import os
import warnings
from typing import List, Optional

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


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    List[Any]         =				question_encoder
						A_		:    List[str]         =				generator
						A_		:    Tuple         =				self.question_encoder
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if os.path.isfile(_snake_case ):
								raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
						os.makedirs(_snake_case		,     exist_ok=_snake_case )
						A_		:    Optional[Any]         =				os.path.join(_snake_case		,     'question_encoder_tokenizer' )
						A_		:    Optional[Any]         =				os.path.join(_snake_case		,     'generator_tokenizer' )
						self.question_encoder.save_pretrained(_snake_case )
						self.generator.save_pretrained(_snake_case )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						from ..auto.tokenization_auto import AutoTokenizer

						A_		:    Tuple         =				kwargs.pop('config'		,     _snake_case )

						if config is None:
								A_		:    str         =				RagConfig.from_pretrained(_snake_case )

						A_		:    List[Any]         =				AutoTokenizer.from_pretrained(
						    _snake_case		,     config=config.question_encoder		,     subfolder='question_encoder_tokenizer' )
						A_		:    str         =				AutoTokenizer.from_pretrained(
						    _snake_case		,     config=config.generator		,     subfolder='generator_tokenizer' )
						return cls(question_encoder=_snake_case		,     generator=_snake_case )
				def __call__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						return self.current_tokenizer(*_snake_case		,     **_snake_case )
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						return self.generator.batch_decode(*_snake_case		,     **_snake_case )
				def   lowerCAmelCase_       (						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						return self.generator.decode(*_snake_case		,     **_snake_case )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.question_encoder
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				self.generator







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = "longest"		,     lowercase = None		,     lowercase = True		,     **lowercase		,     ):



						"""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'		,     _snake_case		,     )
						if max_length is None:
								A_		:    Optional[Any]         =				self.current_tokenizer.model_max_length
						A_		:    Optional[Any]         =				self(
						    _snake_case		,     add_special_tokens=_snake_case		,     return_tensors=_snake_case		,     max_length=_snake_case		,     padding=_snake_case		,     truncation=_snake_case		,     **_snake_case		,     )
						if tgt_texts is None:
								return model_inputs
						# Process tgt_texts
						if max_target_length is None:
								A_		:    Optional[int]         =				self.current_tokenizer.model_max_length
						A_		:    Any         =				self(
						    text_target=_snake_case		,     add_special_tokens=_snake_case		,     return_tensors=_snake_case		,     padding=_snake_case		,     max_length=_snake_case		,     truncation=_snake_case		,     **_snake_case		,     )
						A_		:    Dict         =				labels['input_ids']
						return model_inputs







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






