



"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """tokenizer"""]
			UpperCamelCase__			      =	"""ViTImageProcessor"""
			UpperCamelCase__			      =	("""CLIPTokenizer""", """CLIPTokenizerFast""")



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

							_UpperCAmelCase					=    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__(__UpperCamelCase				,   __UpperCamelCase   )



			def __call__(		self     :    Optional[int]				,   __UpperCamelCase     :    int=None				,   __UpperCamelCase     :    int=None				,   __UpperCamelCase     :    Optional[Any]=None				,   __UpperCamelCase     :    Dict=None				,   **__UpperCamelCase     :    str   )->Union[str, Any]:
							if text is None and visual_prompt is None and images is None:
											raise ValueError('''You have to specify either text, visual prompt or images.'''   )

							if text is not None and visual_prompt is not None:
											raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

							if text is not None:
											_UpperCAmelCase					=    self.tokenizer(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )

							if visual_prompt is not None:
											_UpperCAmelCase					=    self.image_processor(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )

							if images is not None:
											_UpperCAmelCase					=    self.image_processor(__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase   )

							if visual_prompt is not None and images is not None:
											_UpperCAmelCase					=    {
											    '''pixel_values''': image_features.pixel_values,
											    '''conditional_pixel_values''': prompt_features.pixel_values,
											}
											return encoding
							elif text is not None and images is not None:
											_UpperCAmelCase					=    image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							elif visual_prompt is not None:
											_UpperCAmelCase					=    {
											    '''conditional_pixel_values''': prompt_features.pixel_values,
											}
											return encoding
							else:
											return BatchEncoding(data=dict(**__UpperCamelCase   )				,   tensor_type=__UpperCamelCase   )



			def  lowercase__						(		self     :    Dict				,   *__UpperCamelCase     :    str				,   **__UpperCamelCase     :    Union[str, Any]   )->Union[str, Any]:
							return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple				,   *__UpperCamelCase     :    Any				,   **__UpperCamelCase     :    Tuple   )->str:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



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



			@property
			def  lowercase__						(		self     :    Tuple   )->Union[str, Any]:
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor








"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    NestedDataStructureLike[PathLike]				,   __UpperCamelCase     :    Optional[NamedSplit] = None				,   __UpperCamelCase     :    Optional[Features] = None				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Optional[int] = None				,   **__UpperCamelCase     :    Optional[Any]				,   )->Any:
							super().__init__(
							    __UpperCamelCase				,   split=__UpperCamelCase				,   features=__UpperCamelCase				,   cache_dir=__UpperCamelCase				,   keep_in_memory=__UpperCamelCase				,   streaming=__UpperCamelCase				,   num_proc=__UpperCamelCase				,   **__UpperCamelCase				,   )
							_UpperCAmelCase					=    path_or_paths if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else {self.split: path_or_paths}
							_UpperCAmelCase					=    Text(
							    cache_dir=__UpperCamelCase				,   data_files=__UpperCamelCase				,   features=__UpperCamelCase				,   **__UpperCamelCase				,   )



			def  lowercase__						(		self     :    Optional[Any]   )->str:
							# Build iterable dataset
							if self.streaming:
											_UpperCAmelCase					=    self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None

											self.builder.download_and_prepare(
											    download_config=__UpperCamelCase				,   download_mode=__UpperCamelCase				,   verification_mode=__UpperCamelCase				,   base_path=__UpperCamelCase				,   num_proc=self.num_proc				,   )
											_UpperCAmelCase					=    self.builder.as_dataset(
											    split=self.split				,   verification_mode=__UpperCamelCase				,   in_memory=self.keep_in_memory   )
							return dataset



