
import warnings

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






class     a_       (			SCREAMING_SNAKE_CASE_							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					int						=  ['image_processor', 'tokenizer']
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  'AutoImageProcessor'
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  'AutoTokenizer'






       def __init__(		self				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				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.'''				,       SCREAMING_SNAKE_CASE_				,       )
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = kwargs.pop('''feature_extractor'''   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You have to specify an image_processor.'''   )
           if tokenizer is None:
               raise ValueError('''You have to specify a tokenizer.'''   )

           super().__init__(SCREAMING_SNAKE_CASE_				,       SCREAMING_SNAKE_CASE_   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.image_processor






       def __call__(		self				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       **_lowerCamelCase   )					->Optional[int]:






           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:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.tokenizer(SCREAMING_SNAKE_CASE_				,       return_tensors=SCREAMING_SNAKE_CASE_				,       **SCREAMING_SNAKE_CASE_   )

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

           if text is not None and images is not None:
               SCREAMING_SNAKE_CASE				:				Dict				      = image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_   )				,       tensor_type=SCREAMING_SNAKE_CASE_   )






       def 	__lowerCAmelCase     (		self				,       *_lowerCamelCase				,       **_lowerCamelCase   )					->Optional[Any]:
           return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_				,       **SCREAMING_SNAKE_CASE_   )






       def 	__lowerCAmelCase     (		self				,       *_lowerCamelCase				,       **_lowerCamelCase   )					->str:
           return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_				,       **SCREAMING_SNAKE_CASE_   )






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.tokenizer.model_input_names
           SCREAMING_SNAKE_CASE				:				str				      = self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,       SCREAMING_SNAKE_CASE_				,       )
           return self.image_processor_class






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,       SCREAMING_SNAKE_CASE_				,       )
           return self.image_processor


from typing import TYPE_CHECKING

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


lowercase__				:				Dict						=      {
    "configuration_switch_transformers": [
        "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwitchTransformersConfig",
        "SwitchTransformersOnnxConfig",
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__				:				List[Any]						=      [
				    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "SwitchTransformersEncoderModel",
				    "SwitchTransformersForConditionalGeneration",
				    "SwitchTransformersModel",
				    "SwitchTransformersPreTrainedModel",
				    "SwitchTransformersTop1Router",
				    "SwitchTransformersSparseMLP",
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowercase__				:				List[Any]						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




