



import warnings

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



class 	lowerCamelCase__      (							snake_case_):




		'''simple docstring'''



		snake_case_     =["""image_processor""", """tokenizer"""]
		snake_case_     ="""CLIPImageProcessor"""
		snake_case_     =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



		def __init__(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,**__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					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		,)
										lowerCAmelCase__					:    Optional[int]												=					kwargs.pop('''feature_extractor'''			)

						lowerCAmelCase__					:    str												=					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			)					->      Union[str, Any]:

						"""simple docstring"""






						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:
										lowerCAmelCase__					:    List[str]												=					self.tokenizer(__lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase			)

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

						if text is not None and images is not None:
										lowerCAmelCase__					:    Union[str, Any]												=					image_features.pixel_values
										return encoding
						elif text is not None:
										return encoding
						else:
										return BatchEncoding(data=dict(**__lowerCamelCase			)		,tensor_type=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      int:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






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



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.tokenizer.model_input_names
						lowerCAmelCase__					:    Optional[int]												=					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case    :		Optional[int]  ={
    'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
    'processing_vision_text_dual_encoder': ['VisionTextDualEncoderProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		int  =['VisionTextDualEncoderModel']


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =['FlaxVisionTextDualEncoderModel']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['TFVisionTextDualEncoderModel']


if TYPE_CHECKING:
							from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
							from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure)

