




'''simple docstring'''







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 			_a  (	__a ):
       __a				:     List[str]   				=					["""image_processor""", """tokenizer"""]
       __a				:     List[str]   				=					"""BlipImageProcessor"""
       __a				:     Union[str, Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Any   ,				lowercase						:	List[str]   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		False
           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	ImageInput = None   ,				lowercase						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowercase						:	bool = True   ,				lowercase						:	Union[bool, str, PaddingStrategy] = False   ,				lowercase						:	Union[bool, str, TruncationStrategy] = None   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	int = 0   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				**lowercase						:	Any   ,				):



           '''simple docstring'''

           if images is None and text is None:
               raise ValueError('''You have to specify either images or text.'''				)

           # Get only text
           if images is None:
               UpperCAmelCase			       =		self.tokenizer
               UpperCAmelCase			       =		self.tokenizer(
                   text=lowercase   ,				add_special_tokens=lowercase   ,				padding=lowercase   ,				truncation=lowercase   ,				max_length=lowercase   ,				stride=lowercase   ,				pad_to_multiple_of=lowercase   ,				return_attention_mask=lowercase   ,				return_overflowing_tokens=lowercase   ,				return_special_tokens_mask=lowercase   ,				return_offsets_mapping=lowercase   ,				return_token_type_ids=lowercase   ,				return_length=lowercase   ,				verbose=lowercase   ,				return_tensors=lowercase   ,				**lowercase   ,				)
               return text_encoding

           # add pixel_values
           UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(
                   text=lowercase   ,				add_special_tokens=lowercase   ,				padding=lowercase   ,				truncation=lowercase   ,				max_length=lowercase   ,				stride=lowercase   ,				pad_to_multiple_of=lowercase   ,				return_attention_mask=lowercase   ,				return_overflowing_tokens=lowercase   ,				return_special_tokens_mask=lowercase   ,				return_offsets_mapping=lowercase   ,				return_token_type_ids=lowercase   ,				return_length=lowercase   ,				verbose=lowercase   ,				return_tensors=lowercase   ,				**lowercase   ,				)
           else:
               UpperCAmelCase			       =		None

           if text_encoding is not None:
               encoding_image_processor.update(lowercase				)

           return encoding_image_processor
       def     A      (	self						:	Union[str, Any]   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Dict   ,				*lowercase						:	List[str]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)







       @property
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)










'''simple docstring'''







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."     )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )
    else:
        return _concatenate_iterable_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )




