





import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A							   =							logging.get_logger(__name__)



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__: List[str]         =question_encoder
   lowerCamelCase__: List[str]         =generator
   lowerCamelCase__: List[Any]         =self.question_encoder

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Union[str, Any])			->List[str]:



   '''simple docstring'''



   if os.path.isfile(UpperCAmelCase_):
     raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""")
   os.makedirs(UpperCAmelCase_			,					exist_ok=UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =os.path.join(UpperCAmelCase_			,					"question_encoder_tokenizer")
   lowerCamelCase__: Optional[Any]         =os.path.join(UpperCAmelCase_			,					"generator_tokenizer")
   self.question_encoder.save_pretrained(UpperCAmelCase_)
   self.generator.save_pretrained(UpperCAmelCase_)

 @classmethod
 def 				SCREAMING_SNAKE_CASE_   (cls							:  int			,					UpperCAmelCase_							:  int			,					**UpperCAmelCase_							:  Union[str, Any])			->List[Any]:



   '''simple docstring'''



   from ..auto.tokenization_auto import AutoTokenizer

   lowerCamelCase__: Tuple         =kwargs.pop("config"			,					UpperCAmelCase_)

   if config is None:
     lowerCamelCase__: Optional[int]         =RagConfig.from_pretrained(UpperCAmelCase_)

   lowerCamelCase__: Optional[Any]         =AutoTokenizer.from_pretrained(
       UpperCAmelCase_			,					config=config.question_encoder			,					subfolder="question_encoder_tokenizer")
   lowerCamelCase__: int         =AutoTokenizer.from_pretrained(
       UpperCAmelCase_			,					config=config.generator			,					subfolder="generator_tokenizer")
   return cls(question_encoder=UpperCAmelCase_			,					generator=UpperCAmelCase_)

 def __call__(self							:  Union[str, Any]			,					*UpperCAmelCase_							:  Any			,					**UpperCAmelCase_							:  Union[str, Any])			->int:



   '''simple docstring'''



   return self.current_tokenizer(*UpperCAmelCase_			,					**UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					*UpperCAmelCase_							:  Optional[Any]			,					**UpperCAmelCase_							:  Optional[int])			->Dict:



   '''simple docstring'''



   return self.generator.batch_decode(*UpperCAmelCase_			,					**UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					*UpperCAmelCase_							:  Union[str, Any]			,					**UpperCAmelCase_							:  Tuple)			->Any:



   '''simple docstring'''



   return self.generator.decode(*UpperCAmelCase_			,					**UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Any         =self.question_encoder

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



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.generator







 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str]			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Optional[List[str]] = None			,					UpperCAmelCase_							:  Optional[int] = None			,					UpperCAmelCase_							:  Optional[int] = None			,					UpperCAmelCase_							:  str = "longest"			,					UpperCAmelCase_							:  str = None			,					UpperCAmelCase_							:  bool = True			,					**UpperCAmelCase_							:  Union[str, Any]			,					)			->BatchEncoding:



   '''simple docstring'''



   warnings.warn(
       "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
       "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
       "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
       "details"			,					UpperCAmelCase_			,					)
   if max_length is None:
     lowerCamelCase__: List[str]         =self.current_tokenizer.model_max_length
   lowerCamelCase__: List[Any]         =self(
       UpperCAmelCase_			,					add_special_tokens=UpperCAmelCase_			,					return_tensors=UpperCAmelCase_			,					max_length=UpperCAmelCase_			,					padding=UpperCAmelCase_			,					truncation=UpperCAmelCase_			,					**UpperCAmelCase_			,					)
   if tgt_texts is None:
     return model_inputs
   # Process tgt_texts
   if max_target_length is None:
     lowerCamelCase__: Optional[int]         =self.current_tokenizer.model_max_length
   lowerCamelCase__: List[Any]         =self(
       text_target=UpperCAmelCase_			,					add_special_tokens=UpperCAmelCase_			,					return_tensors=UpperCAmelCase_			,					padding=UpperCAmelCase_			,					max_length=UpperCAmelCase_			,					truncation=UpperCAmelCase_			,					**UpperCAmelCase_			,					)
   lowerCamelCase__: Union[str, Any]         =labels["input_ids"]
   return model_inputs







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        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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}}'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     DatasetInfo  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      str(lowerCamelCase_  )
			dataset_info.write_to_directory(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      DatasetInfo.from_directory(lowerCamelCase_  )
			assert dataset_info == reloaded
			assert os.path.exists(os.path.join(lowerCamelCase_  ,							'dataset_info.json'  )  )





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      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=13_37  ,							post_processing_size=4_42  ,							dataset_size=12_34  ,							size_in_bytes=13_37 + 4_42 + 12_34  ,							)
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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)  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      yaml.safe_dump(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      yaml.safe_load(lowerCamelCase_  )
			assert dataset_info_yaml_dict == reloaded





def        __UpperCAmelCase						(							)       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      DatasetInfo()
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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=13_37  ),
            }  ),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     DatasetInfosDict  )       ->  Tuple:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      str(lowerCamelCase_  )
			dataset_infos_dict.write_to_directory(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      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():
						SCREAMING_SNAKE_CASE_   : Any					  =      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
						SCREAMING_SNAKE_CASE_   : Any					  =      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'  )  )



