



'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       *_lowerCAmelCase  :  Union[str, Any]  ,       **_lowerCAmelCase  :  Any):







											'''simple docstring'''

											super().__init__(*_lowerCAmelCase  ,       **_lowerCAmelCase)
											__lowercase       	={}

				def 				__lowerCamelCase     (							self  :  Optional[int]  ,       _lowerCAmelCase  :  Dict  ,       *_lowerCAmelCase  :  Tuple  ,       **_lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	=super().add_tokens(_lowerCAmelCase  ,       *_lowerCAmelCase  ,       **_lowerCAmelCase)
											if num_added_tokens == 0:
																		raise ValueError(
																		    f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
																		    ' `placeholder_token` that is not already in the tokenizer.')

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  Optional[Any]  ,       *_lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  Tuple=1  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=[]
											if num_vec_per_token == 1:
																		self.try_adding_tokens(_lowerCAmelCase  ,       *_lowerCAmelCase  ,       **_lowerCAmelCase)
																		output.append(_lowerCAmelCase)
											else:
																		__lowercase       	=[]
																		for i in range(_lowerCAmelCase):
																									__lowercase       	=placeholder_token + f"""_{i}"""
																									self.try_adding_tokens(_lowerCAmelCase  ,       *_lowerCAmelCase  ,       **_lowerCAmelCase)
																									output.append(_lowerCAmelCase)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
											for token in self.token_map:
																		if token in placeholder_token:
																									raise ValueError(
																									    f"""The tokenizer already has placeholder token {token} that can get confused with"""
																									    f""" {placeholder_token}keep placeholder tokens independent""")
											__lowercase       	=output

				def 				__lowerCamelCase     (							self  :  Optional[int]  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  Optional[Any]=False  ,       _lowerCAmelCase  :  Optional[Any]=1.0):







											'''simple docstring'''

											if isinstance(_lowerCAmelCase  ,       _lowerCAmelCase):
																		__lowercase       	=[]
																		for i in range(len(_lowerCAmelCase)):
																									output.append(self.replace_placeholder_tokens_in_text(text[i]  ,       vector_shuffle=_lowerCAmelCase))
																		return output
											for placeholder_token in self.token_map:
																		if placeholder_token in text:
																									__lowercase       	=self.token_map[placeholder_token]
																									__lowercase       	=tokens[: 1 + int(len(_lowerCAmelCase) * prop_tokens_to_load)]
																									if vector_shuffle:
																																__lowercase       	=copy.copy(_lowerCAmelCase)
																																random.shuffle(_lowerCAmelCase)
																									__lowercase       	=text.replace(_lowerCAmelCase  ,       ' '.join(_lowerCAmelCase))
											return text

				def __call__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Optional[Any]  ,       *_lowerCAmelCase  :  Optional[Any]  ,       _lowerCAmelCase  :  str=False  ,       _lowerCAmelCase  :  List[str]=1.0  ,       **_lowerCAmelCase  :  Any):







											'''simple docstring'''

											return super().__call__(
											    self.replace_placeholder_tokens_in_text(
											        _lowerCAmelCase  ,       vector_shuffle=_lowerCAmelCase  ,       prop_tokens_to_load=_lowerCAmelCase)  ,       *_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  Optional[int]  ,       *_lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Optional[int]=False  ,       _lowerCAmelCase  :  Optional[int]=1.0  ,       **_lowerCAmelCase  :  str):







											'''simple docstring'''

											return super().encode(
											    self.replace_placeholder_tokens_in_text(
											        _lowerCAmelCase  ,       vector_shuffle=_lowerCAmelCase  ,       prop_tokens_to_load=_lowerCAmelCase)  ,       *_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )








'''simple docstring'''



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict



@pytest.mark.parametrize(
    'files'  ,   [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=tmp_path_factory.mktemp('dset_infos_dir'					)
							if "full:README.md" in files:
														with open(dataset_infos_dir / 'README.md'  ,   'w'					) as f:
																					f.write('---\ndataset_info:\n  dataset_size: 42\n---'					)
							if "empty:README.md" in files:
														with open(dataset_infos_dir / 'README.md'  ,   'w'					) as f:
																					f.write(''					)
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / 'dataset_infos.json'  ,   'w'					) as f:
																					f.write('{"default": {"dataset_size": 42}}'					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42



@pytest.mark.parametrize(
    'dataset_info'  ,   [
        DatasetInfo(),
        DatasetInfo(
            description='foo'  ,   features=Features({'a': Value('int32'					)}					)  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train'}]  ,   download_size=42  ,   ),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_info.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfo.from_directory(_lowerCAmelCase					)
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(_lowerCAmelCase  ,   'dataset_info.json'					)					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=DatasetInfo(
							    description='foo'  ,   citation='bar'  ,   homepage='https://foo.bar'  ,   license='CC0'  ,   features=Features({'a': Value('int32'					)}					)  ,   post_processed={}  ,   supervised_keys=()  ,   task_templates=[]  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train', 'num_examples': 42}]  ,   download_checksums={}  ,   download_size=1_337  ,   post_processing_size=442  ,   dataset_size=1_234  ,   size_in_bytes=1_337 + 442 + 1_234  ,   )
							__lowercase       	=dataset_info._to_yaml_dict()
							assert sorted(_lowerCAmelCase					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]  ,   (list, dict, int, str)					)
							__lowercase       	=yaml.safe_dump(_lowerCAmelCase					)
							__lowercase       	=yaml.safe_load(_lowerCAmelCase					)
							assert dataset_info_yaml_dict == reloaded



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=DatasetInfo()
							__lowercase       	=dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    'dataset_infos_dict'  ,   [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}					),
        DatasetInfosDict({'my_config_name': DatasetInfo()}					),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'  ,   features=Features({'a': Value('int32'					)}					)  ,   builder_name='builder'  ,   config_name='config'  ,   version='1.0.0'  ,   splits=[{'name': 'train'}]  ,   download_size=42  ,   )
            }					),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42					),
                'v2': DatasetInfo(dataset_size=1_337					),
            }					),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_infos_dict.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														__lowercase       	=config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														__lowercase       	=DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(_lowerCAmelCase  ,   'README.md'					)					)



