




'''simple docstring'''



import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import DonutSwinModel
     from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST





class       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=13      ,					__SCREAMING_SNAKE_CASE=32      ,					__SCREAMING_SNAKE_CASE=2      ,					__SCREAMING_SNAKE_CASE=3      ,					__SCREAMING_SNAKE_CASE=16      ,					__SCREAMING_SNAKE_CASE=[1, 2, 1]      ,					__SCREAMING_SNAKE_CASE=[2, 2, 4]      ,					__SCREAMING_SNAKE_CASE=2      ,					__SCREAMING_SNAKE_CASE=2.0      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=0.0      ,					__SCREAMING_SNAKE_CASE=0.0      ,					__SCREAMING_SNAKE_CASE=0.1      ,					__SCREAMING_SNAKE_CASE="gelu"      ,					__SCREAMING_SNAKE_CASE=False      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=0.02      ,					__SCREAMING_SNAKE_CASE=1E-5      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=None      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=10      ,					__SCREAMING_SNAKE_CASE=8      ,					) ->					Any:



            '''simple docstring'''




            __snake_case         =					parent
            __snake_case         =					batch_size
            __snake_case         =					image_size
            __snake_case         =					patch_size
            __snake_case         =					num_channels
            __snake_case         =					embed_dim
            __snake_case         =					depths
            __snake_case         =					num_heads
            __snake_case         =					window_size
            __snake_case         =					mlp_ratio
            __snake_case         =					qkv_bias
            __snake_case         =					hidden_dropout_prob
            __snake_case         =					attention_probs_dropout_prob
            __snake_case         =					drop_path_rate
            __snake_case         =					hidden_act
            __snake_case         =					use_absolute_embeddings
            __snake_case         =					patch_norm
            __snake_case         =					layer_norm_eps
            __snake_case         =					initializer_range
            __snake_case         =					is_training
            __snake_case         =					scope
            __snake_case         =					use_labels
            __snake_case         =					type_sequence_label_size
            __snake_case         =					encoder_stride


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



            '''simple docstring'''




            __snake_case         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            __snake_case         =					None
            if self.use_labels:
                 __snake_case         =					ids_tensor([self.batch_size]      ,					self.type_sequence_label_size					)

            __snake_case         =					self.get_config()

            return config, pixel_values, labels


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



            '''simple docstring'''




            return DonutSwinConfig(
                image_size=self.image_size      ,					patch_size=self.patch_size      ,					num_channels=self.num_channels      ,					embed_dim=self.embed_dim      ,					depths=self.depths      ,					num_heads=self.num_heads      ,					window_size=self.window_size      ,					mlp_ratio=self.mlp_ratio      ,					qkv_bias=self.qkv_bias      ,					hidden_dropout_prob=self.hidden_dropout_prob      ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,					drop_path_rate=self.drop_path_rate      ,					hidden_act=self.hidden_act      ,					use_absolute_embeddings=self.use_absolute_embeddings      ,					path_norm=self.patch_norm      ,					layer_norm_eps=self.layer_norm_eps      ,					initializer_range=self.initializer_range      ,					encoder_stride=self.encoder_stride      ,					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					DonutSwinModel(config=__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()
            __snake_case         =					model(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
            __snake_case         =					int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

            self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, expected_seq_len, expected_dim)					)




       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case         =					self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      (
                __snake_case
            )   ,      
            )         =					config_and_inputs
            __snake_case         =					{'''pixel_values''': pixel_values}
            return config, inputs_dict





@require_torch
class       lowerCAmelCase				(	__lowerCAmelCase			,				__lowerCAmelCase			,				unittest.TestCase):
       __lowercase					: Dict	    =     (DonutSwinModel,) if is_torch_available() else ()
       __lowercase					: Any	    =     {'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
       __lowercase					: Tuple	    =     True

       __lowercase					: str	    =     False
       __lowercase					: str	    =     False
       __lowercase					: Tuple	    =     False


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					DonutSwinModelTester(self					)
            __snake_case         =					ConfigTester(self      ,					config_class=__SCREAMING_SNAKE_CASE      ,					embed_dim=37					)


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()


       def  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            return


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



            '''simple docstring'''




