"""simple docstring"""





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        _snake_case   :






						def __init__(				self					:	List[str]							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	Dict			):
													__lowerCamelCase    :						int						      =   question_encoder
													__lowerCamelCase    :						Optional[int]						      =   generator
													__lowerCamelCase    :						int						      =   self.question_encoder






						def 					lowerCamelCase__   (				self					:	List[str]							,    UpperCAmelCase					:	Optional[Any]			):




													if os.path.isfile(lowercase_			):
																				raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""			)
													os.makedirs(lowercase_							,    exist_ok=lowercase_			)
													__lowerCamelCase    :						Dict						      =   os.path.join(lowercase_							,    "question_encoder_tokenizer"			)
													__lowerCamelCase    :						Dict						      =   os.path.join(lowercase_							,    "generator_tokenizer"			)
													self.question_encoder.save_pretrained(lowercase_			)
													self.generator.save_pretrained(lowercase_			)






						@classmethod
						def 					lowerCamelCase__   (				cls					:	List[str]							,    UpperCAmelCase					:	Tuple							,    **UpperCAmelCase					:	List[str]			):
													from ..auto.tokenization_auto import AutoTokenizer

													__lowerCamelCase    :						Any						      =   kwargs.pop("config"							,    lowercase_			)

													if config is None:
																				__lowerCamelCase    :						Optional[Any]						      =   RagConfig.from_pretrained(lowercase_			)

													__lowerCamelCase    :						Dict						      =   AutoTokenizer.from_pretrained(
													    lowercase_							,    config=config.question_encoder							,    subfolder="question_encoder_tokenizer"			)
													__lowerCamelCase    :						Union[str, Any]						      =   AutoTokenizer.from_pretrained(
													    lowercase_							,    config=config.generator							,    subfolder="generator_tokenizer"			)
													return cls(question_encoder=lowercase_							,    generator=lowercase_			)






						def __call__(				self					:	Dict							,    *UpperCAmelCase					:	List[Any]							,    **UpperCAmelCase					:	Tuple			):
													return self.current_tokenizer(*lowercase_							,    **lowercase_			)






						def 					lowerCamelCase__   (				self					:	Tuple							,    *UpperCAmelCase					:	Dict							,    **UpperCAmelCase					:	Optional[Any]			):
													return self.generator.batch_decode(*lowercase_							,    **lowercase_			)






						def 					lowerCamelCase__   (				self					:	Any							,    *UpperCAmelCase					:	str							,    **UpperCAmelCase					:	Any			):
													return self.generator.decode(*lowercase_							,    **lowercase_			)






						def 					lowerCamelCase__   (				self					:	Optional[int]			):
													__lowerCamelCase    :						Optional[Any]						      =   self.question_encoder






						def 					lowerCamelCase__   (				self					:	Any			):
													__lowerCamelCase    :						List[Any]						      =   self.generator







						def 					lowerCamelCase__   (				self					:	List[Any]							,    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					:	int							,    ):
													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"							,    lowercase_							,    )
													if max_length is None:
																				__lowerCamelCase    :						List[Any]						      =   self.current_tokenizer.model_max_length
													__lowerCamelCase    :						Dict						      =   self(
													    lowercase_							,    add_special_tokens=lowercase_							,    return_tensors=lowercase_							,    max_length=lowercase_							,    padding=lowercase_							,    truncation=lowercase_							,    **lowercase_							,    )
													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    :						int						      =   self(
													    text_target=lowercase_							,    add_special_tokens=lowercase_							,    return_tensors=lowercase_							,    padding=lowercase_							,    max_length=lowercase_							,    truncation=lowercase_							,    **lowercase_							,    )
													__lowerCamelCase    :						Optional[Any]						      =   labels['input_ids']
													return model_inputs



import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCamelCase											=       logging.getLogger()




@unittest.skip('Temporarily disable the doc tests.'     )
@require_torch
@require_tf
@slow
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Path			,   lowercase_						:     Union[str, None] = None			,   lowercase_						:     Union[List[str], None] = None			,   lowercase_						:     Union[str, List[str], None] = None			,   lowercase_						:     bool = True			,   )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[file for file in os.listdir(lowercase_ ) if os.path.isfile(os.path.join(lowercase_			,   lowercase_ ) )]

             if identifier is not None:
                    _lowerCamelCase       :		Any		=[file for file in files if identifier in file]

             if n_identifier is not None:
                    if isinstance(lowercase_			,   lowercase_ ):
                           for n_ in n_identifier:
                                  _lowerCamelCase       :		Any		=[file for file in files if n_ not in file]
                    else:
                           _lowerCamelCase       :		List[Any]		=[file for file in files if n_identifier not in file]

             _lowerCamelCase       :		List[Any]		=ignore_files or []
             ignore_files.append('__init__.py' )
             _lowerCamelCase       :		List[Any]		=[file for file in files if file not in ignore_files]

             for file in files:
                    # Open all files
                    print('Testing'			,   lowercase_ )

                    if only_modules:
                           _lowerCamelCase       :		Optional[int]		=file.split('.' )[0]
                           try:
                                  _lowerCamelCase       :		Any		=getattr(lowercase_			,   lowercase_ )
                                  _lowerCamelCase       :		Optional[int]		=doctest.DocTestSuite(lowercase_ )
                                  _lowerCamelCase       :		List[Any]		=unittest.TextTestRunner().run(lowercase_ )
                                  self.assertIs(len(result.failures )			,   0 )
                           except AttributeError:
                                  logger.info(F'''{module_identifier} is not a module.''' )
                    else:
                           _lowerCamelCase       :		int		=doctest.testfile(str('..' / directory / file )			,   optionflags=doctest.ELLIPSIS )
                           self.assertIs(result.failed			,   0 )






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		int		=Path('src/transformers' )
             _lowerCamelCase       :		Optional[Any]		='modeling'
             _lowerCamelCase       :		List[str]		=[
                 'modeling_ctrl.py',
                 'modeling_tf_ctrl.py',
             ]
             self.analyze_directory(lowercase_			,   identifier=lowercase_			,   ignore_files=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=Path('src/transformers' )
             _lowerCamelCase       :		Union[str, Any]		='tokenization'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('src/transformers' )
             _lowerCamelCase       :		Any		='configuration'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=Path('src/transformers' )
             _lowerCamelCase       :		int		=['configuration', 'modeling', 'tokenization']
             self.analyze_directory(lowercase_			,   n_identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('docs/source' )
             _lowerCamelCase       :		Dict		=['favicon.ico']
             self.analyze_directory(lowercase_			,   ignore_files=lowercase_			,   only_modules=lowercase_ )


