



'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    ["""image_processor""", """tokenizer"""]
					A_         =    """AutoImageProcessor"""
					A_         =    """AutoTokenizer"""




					def __init__(	self					:						List[str]  ,	UpperCamelCase_					:						Tuple=None  ,	UpperCamelCase_					:						int=None  ,	**UpperCamelCase_					:						Tuple       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							str  	=		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_  ,	)
							_lowercase       :							Union[str, Any]  	=		kwargs.pop('feature_extractor'       )

						_lowercase       :							Any  	=		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_       )
						_lowercase       :							int  	=		self.image_processor
						_lowercase       :							List[str]  	=		False




					def __call__(	self					:						Dict  ,	*UpperCamelCase_					:						Optional[int]  ,	**UpperCamelCase_					:						Optional[Any]       )	->							Optional[Any]:



						'''simple docstring'''





						if self._in_target_context_manager:
							return self.current_processor(*UpperCamelCase_  ,	**UpperCamelCase_       )

						_lowercase       :							List[str]  	=		kwargs.pop('images'  ,	UpperCamelCase_       )
						_lowercase       :							Dict  	=		kwargs.pop('text'  ,	UpperCamelCase_       )
						if len(UpperCamelCase_       ) > 0:
							_lowercase       :							str  	=		args[0]
							_lowercase       :							List[Any]  	=		args[1:]

						if images is None and text is None:
							raise ValueError('You need to specify either an `images` or `text` input to process.'       )

						if images is not None:
							_lowercase       :							List[Any]  	=		self.image_processor(UpperCamelCase_  ,	*UpperCamelCase_  ,	**UpperCamelCase_       )
						if text is not None:
							_lowercase       :							Any  	=		self.tokenizer(UpperCamelCase_  ,	**UpperCamelCase_       )

						if text is None:
							return inputs
						elif images is None:
							return encodings
						else:
							_lowercase       :							int  	=		encodings['input_ids']
							return inputs




					def     __UpperCAmelCase  (	self					:						Any  ,	*UpperCamelCase_					:						Dict  ,	**UpperCamelCase_					:						Dict       )	->							List[str]:



						'''simple docstring'''





						return self.tokenizer.batch_decode(*UpperCamelCase_  ,	**UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						int  ,	*UpperCamelCase_					:						Optional[Any]  ,	**UpperCamelCase_					:						Optional[int]       )	->							int:



						'''simple docstring'''





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




					@contextmanager
					def     __UpperCAmelCase  (	self					:						int       )	->							Any:



						'''simple docstring'''





						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your images inputs, or in a separate call.'       )
						_lowercase       :							Union[str, Any]  	=		True
						_lowercase       :							Dict  	=		self.tokenizer
						yield
						_lowercase       :							Tuple  	=		self.image_processor
						_lowercase       :							int  	=		False




					@property
					def     __UpperCAmelCase  (	self					:						int       )	->							List[Any]:



						'''simple docstring'''





						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     __UpperCAmelCase  (	self					:						Optional[int]       )	->							List[str]:



						'''simple docstring'''





						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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:     List[Any]      ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Optional[Any]      =[
		    '''UMT5EncoderModel''',
		    '''UMT5ForConditionalGeneration''',
		    '''UMT5ForQuestionAnswering''',
		    '''UMT5Model''',
		    '''UMT5PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

		_A					:     Optional[int]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