            __snake_case         =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            pass


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



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 self.assertIsInstance(model.get_input_embeddings()      ,					(nn.Module)					)
                 __snake_case         =					model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE      ,					nn.Linear					)					)


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 __snake_case         =					inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __snake_case         =					[*signature.parameters.keys()]

                 __snake_case         =					['''pixel_values''']
                 self.assertListEqual(arg_names[:1]      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case         =					True

            for model_class in self.all_model_classes:
                 __snake_case         =					True
                 __snake_case         =					False
                 __snake_case         =					True
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 with torch.no_grad():
                      __snake_case         =					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)					)
                 __snake_case         =					outputs.attentions
                 __snake_case         =					len(self.model_tester.depths					)
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE					)      ,					__SCREAMING_SNAKE_CASE					)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 __snake_case         =					True
                 __snake_case         =					config.window_size**2
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 with torch.no_grad():
                      __snake_case         =					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)					)
                 __snake_case         =					outputs.attentions
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE					)      ,					__SCREAMING_SNAKE_CASE					)

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]					)      ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,					)
                 __snake_case         =					len(__SCREAMING_SNAKE_CASE					)

                 # Check attention is always last and order is fine
                 __snake_case         =					True
                 __snake_case         =					True
                 __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
                 model.to(__SCREAMING_SNAKE_CASE					)
                 model.eval()
                 with torch.no_grad():
                      __snake_case         =					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)					)

                 if hasattr(self.model_tester      ,					'''num_hidden_states_types'''					):
                      __snake_case         =					self.model_tester.num_hidden_states_types
                 else:
                      # also another +1 for reshaped_hidden_states
                      __snake_case         =					2
                 self.assertEqual(out_len + added_hidden_states      ,					len(__SCREAMING_SNAKE_CASE					)					)

                 __snake_case         =					outputs.attentions

                 self.assertEqual(len(__SCREAMING_SNAKE_CASE					)      ,					__SCREAMING_SNAKE_CASE					)

                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]					)      ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,					)


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



            '''simple docstring'''




            __snake_case         =					model_class(__SCREAMING_SNAKE_CASE					)
            model.to(__SCREAMING_SNAKE_CASE					)
            model.eval()

            with torch.no_grad():
                 __snake_case         =					model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)					)

            __snake_case         =					outputs.hidden_states

            __snake_case         =					getattr(
                self.model_tester      ,					'''expected_num_hidden_layers'''      ,					len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE					)      ,					__SCREAMING_SNAKE_CASE					)

            # DonutSwin has a different seq_length
            __snake_case         =					(
                config.patch_size
                if isinstance(config.patch_size      ,					collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            __snake_case         =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]					)      ,					[num_patches, self.model_tester.embed_dim]      ,					)

            __snake_case         =					outputs.reshaped_hidden_states
            self.assertEqual(len(__SCREAMING_SNAKE_CASE					)      ,					__SCREAMING_SNAKE_CASE					)

            __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case         =					reshaped_hidden_states[0].shape
            __snake_case         =					(
                reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					height * width					).permute(0      ,					2      ,					1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]					)      ,					[num_patches, self.model_tester.embed_dim]      ,					)


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



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            __snake_case         =					(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size      ,					collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                 __snake_case         =					True
                 self.check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 __snake_case         =					True

                 self.check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case         =					3

            __snake_case         =					(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size      ,					collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            __snake_case         =					(
                config.patch_size
                if isinstance(config.patch_size      ,					collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            __snake_case         =					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            __snake_case         =					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                 __snake_case         =					True
                 self.check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					(padded_height, padded_width)					)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 __snake_case         =					True
                 self.check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					(padded_height, padded_width)					)


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



            '''simple docstring'''




            for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case         =					DonutSwinModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)




       def  lowerCAmelCase (				self					) ->					Any:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.model_tester.prepare_config_and_inputs_for_common()

            __snake_case         =					_config_zero_init(__SCREAMING_SNAKE_CASE					)
            for model_class in self.all_model_classes:
                 __snake_case         =					model_class(config=__SCREAMING_SNAKE_CASE					)
                 for name, param in model.named_parameters():
                      if "embeddings" not in name and param.requires_grad:
                           self.assertIn(
                               ((param.data.mean() * 1E9).round() / 1E9).item()      ,					[0.0, 1.0]      ,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,					)












'''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.'''					)






