






'''simple docstring'''






import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							MobileBertTokenizer
   _snake_case        =							MobileBertTokenizerFast
   _snake_case        =							True
   _snake_case        =							True
   _snake_case        =							filter_non_english
   _snake_case        =							"""google/mobilebert-uncased"""







   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      super().setUp()

      __snake_case				:      str     							= [
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      __snake_case				:      Tuple     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file'])
      with open(self.vocab_file					,   'w'					,   encoding='utf-8') as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

      __snake_case				:      str     							= [
          (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
          for tokenizer_def in self.tokenizers_list
      ]







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Any     							= 'UNwant\u00E9d,running'
      __snake_case				:      Union[str, Any]     							= 'unwanted, running'
      return input_text, output_text







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Dict     							= self.tokenizer_class(self.vocab_file)

      __snake_case				:      int     							= tokenizer.tokenize('UNwant\u00E9d,running')
      self.assertListEqual(__a					,   ['un', '##want', '##ed', ',', 'runn', '##ing'])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__a)					,   [9, 6, 7, 1_2, 1_0, 1_1])







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






      if not self.test_rust_tokenizer:
         return

      __snake_case				:      Dict     							= self.get_tokenizer()
      __snake_case				:      Optional[Any]     							= self.get_rust_tokenizer()

      __snake_case				:      int     							= 'UNwant\u00E9d,running'

      __snake_case				:      Tuple     							= tokenizer.tokenize(__a)
      __snake_case				:      int     							= rust_tokenizer.tokenize(__a)
      self.assertListEqual(__a					,   __a)

      __snake_case				:      Union[str, Any]     							= tokenizer.encode(__a					,   add_special_tokens=__a)
      __snake_case				:      int     							= rust_tokenizer.encode(__a					,   add_special_tokens=__a)
      self.assertListEqual(__a					,   __a)

      __snake_case				:      Optional[int]     							= self.get_rust_tokenizer()
      __snake_case				:      List[str]     							= tokenizer.encode(__a)
      __snake_case				:      int     							= rust_tokenizer.encode(__a)
      self.assertListEqual(__a					,   __a)

      # With lower casing
      __snake_case				:      Tuple     							= self.get_tokenizer(do_lower_case=__a)
      __snake_case				:      int     							= self.get_rust_tokenizer(do_lower_case=__a)

      __snake_case				:      Optional[int]     							= 'UNwant\u00E9d,running'

      __snake_case				:      str     							= tokenizer.tokenize(__a)
      __snake_case				:      Any     							= rust_tokenizer.tokenize(__a)
      self.assertListEqual(__a					,   __a)

      __snake_case				:      List[str]     							= tokenizer.encode(__a					,   add_special_tokens=__a)
      __snake_case				:      Dict     							= rust_tokenizer.encode(__a					,   add_special_tokens=__a)
      self.assertListEqual(__a					,   __a)

      __snake_case				:      Optional[Any]     							= self.get_rust_tokenizer()
      __snake_case				:      Union[str, Any]     							= tokenizer.encode(__a)
      __snake_case				:      Optional[Any]     							= rust_tokenizer.encode(__a)
      self.assertListEqual(__a					,   __a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      str     							= BasicTokenizer()

      self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz')					,   ['ah', '\u535A', '\u63A8', 'zz'])







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






      __snake_case				:      Dict     							= BasicTokenizer(do_lower_case=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')					,   ['hello', '!', 'how', 'are', 'you', '?'])
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo')					,   ['hello'])







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






      __snake_case				:      Dict     							= BasicTokenizer(do_lower_case=__a					,   strip_accents=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')					,   ['hällo', '!', 'how', 'are', 'you', '?'])
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo')					,   ['h\u00E9llo'])







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






      __snake_case				:      int     							= BasicTokenizer(do_lower_case=__a					,   strip_accents=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')					,   ['hallo', '!', 'how', 'are', 'you', '?'])
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo')					,   ['hello'])







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= BasicTokenizer(do_lower_case=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')					,   ['hallo', '!', 'how', 'are', 'you', '?'])
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo')					,   ['hello'])







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      int     							= BasicTokenizer(do_lower_case=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')					,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?'])







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






      __snake_case				:      Optional[int]     							= BasicTokenizer(do_lower_case=__a					,   strip_accents=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')					,   ['HäLLo', '!', 'how', 'Are', 'yoU', '?'])







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      int     							= BasicTokenizer(do_lower_case=__a					,   strip_accents=__a)

      self.assertListEqual(
          tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')					,   ['HaLLo', '!', 'how', 'Are', 'yoU', '?'])







