






"""simple docstring"""





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


UpperCamelCase					=     get_logger(__name__)






def 							A(    snake_case_						):
       """simple docstring"""





       return TestCommand(
           args.dataset		,  args.name		,  args.cache_dir		,  args.data_dir		,  args.all_configs		,  args.save_info or args.save_infos		,  args.ignore_verifications		,  args.force_redownload		,  args.clear_cache		,  )







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							False  # to tell pytest it's not a test class





   @staticmethod
   def __lowercase							(       UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




          lowercase__:							int         =     parser.add_parser("test"					, help="Test dataset implementation.")
          test_parser.add_argument("--name"					, type=UpperCAmelCase_					, default=UpperCAmelCase_					, help="Dataset processing name")
          test_parser.add_argument(
              "--cache_dir"					, type=UpperCAmelCase_					, default=UpperCAmelCase_					, help="Cache directory where the datasets are stored."					, )
          test_parser.add_argument(
              "--data_dir"					, type=UpperCAmelCase_					, default=UpperCAmelCase_					, help="Can be used to specify a manual directory to get the files from."					, )
          test_parser.add_argument("--all_configs"					, action="store_true"					, help="Test all dataset configurations")
          test_parser.add_argument(
              "--save_info"					, action="store_true"					, help="Save the dataset infos in the dataset card (README.md)")
          test_parser.add_argument(
              "--ignore_verifications"					, action="store_true"					, help="Run the test without checksums and splits checks."					, )
          test_parser.add_argument("--force_redownload"					, action="store_true"					, help="Force dataset redownload")
          test_parser.add_argument(
              "--clear_cache"					, action="store_true"					, help="Remove downloaded files and cached datasets after each config test"					, )
          # aliases
          test_parser.add_argument("--save_infos"					, action="store_true"					, help="alias to save_info")
          test_parser.add_argument("dataset"					, type=UpperCAmelCase_					, help="Name of the dataset to download")
          test_parser.set_defaults(func=_test_command_factory)





   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, )			->   List[str]:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     dataset
          lowercase__:							Optional[Any]         =     name
          lowercase__:							int         =     cache_dir
          lowercase__:							str         =     data_dir
          lowercase__:							str         =     all_configs
          lowercase__:							List[Any]         =     save_infos
          lowercase__:							List[str]         =     ignore_verifications
          lowercase__:							Tuple         =     force_redownload
          lowercase__:							str         =     clear_cache
          if clear_cache and not cache_dir:
                 print(
                     "When --clear_cache is used, specifying a cache directory is mandatory.\n"
                     "The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
                     "Please provide a --cache_dir that will be used to test the dataset script.")
                 exit(1)
          if save_infos:
                 lowercase__:							Optional[int]         =     True





   def __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          fl_logger().setLevel(UpperCAmelCase_)
          if self._name is not None and self._all_configs:
                 print("Both parameters `config` and `all_configs` can't be used at once.")
                 exit(1)
          lowercase__    ,      lowercase__:							Tuple         =     self._dataset, self._name
          lowercase__:							Optional[Any]         =     dataset_module_factory(UpperCAmelCase_)
          lowercase__:							Optional[Any]         =     import_main_class(module.module_path)
          lowercase__:							Tuple         =     len(builder_cls.BUILDER_CONFIGS) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

          def get_builders() -> Generator[DatasetBuilder, None, None]:
                 if self._all_configs and builder_cls.BUILDER_CONFIGS:
                        for i, config in enumerate(builder_cls.BUILDER_CONFIGS):
                               if "config_name" in module.builder_kwargs:
                                      yield builder_cls(
                                          cache_dir=self._cache_dir					, data_dir=self._data_dir					, **module.builder_kwargs					, )
                               else:
                                      yield builder_cls(
                                          config_name=config.name					, cache_dir=self._cache_dir					, data_dir=self._data_dir					, **module.builder_kwargs					, )
                 else:
                        if "config_name" in module.builder_kwargs:
                               yield builder_cls(cache_dir=self._cache_dir					, data_dir=self._data_dir					, **module.builder_kwargs)
                        else:
                               yield builder_cls(
                                   config_name=UpperCAmelCase_					, cache_dir=self._cache_dir					, data_dir=self._data_dir					, **module.builder_kwargs					, )

          for j, builder in enumerate(get_builders()):
                 print(F"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})""")
                 lowercase__:							List[Any]         =     os.path.exists(
                     os.path.join(builder.get_imported_module_dir()					, datasets.config.DATASETDICT_INFOS_FILENAME))  # record checksums only if we need to update a (deprecated) dataset_infos.json
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD					, verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS					, try_from_hf_gcs=UpperCAmelCase_					, )
                 builder.as_dataset()
                 if self._save_infos:
                        builder._save_infos()

