





"""simple docstring"""






import warnings

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




class 				__lowercase			(			_UpperCamelCase							):







		'''simple docstring'''




		__lowerCAmelCase  =  ['''image_processor''', '''tokenizer''']
		__lowerCAmelCase  =  '''ViTImageProcessor'''
		__lowerCAmelCase  =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







		def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
						__a :						Union[str, Any]			       =  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	,			)
										__a :						List[str]			       =  kwargs.pop('''feature_extractor'''     )

						__a :						int			       =  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	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
						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:
										__a :						Union[str, Any]			       =  self.tokenizer(_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase     )

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

						if images is not None:
										__a :						Optional[Any]			       =  self.image_processor(_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase     )

						if visual_prompt is not None and images is not None:
										__a :						int			       =  {
										    '''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:
										__a :						Tuple			       =  image_features.pixel_values
										return encoding
						elif text is not None:
										return encoding
						elif visual_prompt is not None:
										__a :						Optional[Any]			       =  {
										    '''conditional_pixel_values''': prompt_features.pixel_values,
										}
										return encoding
						else:
										return BatchEncoding(data=dict(**_UpperCAmelCase     )	,			tensor_type=_UpperCAmelCase     )







		def  _lowerCamelCase		(						self	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
						return self.tokenizer.batch_decode(*_UpperCAmelCase	,			**_UpperCAmelCase     )







		def  _lowerCamelCase		(						self	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
						return self.tokenizer.decode(*_UpperCAmelCase	,			**_UpperCAmelCase     )







		@property
		def  _lowerCamelCase		(						self     ):
						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  _lowerCamelCase		(						self     ):
						warnings.warn(
						    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,			_UpperCAmelCase	,			)
						return self.image_processor






"""simple docstring"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=_UpperCamelCase							)
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  field(default='''language-modeling'''   ,      metadata={'''include_in_asdict_even_if_is_default''': True}							)

					__lowerCAmelCase  =  Features({'''text''': Value('''string'''							)}							)
					__lowerCAmelCase  =  Features({}							)
					__lowerCAmelCase  =  "text"







					@property
					def  _lowerCamelCase		(						self     ):
									return {self.text_column: "text"}