



import os
import warnings
from typing import List, Optional

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


__snake_case    :		Tuple  =logging.get_logger(__name__)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					question_encoder
						lowerCAmelCase__					:    Any												=					generator
						lowerCAmelCase__					:    Optional[Any]												=					self.question_encoder



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






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



		@classmethod
		def 		lowerCAmelCase__				(cls		,__lowerCamelCase		,**__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






						from ..auto.tokenization_auto import AutoTokenizer

						lowerCAmelCase__					:    Optional[Any]												=					kwargs.pop('''config'''		,__lowerCamelCase			)

						if config is None:
										lowerCAmelCase__					:    int												=					RagConfig.from_pretrained(__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[Any]												=					AutoTokenizer.from_pretrained(
						    __lowerCamelCase		,config=config.question_encoder		,subfolder='''question_encoder_tokenizer'''			)
						lowerCAmelCase__					:    str												=					AutoTokenizer.from_pretrained(
						    __lowerCamelCase		,config=config.generator		,subfolder='''generator_tokenizer'''			)
						return cls(question_encoder=__lowerCamelCase		,generator=__lowerCamelCase			)



		def __call__(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      int:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.question_encoder



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.generator



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = "longest"		,__lowerCamelCase = None		,__lowerCamelCase = True		,**__lowerCamelCase		,)					->      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'''		,__lowerCamelCase		,)
						if max_length is None:
										lowerCAmelCase__					:    Union[str, Any]												=					self.current_tokenizer.model_max_length
						lowerCAmelCase__					:    List[str]												=					self(
						    __lowerCamelCase		,add_special_tokens=__lowerCamelCase		,return_tensors=__lowerCamelCase		,max_length=__lowerCamelCase		,padding=__lowerCamelCase		,truncation=__lowerCamelCase		,**__lowerCamelCase		,)
						if tgt_texts is None:
										return model_inputs
						# Process tgt_texts
						if max_target_length is None:
										lowerCAmelCase__					:    str												=					self.current_tokenizer.model_max_length
						lowerCAmelCase__					:    Optional[int]												=					self(
						    text_target=__lowerCamelCase		,add_special_tokens=__lowerCamelCase		,return_tensors=__lowerCamelCase		,padding=__lowerCamelCase		,max_length=__lowerCamelCase		,truncation=__lowerCamelCase		,**__lowerCamelCase		,)
						lowerCAmelCase__					:    Optional[Any]												=					labels['''input_ids''']
						return model_inputs






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case    :		Optional[int]  ={
    'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
    'processing_vision_text_dual_encoder': ['VisionTextDualEncoderProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		int  =['VisionTextDualEncoderModel']


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =['FlaxVisionTextDualEncoderModel']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['TFVisionTextDualEncoderModel']


if TYPE_CHECKING:
							from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
							from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure)

