




'''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_    :					Optional[Any]					      =						get_logger(__name__)







def      _UpperCamelCase	(_lowerCamelCase							:       Dict		)->				Tuple:




      '''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       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: int	    =     False  # to tell pytest it's not a test class


       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE					) ->					Optional[int]:



             '''simple docstring'''




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


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					Any:



             '''simple docstring'''




             __snake_case         =					dataset
             __snake_case         =					name
             __snake_case         =					cache_dir
             __snake_case         =					data_dir
             __snake_case         =					all_configs
             __snake_case         =					save_infos
             __snake_case         =					ignore_verifications
             __snake_case         =					force_redownload
             __snake_case         =					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:
                   __snake_case         =					True




       def  lowerCAmelCase (				self					) ->					Dict:



             '''simple docstring'''




             fl_logger().setLevel(__SCREAMING_SNAKE_CASE					)
             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					)
             __snake_case   ,      __snake_case         =					self._dataset, self._name
             __snake_case         =					dataset_module_factory(__SCREAMING_SNAKE_CASE					)
             __snake_case         =					import_main_class(module.module_path					)
             __snake_case         =					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=__SCREAMING_SNAKE_CASE      ,					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})'''					)
                   __snake_case         =					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=__SCREAMING_SNAKE_CASE      ,					)
                   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:
                         __snake_case         =					os.path.join(builder_cls.get_imported_module_dir()      ,					'''README.md'''					)
                         __snake_case         =					Path(__SCREAMING_SNAKE_CASE					).name + '''.py'''
                         __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
                         if os.path.isfile(__SCREAMING_SNAKE_CASE					):
                               __snake_case         =					os.path.dirname(__SCREAMING_SNAKE_CASE					)
                         elif os.path.isfile(__SCREAMING_SNAKE_CASE					):
                               __snake_case         =					path
                         elif os.path.isdir(__SCREAMING_SNAKE_CASE					):  # for local directories containing only data files
                               __snake_case         =					path
                         else:  # in case of a remote dataset
                               __snake_case         =					None
                               print(F'''Dataset card saved at {dataset_readme_path}'''					)

                         # Move dataset_info back to the user
                         if dataset_dir is not None:
                               __snake_case         =					os.path.join(__SCREAMING_SNAKE_CASE      ,					'''README.md'''					)
                               copyfile(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
                               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					)
                         __snake_case         =					os.path.join(self._cache_dir      ,					datasets.config.DOWNLOADED_DATASETS_DIR					)
                         if os.path.isdir(__SCREAMING_SNAKE_CASE					):
                               logger.warning(F'''Clearing cache at {download_dir}'''					)
                               rmtree(__SCREAMING_SNAKE_CASE					)

             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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import CLIPImageProcessor





class       lowerCAmelCase				(	unittest.TestCase):


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=7      ,					__SCREAMING_SNAKE_CASE=3      ,					__SCREAMING_SNAKE_CASE=18      ,					__SCREAMING_SNAKE_CASE=30      ,					__SCREAMING_SNAKE_CASE=400      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=[0.48_145_466, 0.4_578_275, 0.40_821_073]      ,					__SCREAMING_SNAKE_CASE=[0.26_862_954, 0.26_130_258, 0.27_577_711]      ,					__SCREAMING_SNAKE_CASE=True      ,					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					size if size is not None else {'''shortest_edge''': 20}
            __snake_case         =					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
            __snake_case         =					parent
            __snake_case         =					batch_size
            __snake_case         =					num_channels
            __snake_case         =					image_size
            __snake_case         =					min_resolution
            __snake_case         =					max_resolution
            __snake_case         =					do_resize
            __snake_case         =					size
            __snake_case         =					do_center_crop
            __snake_case         =					crop_size
            __snake_case         =					do_normalize
            __snake_case         =					image_mean
            __snake_case         =					image_std
            __snake_case         =					do_convert_rgb


       def  lowerCAmelCase (				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  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE=False      ,					__SCREAMING_SNAKE_CASE=False      ,					__SCREAMING_SNAKE_CASE=False					) ->					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         =					[]
                 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:
                 __snake_case         =					[]
                 for i in range(self.batch_size					):
                      __snake_case   ,      __snake_case         =					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
                 __snake_case         =					[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE      ,					0      ,					-1					)					) for x in image_inputs]

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

            return image_inputs





@require_torch
@require_vision
class       lowerCAmelCase				(	__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: Dict	    =     CLIPImageProcessor if is_vision_available() else None


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case         =					CLIPImageProcessingTester(self					)


       @property
       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					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}					)

            __snake_case         =					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					) ->					Dict:



            '''simple docstring'''




            pass


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            __snake_case         =					self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE					)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE      ,					Image.Image					)

            # Test not batched input
            __snake_case         =					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         =					image_processing(__SCREAMING_SNAKE_CASE      ,					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					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            __snake_case         =					self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE      ,					numpify=__SCREAMING_SNAKE_CASE					)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE      ,					np.ndarray					)

            # Test not batched input
            __snake_case         =					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         =					image_processing(__SCREAMING_SNAKE_CASE      ,					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					) ->					int:



            '''simple docstring'''




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

            # Test not batched input
            __snake_case         =					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         =					image_processing(__SCREAMING_SNAKE_CASE      ,					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       lowerCAmelCase				(	__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: Dict	    =     CLIPImageProcessor if is_vision_available() else None


       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case         =					CLIPImageProcessingTester(self      ,					num_channels=4					)
            __snake_case         =					3


       @property
       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            pass




       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            __snake_case         =					self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE					)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE      ,					Image.Image					)

            # Test not batched input
            __snake_case         =					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         =					image_processing(__SCREAMING_SNAKE_CASE      ,					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'''],
                )      ,					)






