


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


A				:   List[Any]		= get_logger(__name__)

def 				lowercase_		(		_A      :   Optional[int]						):

      """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       _lowercase			(							lowercase__):


       """simple docstring"""




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





       @staticmethod
       def 						lowerCAmelCase					(       __lowerCamelCase							:      ArgumentParser	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  parser.add_parser("test" ,				help="Test dataset implementation."	)
             test_parser.add_argument("--name" ,				type=__lowerCamelCase ,				default=__lowerCamelCase ,				help="Dataset processing name"	)
             test_parser.add_argument(
                 "--cache_dir" ,				type=__lowerCamelCase ,				default=__lowerCamelCase ,				help="Cache directory where the datasets are stored." ,				)
             test_parser.add_argument(
                 "--data_dir" ,				type=__lowerCamelCase ,				default=__lowerCamelCase ,				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=__lowerCamelCase ,				help="Name of the dataset to download"	)
             test_parser.set_defaults(func=_test_command_factory	)





       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      bool ,				__lowerCamelCase							:      bool ,				__lowerCamelCase							:      bool ,				__lowerCamelCase							:      bool ,				__lowerCamelCase							:      bool ,				):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  dataset
             lowerCamelCase__		:    List[Any]   =  name
             lowerCamelCase__		:    int   =  cache_dir
             lowerCamelCase__		:    str   =  data_dir
             lowerCamelCase__		:    Tuple   =  all_configs
             lowerCamelCase__		:    Optional[Any]   =  save_infos
             lowerCamelCase__		:    Union[str, Any]   =  ignore_verifications
             lowerCamelCase__		:    Optional[Any]   =  force_redownload
             lowerCamelCase__		:    Tuple   =  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:
                   lowerCamelCase__		:    Optional[int]   =  True





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             fl_logger().setLevel(__lowerCamelCase	)
             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	)
             lowerCamelCase__   ,       lowerCamelCase__		:    Dict   =  self._dataset, self._name
             lowerCamelCase__		:    List[str]   =  dataset_module_factory(__lowerCamelCase	)
             lowerCamelCase__		:    int   =  import_main_class(module.module_path	)
             lowerCamelCase__		:    int   =  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=__lowerCamelCase ,				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})"	)
                   lowerCamelCase__		:    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=__lowerCamelCase ,				)
                   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:
                         lowerCamelCase__		:    Dict   =  os.path.join(builder_cls.get_imported_module_dir() ,				"README.md"	)
                         lowerCamelCase__		:    List[str]   =  Path(__lowerCamelCase	).name + ".py"
                         lowerCamelCase__		:    Optional[Any]   =  os.path.join(__lowerCamelCase ,				__lowerCamelCase	)
                         if os.path.isfile(__lowerCamelCase	):
                               lowerCamelCase__		:    str   =  os.path.dirname(__lowerCamelCase	)
                         elif os.path.isfile(__lowerCamelCase	):
                               lowerCamelCase__		:    List[Any]   =  path
                         elif os.path.isdir(__lowerCamelCase	):  # for local directories containing only data files
                               lowerCamelCase__		:    Optional[int]   =  path
                         else:  # in case of a remote dataset
                               lowerCamelCase__		:    List[Any]   =  None
                               print(f"Dataset card saved at {dataset_readme_path}"	)

                         # Move dataset_info back to the user
                         if dataset_dir is not None:
                               lowerCamelCase__		:    int   =  os.path.join(__lowerCamelCase ,				"README.md"	)
                               copyfile(__lowerCamelCase ,				__lowerCamelCase	)
                               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	)
                         lowerCamelCase__		:    List[Any]   =  os.path.join(self._cache_dir ,				datasets.config.DOWNLOADED_DATASETS_DIR	)
                         if os.path.isdir(__lowerCamelCase	):
                               logger.warning(f"Clearing cache at {download_dir}"	)
                               rmtree(__lowerCamelCase	)

             print("Test successful."	)






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 EfficientNetImageProcessor







class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Optional[Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      int=13 ,				__lowerCamelCase							:      Tuple=3 ,				__lowerCamelCase							:      Optional[Any]=18 ,				__lowerCamelCase							:      List[Any]=30 ,				__lowerCamelCase							:      Union[str, Any]=400 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      Union[str, Any]=None ,				__lowerCamelCase							:      Optional[Any]=True ,				__lowerCamelCase							:      str=[0.5, 0.5, 0.5] ,				__lowerCamelCase							:      Dict=[0.5, 0.5, 0.5] ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  size if size is not None else {"height": 18, "width": 18}
             lowerCamelCase__		:    Dict   =  parent
             lowerCamelCase__		:    str   =  batch_size
             lowerCamelCase__		:    Tuple   =  num_channels
             lowerCamelCase__		:    Tuple   =  image_size
             lowerCamelCase__		:    Any   =  min_resolution
             lowerCamelCase__		:    Dict   =  max_resolution
             lowerCamelCase__		:    Tuple   =  do_resize
             lowerCamelCase__		:    List[str]   =  size
             lowerCamelCase__		:    Optional[Any]   =  do_normalize
             lowerCamelCase__		:    Union[str, Any]   =  image_mean
             lowerCamelCase__		:    Optional[Any]   =  image_std





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



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







@require_torch
@require_vision
class       _lowercase			(							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      EfficientNetImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  EfficientNetImageProcessorTester(self	)





       @property
       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  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"	)	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.image_processing_class.from_dict(self.image_processor_dict	)
             self.assertEqual(image_processor.size ,				{"height": 18, "width": 18}	)

             lowerCamelCase__		:    Optional[int]   =  self.image_processing_class.from_dict(self.image_processor_dict ,				size=42	)
             self.assertEqual(image_processor.size ,				{"height": 42, "width": 42}	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  self.image_processing_class(**self.image_processor_dict	)
             # create random PIL images
             lowerCamelCase__		:    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
             lowerCamelCase__		:    Union[str, 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.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    Dict   =  image_processing(__lowerCamelCase ,				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.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  self.image_processing_class(**self.image_processor_dict	)
             # create random numpy tensors
             lowerCamelCase__		:    List[Any]   =  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
             lowerCamelCase__		:    List[str]   =  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.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    Union[str, Any]   =  image_processing(__lowerCamelCase ,				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.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.image_processing_class(**self.image_processor_dict	)
             # create random PyTorch tensors
             lowerCamelCase__		:    int   =  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
             lowerCamelCase__		:    Dict   =  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.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    List[Any]   =  image_processing(__lowerCamelCase ,				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.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)