                 # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
                 # The dataset_infos are saved in the YAML part of the README.md

                 # Let's move it to the original directory of the dataset script, to allow the user to
                 # upload them on S3 at the same time afterwards.
                 if self._save_infos:
                        lowercase__:							Dict         =     os.path.join(builder_cls.get_imported_module_dir()					, "README.md")
                        lowercase__:							Dict         =     Path(UpperCAmelCase_).name + ".py"
                        lowercase__:							Optional[Any]         =     os.path.join(UpperCAmelCase_					, UpperCAmelCase_)
                        if os.path.isfile(UpperCAmelCase_):
                               lowercase__:							Dict         =     os.path.dirname(UpperCAmelCase_)
                        elif os.path.isfile(UpperCAmelCase_):
                               lowercase__:							List[str]         =     path
                        elif os.path.isdir(UpperCAmelCase_):  # for local directories containing only data files
                               lowercase__:							Tuple         =     path
                        else:  # in case of a remote dataset
                               lowercase__:							Tuple         =     None
                               print(F"""Dataset card saved at {dataset_readme_path}""")

                        # Move dataset_info back to the user
                        if dataset_dir is not None:
                               lowercase__:							int         =     os.path.join(UpperCAmelCase_					, "README.md")
                               copyfile(UpperCAmelCase_					, UpperCAmelCase_)
                               print(F"""Dataset card saved at {user_dataset_readme_path}""")

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
                 if self._clear_cache:
                        if os.path.isdir(builder._cache_dir):
                               logger.warning(F"""Clearing cache at {builder._cache_dir}""")
                               rmtree(builder._cache_dir)
                        lowercase__:							Any         =     os.path.join(self._cache_dir					, datasets.config.DOWNLOADED_DATASETS_DIR)
                        if os.path.isdir(UpperCAmelCase_):
                               logger.warning(F"""Clearing cache at {download_dir}""")
                               rmtree(UpperCAmelCase_)

          print("Test successful.")











'''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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=2					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    )    ->    Tuple:
    A_  :		Dict						=					size if size is not None else {"""shortest_edge""": 30}
    A_  :		List[Any]						=					parent
    A_  :		Optional[Any]						=					batch_size
    A_  :		Tuple						=					num_channels
    A_  :		Any						=					image_size
    A_  :		str						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Any						=					do_resize
    A_  :		List[Any]						=					size
    A_  :		Union[str, Any]						=					size_divisor
    A_  :		Dict						=					do_normalize
    A_  :		Dict						=					image_mean
    A_  :		Optional[Any]						=					image_std







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "size_divisor": self.size_divisor,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Union[str, Any]:
    if not batched:
       A_  :		List[str]						=					self.size["""shortest_edge"""]
       A_  :		str						=					image_inputs[0]
       if isinstance(_lowerCamelCase					,    Image.Image  ):
          A_						,  A_  :		Dict						=					image.size
       else:
          A_						,  A_  :		Dict						=					image.shape[1], image.shape[2]
       A_  :		Dict						=					size / min(_lowerCamelCase					,    _lowerCamelCase  )
       if h < w:
          A_						,  A_  :		Any						=					size, scale * w
       else:
          A_						,  A_  :		List[str]						=					scale * h, size

       A_  :		Dict						=					int((1333 / 800) * size  )
       if max(_lowerCamelCase					,    _lowerCamelCase  ) > max_size:
          A_  :		Dict						=					max_size / max(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					newh * scale
          A_  :		Tuple						=					neww * scale

       A_						,  A_  :		str						=					int(newh + 0.5  ), int(neww + 0.5  )
       A_						,  A_  :		Union[str, Any]						=					(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

    else:
       A_  :		Union[str, Any]						=					[]
       for image in image_inputs:
          A_						,  A_  :		Any						=					self.get_expected_values([image]  )
          expected_values.append((expected_height, expected_width)  )
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[0]  )[0]
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[1]  )[1]

    return expected_height, expected_width


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     ViltImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[int]						=					ViltImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size_divisor"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 30}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		Any						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Tuple						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Tuple						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[str]						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    numpify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    np.ndarray  )

    # Test not batched input
    A_  :		Any						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		int						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    # Initialize image_processing
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		str						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    torchify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    torch.Tensor  )

    # Test not batched input
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		List[str]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		Any						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )
