizer(
             self.src_text ,   text_target=self.tgt_text ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=len(self.expected_src_tokens      ) ,   return_tensors='pt' ,   )

         __a				     =    shift_tokens_right(batch['labels'] ,   self.tokenizer.pad_token_id      )

         self.assertIsInstance(lowerCamelCase ,   lowerCamelCase      )

         self.assertEqual((2, 14) ,   batch.input_ids.shape      )
         self.assertEqual((2, 14) ,   batch.attention_mask.shape      )
         __a				     =    batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens ,   lowerCamelCase      )
         self.assertEqual(2 ,   batch.decoder_input_ids[0, -1]      )  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens ,   []      )
         self.assertEqual(self.tokenizer.suffix_tokens ,   [self.tokenizer.eos_token_id, EN_CODE]      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.tokenizer(self.src_text ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=3 ,   return_tensors='pt'      )
         __a				     =    self.tokenizer(
             text_target=self.tgt_text ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=10 ,   return_tensors='pt'      )
         __a				     =    targets['input_ids']
         __a				     =    shift_tokens_right(lowerCamelCase ,   self.tokenizer.pad_token_id      )

         self.assertEqual(batch.input_ids.shape[1] ,   3      )
         self.assertEqual(batch.decoder_input_ids.shape[1] ,   10      )






      @require_torch
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.tokenizer._build_translation_inputs(
             'A test' ,   return_tensors='pt' ,   src_lang='en_XX' ,   tgt_lang='ar_AR'      )

         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   {
                 # A, test, EOS, en_XX
                 'input_ids': [[62, 3034, 2, 25_0004]],
                 'attention_mask': [[1, 1, 1, 1]],
                 # ar_AR
                 'forced_bos_token_id': 25_0001,
             } ,   )







'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(SCREAMING_SNAKE_CASE__    ):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}"""    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         __a				     =    size if size is not None else {'shortest_edge': 256}
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         if "shortest_edge" in size:
            __a				     =    get_resize_output_image_size(lowerCamelCase ,   size['shortest_edge'] ,   default_to_square=lowerCamelCase      )
         elif "height" in size and "width" in size:
            __a				     =    (size['height'], size['width'])
         else:
            raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
         return resize(lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
         return center_crop(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   )							->np.ndarray:



         '''simple docstring'''
         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_center_crop and crop_size is None:
            raise ValueError('Crop size must be specified if do_center_crop is True.'      )

         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.'      )

         if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'      )

         # All transformations expect numpy arrays.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->PIL.Image.Image:



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __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				     =    offset if offset is not None else self.offset
         __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				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         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.'      )

         __a				     =    make_batched(lowerCamelCase      )

         __a				     =    [
             [
                 self._preprocess_image(
                     image=lowerCamelCase ,   do_resize=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   do_center_crop=lowerCamelCase ,   crop_size=lowerCamelCase ,   do_rescale=lowerCamelCase ,   rescale_factor=lowerCamelCase ,   offset=lowerCamelCase ,   do_normalize=lowerCamelCase ,   image_mean=lowerCamelCase ,   image_std=lowerCamelCase ,   data_format=lowerCamelCase ,   )
                 for img in video
             ]
             for video in videos
         ]

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )