






"""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       :	Optional[Any]             = logging.get_logger(__name__)







class        _SCREAMING_SNAKE_CASE						:





       """simple docstring"""





       def __init__(   self:     Optional[int]				,		__A:     Tuple				,		__A:     Optional[Any]    ):

            '''simple docstring'''

            a__          =  question_encoder
            a__          =  generator
            a__          =  self.question_encoder




       def  lowercase		(   self:     Optional[int]				,		__A:     Any    ):

            '''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    )
            a__          =  os.path.join(_lowerCamelCase				,		'''question_encoder_tokenizer'''    )
            a__          =  os.path.join(_lowerCamelCase				,		'''generator_tokenizer'''    )
            self.question_encoder.save_pretrained(_lowerCamelCase    )
            self.generator.save_pretrained(_lowerCamelCase    )




       @classmethod
       def  lowercase		(   cls:     List[Any]				,		__A:     List[Any]				,		**__A:     List[Any]    ):

            '''simple docstring'''

            from ..auto.tokenization_auto import AutoTokenizer

            a__          =  kwargs.pop('''config'''				,		_lowerCamelCase    )

            if config is None:
                 a__          =  RagConfig.from_pretrained(_lowerCamelCase    )

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




       def __call__(   self:     Any				,		*__A:     int				,		**__A:     Optional[Any]    ):

            '''simple docstring'''

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




       def  lowercase		(   self:     List[Any]				,		*__A:     int				,		**__A:     Union[str, Any]    ):

            '''simple docstring'''

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




       def  lowercase		(   self:     int				,		*__A:     Optional[Any]				,		**__A:     Union[str, Any]    ):

            '''simple docstring'''

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




       def  lowercase		(   self:     int    ):

            '''simple docstring'''

            a__          =  self.question_encoder




       def  lowercase		(   self:     Any    ):

            '''simple docstring'''

            a__          =  self.generator





       def  lowercase		(   self:     Dict				,		__A:     int				,		__A:     Tuple = None				,		__A:     List[Any] = None				,		__A:     str = None				,		__A:     List[Any] = "longest"				,		__A:     Tuple = None				,		__A:     Any = True				,		**__A:     List[str]				,		):

            '''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:
                 a__          =  self.current_tokenizer.model_max_length
            a__          =  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:
                 a__          =  self.current_tokenizer.model_max_length
            a__          =  self(
                text_target=_lowerCamelCase				,		add_special_tokens=_lowerCamelCase				,		return_tensors=_lowerCamelCase				,		padding=_lowerCamelCase				,		max_length=_lowerCamelCase				,		truncation=_lowerCamelCase				,		**_lowerCamelCase				,		)
            a__          =  labels['''input_ids''']
            return model_inputs










"""simple docstring"""







__a       :	List[Any]             = frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'negative_prompt'])

__a       :	Any             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Dict             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Optional[Any]             = frozenset(['prompt', 'image', 'negative_prompt'])

__a       :	List[Any]             = frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

__a       :	Optional[Any]             = frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	int             = frozenset(['image', 'mask_image'])

__a       :	Tuple             = frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Optional[Any]             = frozenset(['example_image', 'image', 'mask_image'])

__a       :	Optional[Any]             = frozenset(['class_labels'])

__a       :	Tuple             = frozenset(['class_labels'])

__a       :	int             = frozenset(['batch_size'])

__a       :	int             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['batch_size'])

__a       :	Tuple             = frozenset([])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Dict             = frozenset(['prompt', 'negative_prompt'])
__a       :	Optional[int]             = frozenset(['input_tokens'])

__a       :	str             = frozenset(['input_tokens'])


