"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class  UpperCamelCase					(				__a   ):
       SCREAMING_SNAKE_CASE_			=      ["""image_processor""", """tokenizer"""]
       SCREAMING_SNAKE_CASE_			=      """CLIPImageProcessor"""
       SCREAMING_SNAKE_CASE_			=      ("""CLIPTokenizer""", """CLIPTokenizerFast""")







       def __init__( self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      Union[str, Any]:
            snake_case_					       =					None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                     ' instead.',							lowerCAmelCase__,							)
                 snake_case_					       =					kwargs.pop('feature_extractor')

            snake_case_					       =					image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                 raise ValueError('You need to specify an `image_processor`.')
            if tokenizer is None:
                 raise ValueError('You need to specify a `tokenizer`.')

            super().__init__(lowerCAmelCase__,							lowerCAmelCase__)







       def __call__( self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      str:

            if text is None and images is None:
                 raise ValueError('You have to specify either text or images. Both cannot be none.')

            if text is not None:
                 snake_case_					       =					self.tokenizer(lowerCAmelCase__,							return_tensors=lowerCAmelCase__,							**lowerCAmelCase__)

            if images is not None:
                 snake_case_					       =					self.image_processor(lowerCAmelCase__,							return_tensors=lowerCAmelCase__,							**lowerCAmelCase__)

            if text is not None and images is not None:
                 snake_case_					       =					image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**lowerCAmelCase__),							tensor_type=lowerCAmelCase__)







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Any:
            return self.tokenizer.batch_decode(*lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      str:
            return self.tokenizer.decode(*lowerCAmelCase__,							**lowerCAmelCase__)







       @property
       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.tokenizer.model_input_names
            snake_case_					       =					self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







       @property
       def 		a_						( self)      ->      List[Any]:
            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',							lowerCAmelCase__,							)
            return self.image_processor_class







       @property
       def 		a_						( self)      ->      str:
            warnings.warn(
                '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',							lowerCAmelCase__,							)
            return self.image_processor







'''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     )




