import os
import warnings
from typing import List, Optional

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


lowerCamelCase_    :		List[Any]							=	logging.get_logger(__name__)


class 						a__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[str]:
								__a		 =       question_encoder
								__a		 =       generator
								__a		 =       self.question_encoder



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[int]:
								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   )
								__a		 =       os.path.join(UpperCAmelCase				,						'question_encoder_tokenizer'   )
								__a		 =       os.path.join(UpperCAmelCase				,						'generator_tokenizer'   )
								self.question_encoder.save_pretrained(UpperCAmelCase   )
								self.generator.save_pretrained(UpperCAmelCase   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[int]:
								# dynamically import AutoTokenizer
								from ..auto.tokenization_auto import AutoTokenizer

								__a		 =       kwargs.pop('config'				,						UpperCAmelCase   )

								if config is None:
													__a		 =       RagConfig.from_pretrained(UpperCAmelCase   )

								__a		 =       AutoTokenizer.from_pretrained(
								    UpperCAmelCase				,						config=config.question_encoder				,						subfolder='question_encoder_tokenizer'   )
								__a		 =       AutoTokenizer.from_pretrained(
								    UpperCAmelCase				,						config=config.generator				,						subfolder='generator_tokenizer'   )
								return cls(question_encoder=UpperCAmelCase				,						generator=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[int]:
								return self.current_tokenizer(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[int]:
								return self.generator.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.generator.decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.question_encoder



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       self.generator







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = "longest"				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						**UpperCAmelCase				,						)			->   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'				,						UpperCAmelCase				,						)
								if max_length is None:
													__a		 =       self.current_tokenizer.model_max_length
								__a		 =       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:
													__a		 =       self.current_tokenizer.model_max_length
								__a		 =       self(
								    text_target=UpperCAmelCase				,						add_special_tokens=UpperCAmelCase				,						return_tensors=UpperCAmelCase				,						padding=UpperCAmelCase				,						max_length=UpperCAmelCase				,						truncation=UpperCAmelCase				,						**UpperCAmelCase				,						)
								__a		 =       labels['input_ids']
								return model_inputs





from ..utils import DummyObject, requires_backends


class 						a__							(       metaclass=__snake_case		):
			A__    :       List[Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Union[str, Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Dict        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Union[str, Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[Any]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Dict        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[int]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   str:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Tuple        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )




