code: 
  
  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 CLIPImageProcessor
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__=7     ,      lowercase__=3     ,      lowercase__=18     ,      lowercase__=30     ,      lowercase__=400     ,      lowercase__=True     ,      lowercase__=None     ,      lowercase__=True     ,      lowercase__=None     ,      lowercase__=True     ,      lowercase__=[0.48145466, 0.4578275, 0.40821073]     ,      lowercase__=[0.26862954, 0.26130258, 0.27577711]     ,      lowercase__=True     ,      )     ->      Optional[int]:
          __UpperCAmelCase									=			size if size is not None else {'''shortest_edge''': 20}
          __UpperCAmelCase									=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCAmelCase									=			parent
          __UpperCAmelCase									=			batch_size
          __UpperCAmelCase									=			num_channels
          __UpperCAmelCase									=			image_size
          __UpperCAmelCase									=			min_resolution
          __UpperCAmelCase									=			max_resolution
          __UpperCAmelCase									=			do_resize
          __UpperCAmelCase									=			size
          __UpperCAmelCase									=			do_center_crop
          __UpperCAmelCase									=			crop_size
          __UpperCAmelCase									=			do_normalize
          __UpperCAmelCase									=			image_mean
          __UpperCAmelCase									=			image_std
          __UpperCAmelCase									=			do_convert_rgb
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          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      lowerCAmelCase_						(self     ,      lowercase__=False     ,      lowercase__=False     ,      lowercase__=False  )     ->      List[Any]:
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
          if equal_resolution:
              __UpperCAmelCase									=			[]
              for i in range(self.batch_size  ):
                  image_inputs.append(
                      np.random.randint(
                          255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta  )  )
          else:
              __UpperCAmelCase									=			[]
              for i in range(self.batch_size  ):
                  __UpperCAmelCase					,     __UpperCAmelCase									=			np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution  )     ,      2  )
                  image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta  )  )
  
          if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __UpperCAmelCase									=			[Image.fromarray(np.moveaxis(lowercase__     ,      0     ,      -1  )  ) for x in image_inputs]
  
          if torchify:
              __UpperCAmelCase									=			[torch.from_numpy(lowercase__  ) for x in image_inputs]
  
          return image_inputs
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					A_   (      _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       CLIPImageProcessor if is_vision_available() else None
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			CLIPImageProcessingTester(self  )
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_resize'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''size'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_center_crop'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''center_crop'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_normalize'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''image_mean'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''image_std'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_convert_rgb'''  )  )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			self.image_processing_class.from_dict(self.image_processor_dict  )
          self.assertEqual(image_processor.size     ,      {'''shortest_edge''': 20}  )
          self.assertEqual(image_processor.crop_size     ,      {'''height''': 18, '''width''': 18}  )
  
          __UpperCAmelCase									=			self.image_processing_class.from_dict(self.image_processor_dict     ,      size=42     ,      crop_size=84  )
          self.assertEqual(image_processor.size     ,      {'''shortest_edge''': 42}  )
          self.assertEqual(image_processor.crop_size     ,      {'''height''': 84, '''width''': 84}  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          pass
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          # Initialize image_processing
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      Image.Image  )
  
          # Test not batched input
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			image_processing(lowercase__     ,      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      lowerCAmelCase_						(self  )     ->      str:
          # Initialize image_processing
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      numpify=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      np.ndarray  )
  
          # Test not batched input
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			image_processing(lowercase__     ,      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      lowerCAmelCase_						(self  )     ->      Any:
          # Initialize image_processing
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      torchify=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      torch.Tensor  )
  
          # Test not batched input
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			image_processing(lowercase__     ,      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_   (      _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       CLIPImageProcessor if is_vision_available() else None
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			CLIPImageProcessingTester(self     ,      num_channels=4  )
          __UpperCAmelCase									=			3
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_resize'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''size'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_center_crop'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''center_crop'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_normalize'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''image_mean'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''image_std'''  )  )
          self.assertTrue(hasattr(lowercase__     ,      '''do_convert_rgb'''  )  )
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          pass
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          # Initialize image_processing
          __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  )
          for image in image_inputs:
              self.assertIsInstance(lowercase__     ,      Image.Image  )
  
          # Test not batched input
          __UpperCAmelCase									=			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
          __UpperCAmelCase									=			image_processing(lowercase__     ,      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'''],
              )     ,      )
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  