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= BasicTokenizer(do_lower_case=__a					,   never_split=['[UNK]'])

      self.assertListEqual(
          tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]')					,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

      __snake_case				:      Optional[Any]     							= {}
      for i, token in enumerate(__a):
         __snake_case				:      str     							= i
      __snake_case				:      Optional[Any]     							= WordpieceTokenizer(vocab=__a					,   unk_token='[UNK]')

      self.assertListEqual(tokenizer.tokenize('')					,   [])

      self.assertListEqual(tokenizer.tokenize('unwanted running')					,   ['un', '##want', '##ed', 'runn', '##ing'])

      self.assertListEqual(tokenizer.tokenize('unwantedX running')					,   ['[UNK]', 'runn', '##ing'])







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






      self.assertTrue(_is_whitespace(' '))
      self.assertTrue(_is_whitespace('\t'))
      self.assertTrue(_is_whitespace('\r'))
      self.assertTrue(_is_whitespace('\n'))
      self.assertTrue(_is_whitespace('\u00A0'))

      self.assertFalse(_is_whitespace('A'))
      self.assertFalse(_is_whitespace('-'))







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






      self.assertTrue(_is_control('\u0005'))

      self.assertFalse(_is_control('A'))
      self.assertFalse(_is_control(' '))
      self.assertFalse(_is_control('\t'))
      self.assertFalse(_is_control('\r'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      self.assertTrue(_is_punctuation('-'))
      self.assertTrue(_is_punctuation('$'))
      self.assertTrue(_is_punctuation('`'))
      self.assertTrue(_is_punctuation('.'))

      self.assertFalse(_is_punctuation('A'))
      self.assertFalse(_is_punctuation(' '))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.get_tokenizer()
      __snake_case				:      Union[str, Any]     							= self.get_rust_tokenizer()

      # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
      self.assertListEqual([tokenizer.tokenize(__a) for t in ['Test', '\xad', 'test']]					,   [['[UNK]'], [], ['[UNK]']])

      self.assertListEqual(
          [rust_tokenizer.tokenize(__a) for t in ['Test', '\xad', 'test']]					,   [['[UNK]'], [], ['[UNK]']])







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






      __snake_case				:      int     							= self.tokenizer_class.from_pretrained('google/mobilebert-uncased')

      __snake_case				:      Optional[Any]     							= tokenizer.encode('sequence builders'					,   add_special_tokens=__a)
      __snake_case				:      Optional[int]     							= tokenizer.encode('multi-sequence build'					,   add_special_tokens=__a)

      __snake_case				:      List[Any]     							= tokenizer.build_inputs_with_special_tokens(__a)
      __snake_case				:      str     							= tokenizer.build_inputs_with_special_tokens(__a					,   __a)

      assert encoded_sentence == [1_0_1] + text + [1_0_2]
      assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]







   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
            __snake_case				:      Optional[Any]     							= self.rust_tokenizer_class.from_pretrained(__a					,   **__a)

            __snake_case				:      Dict     							= F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
            __snake_case				:      Union[str, Any]     							= tokenizer_r.encode_plus(
                __a					,   return_attention_mask=__a					,   return_token_type_ids=__a					,   return_offsets_mapping=__a					,   add_special_tokens=__a					,   )

            __snake_case				:      List[Any]     							= tokenizer_r.do_lower_case if hasattr(__a					,   'do_lower_case') else False
            __snake_case				:      Tuple     							= (
                [
                    ((0, 0), tokenizer_r.cls_token),
                    ((0, 1), 'A'),
                    ((1, 2), ','),
                    ((3, 5), 'na'),
                    ((5, 6), '##ï'),
                    ((6, 8), '##ve'),
                    ((9, 1_5), tokenizer_r.mask_token),
                    ((1_6, 2_1), 'Allen'),
                    ((2_1, 2_3), '##NL'),
                    ((2_3, 2_4), '##P'),
                    ((2_5, 3_3), 'sentence'),
                    ((3_3, 3_4), '.'),
                    ((0, 0), tokenizer_r.sep_token),
                ]
                if not do_lower_case
                else [
                    ((0, 0), tokenizer_r.cls_token),
                    ((0, 1), 'a'),
                    ((1, 2), ','),
                    ((3, 8), 'naive'),
                    ((9, 1_5), tokenizer_r.mask_token),
                    ((1_6, 2_1), 'allen'),
                    ((2_1, 2_3), '##nl'),
                    ((2_3, 2_4), '##p'),
                    ((2_5, 3_3), 'sentence'),
                    ((3_3, 3_4), '.'),
                    ((0, 0), tokenizer_r.sep_token),
                ]
            )

            self.assertEqual(
                [e[1] for e in expected_results]					,   tokenizer_r.convert_ids_to_tokens(tokens['input_ids']))
            self.assertEqual([e[0] for e in expected_results]					,   tokens['offset_mapping'])







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






      __snake_case				:      Tuple     							= ['的', '人', '有']
      __snake_case				:      List[str]     							= ''.join(__a)
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
            __snake_case				:      Any     							= True
            __snake_case				:      int     							= self.tokenizer_class.from_pretrained(__a					,   **__a)
            __snake_case				:      int     							= self.rust_tokenizer_class.from_pretrained(__a					,   **__a)

            __snake_case				:      List[Any]     							= tokenizer_p.encode(__a					,   add_special_tokens=__a)
            __snake_case				:      Optional[Any]     							= tokenizer_r.encode(__a					,   add_special_tokens=__a)

