"""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 lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				question_encoder
									SCREAMING_SNAKE_CASE__		  =				generator
									SCREAMING_SNAKE_CASE__		  =				self.question_encoder



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				List[str]		)							->       Tuple:
									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		)
									SCREAMING_SNAKE_CASE__		  =				os.path.join(_snake_case   , "question_encoder_tokenizer"		)
									SCREAMING_SNAKE_CASE__		  =				os.path.join(_snake_case   , "generator_tokenizer"		)
									self.question_encoder.save_pretrained(_snake_case		)
									self.generator.save_pretrained(_snake_case		)



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				List[Any]   , _snake_case			:				Union[str, Any]   , **_snake_case			:				Any		)							->       Union[str, Any]:
									# dynamically import AutoTokenizer
									from ..auto.tokenization_auto import AutoTokenizer

									SCREAMING_SNAKE_CASE__		  =				kwargs.pop("config"   , _snake_case		)

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

									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(
									    _snake_case   , config=config.question_encoder   , subfolder="question_encoder_tokenizer"		)
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(
									    _snake_case   , config=config.generator   , subfolder="generator_tokenizer"		)
									return cls(question_encoder=_snake_case   , generator=_snake_case		)



					def __call__(						self			:				Tuple   , *_snake_case			:				Tuple   , **_snake_case			:				List[Any]		)							->       Union[str, Any]:
									return self.current_tokenizer(*_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str   , *_snake_case			:				str   , **_snake_case			:				Union[str, Any]		)							->       List[Any]:
									return self.generator.batch_decode(*_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict   , *_snake_case			:				List[Any]   , **_snake_case			:				int		)							->       List[str]:
									return self.generator.decode(*_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.question_encoder



					def 			lowerCAmelCase_					(						self			:				int		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.generator



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				List[str]   , _snake_case			:				Optional[List[str]] = None   , _snake_case			:				Optional[int] = None   , _snake_case			:				Optional[int] = None   , _snake_case			:				str = "longest"   , _snake_case			:				str = None   , _snake_case			:				bool = True   , **_snake_case			:				str   , )							->       BatchEncoding:
									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:
													SCREAMING_SNAKE_CASE__		  =				self.current_tokenizer.model_max_length
									SCREAMING_SNAKE_CASE__		  =				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:
													SCREAMING_SNAKE_CASE__		  =				self.current_tokenizer.model_max_length
									SCREAMING_SNAKE_CASE__		  =				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   , )
									SCREAMING_SNAKE_CASE__		  =				labels["input_ids"]
									return model_inputs





"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A   					=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    [
						    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'VanForImageClassification',
						    'VanModel',
						    'VanPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure)




