



"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_			   =					{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			pass

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_			   =					[
			    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RoCBertForCausalLM''',
			    '''RoCBertForMaskedLM''',
			    '''RoCBertForMultipleChoice''',
			    '''RoCBertForPreTraining''',
			    '''RoCBertForQuestionAnswering''',
			    '''RoCBertForSequenceClassification''',
			    '''RoCBertForTokenClassification''',
			    '''RoCBertLayer''',
			    '''RoCBertModel''',
			    '''RoCBertPreTrainedModel''',
			    '''load_tf_weights_in_roc_bert''',
			]

if TYPE_CHECKING:
			from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
			from .tokenization_roc_bert import RoCBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						raise OptionalDependencyNotAvailable()

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roc_bert import (
						    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoCBertForCausalLM,
						    RoCBertForMaskedLM,
						    RoCBertForMultipleChoice,
						    RoCBertForPreTraining,
						    RoCBertForQuestionAnswering,
						    RoCBertForSequenceClassification,
						    RoCBertForTokenClassification,
						    RoCBertLayer,
						    RoCBertModel,
						    RoCBertPreTrainedModel,
						    load_tf_weights_in_roc_bert,
						)


else:
			import sys

			a_			   =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""

import warnings

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







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		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."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		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__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											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:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







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




