


"""simple docstring"""




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				['image_processor', 'tokenizer']
		__UpperCAmelCase		:			Optional[Any]										=				'ViTImageProcessor'
		__UpperCAmelCase		:			str										=				('CLIPTokenizer', 'CLIPTokenizerFast')

		def __init__(  self							, _a=None							, _a=None							, **_a      ):
				__a           =			None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
						    ''' instead.'''							, _a							, )
						__a           =			kwargs.pop('''feature_extractor'''      )

				__a           =			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__(_a							, _a      )

		def __call__(  self							, _a=None							, _a=None							, _a=None							, _a=None							, **_a      ):
				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           =			self.tokenizer(_a							, return_tensors=_a							, **_a      )

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

				if images is not None:
						__a           =			self.image_processor(_a							, return_tensors=_a							, **_a      )

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

		def 			__UpperCAmelCase       (  self							, *_a							, **_a      ):
				return self.tokenizer.batch_decode(*_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, *_a							, **_a      ):
				return self.tokenizer.decode(*_a							, **_a      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				warnings.warn(
				    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							, _a							, )
				return self.image_processor_class





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










"""simple docstring"""




import warnings

from ..trainer import Trainer
from ..utils import logging


lowercase_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a=None							, **_a      ):
				warnings.warn(
				    '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
				    '''instead.'''							, _a							, )
				super().__init__(args=_a							, **_a      )