            __snake_case				:      int     							= tokenizer_r.convert_ids_to_tokens(__a)
            __snake_case				:      List[Any]     							= tokenizer_p.convert_ids_to_tokens(__a)

            # it is expected that each Chinese character is not preceded by "##"
            self.assertListEqual(__a					,   __a)
            self.assertListEqual(__a					,   __a)

            __snake_case				:      Tuple     							= False
            __snake_case				:      List[Any]     							= self.rust_tokenizer_class.from_pretrained(__a					,   **__a)
            __snake_case				:      Optional[int]     							= self.tokenizer_class.from_pretrained(__a					,   **__a)

            __snake_case				:      str     							= tokenizer_r.encode(__a					,   add_special_tokens=__a)
            __snake_case				:      int     							= tokenizer_p.encode(__a					,   add_special_tokens=__a)

            __snake_case				:      str     							= tokenizer_r.convert_ids_to_tokens(__a)
            __snake_case				:      Any     							= tokenizer_p.convert_ids_to_tokens(__a)

            # it is expected that only the first Chinese character is not preceded by "##".
            __snake_case				:      Optional[Any]     							= [
                F"""##{token}""" if idx != 0 else token for idx, token in enumerate(__a)
            ]
            self.assertListEqual(__a					,   __a)
            self.assertListEqual(__a					,   __a)







'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor




class a_    (    unittest.TestCase							):







   def __init__(self					,   __a					,   __a=7					,   __a=3					,   __a=1_8					,   __a=3_0					,   __a=4_0_0					,   __a=True					,   __a=None					,   __a=True					,   __a=None					,   __a=True					,   __a=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,   __a=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,   __a=True					,   )			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Tuple     							= size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
      __snake_case				:      Any     							= crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      __snake_case				:      Optional[int]     							= parent
      __snake_case				:      Dict     							= batch_size
      __snake_case				:      str     							= num_channels
      __snake_case				:      Optional[Any]     							= image_size
      __snake_case				:      Optional[int]     							= min_resolution
      __snake_case				:      Tuple     							= max_resolution
      __snake_case				:      Optional[int]     							= do_resize
      __snake_case				:      Optional[int]     							= size
      __snake_case				:      Union[str, Any]     							= do_center_crop
      __snake_case				:      List[Any]     							= crop_size
      __snake_case				:      int     							= do_normalize
      __snake_case				:      Optional[Any]     							= image_mean
      __snake_case				:      str     							= image_std
      __snake_case				:      Optional[Any]     							= do_convert_rgb







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






      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=False					,   __a=False					,   __a=False)			->      List[str]:
      """simple docstring"""






      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         __snake_case				:      Optional[int]     							= []
         for i in range(self.batch_size):
            image_inputs.append(
                np.random.randint(
                    2_5_5					,   size=(self.num_channels, self.max_resolution, self.max_resolution)					,   dtype=np.uinta))
      else:
         __snake_case				:      Dict     							= []
         for i in range(self.batch_size):
            __snake_case							,__snake_case				:      Optional[Any]     							= np.random.choice(np.arange(self.min_resolution					,   self.max_resolution)					,   2)
            image_inputs.append(np.random.randint(2_5_5					,   size=(self.num_channels, width, height)					,   dtype=np.uinta))

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         __snake_case				:      int     							= [Image.fromarray(np.moveaxis(__a					,   0					,   -1)) for x in image_inputs]

      if torchify:
         __snake_case				:      List[Any]     							= [torch.from_numpy(__a) for x in image_inputs]

      return image_inputs




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= ChineseCLIPImageProcessingTester(self					,   do_center_crop=__a)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      int     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size					,   {'height': 2_2_4, 'width': 2_2_4})
      self.assertEqual(image_processor.crop_size					,   {'height': 1_8, 'width': 1_8})

      __snake_case				:      List[str]     							= self.image_processing_class.from_dict(self.image_processor_dict					,   size=4_2					,   crop_size=8_4)
      self.assertEqual(image_processor.size					,   {'shortest_edge': 4_2})
      self.assertEqual(image_processor.crop_size					,   {'height': 8_4, 'width': 8_4})







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






      pass







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






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Optional[Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      int     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      List[Any]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )







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






      __snake_case				:      Union[str, Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      __snake_case				:      Optional[int]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   numpify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   np.ndarray)

      # Test not batched input
      __snake_case				:      List[Any]     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      int     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      __snake_case				:      Tuple     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   torchify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   torch.Tensor)

      # Test not batched input
      __snake_case				:      Any     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      Union[str, Any]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







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






      __snake_case				:      Optional[Any]     							= ChineseCLIPImageProcessingTester(self					,   num_channels=4					,   do_center_crop=__a)
      __snake_case				:      List[Any]     							= 3







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Union[str, Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      Optional[int]     							= image_processing(image_inputs[0]					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )

      # Test batched
      __snake_case				:      Optional[int]     							= image_processing(__a					,   return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape					,   (
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,   )