

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'''],
            )     ,      )







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"],
                    }




