






'''simple docstring'''



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''AutoImageProcessor'''
      UpperCamelCase_								=			'''AutoTokenizer'''






      def __init__( self			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[str]		)  ->  str:


       '''simple docstring'''

       super().__init__(UpperCAmelCase     ,      UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =self.image_processor






      def __call__( self			:		Dict     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		int=None     ,      UpperCAmelCase			:		str=None     ,      **UpperCAmelCase			:		Dict		)  ->  int:


       '''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:
        lowercase					:   Optional[int]				  =self.tokenizer(UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase		)

       if images is not None:
        lowercase					:   Dict				  =self.image_processor(UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase		)

       if text is not None and images is not None:
        lowercase					:   Optional[int]				  =image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**UpperCAmelCase		)     ,      tensor_type=UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		List[str]     ,      **UpperCAmelCase			:		int		)  ->  List[str]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		str		)  ->  List[Any]:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       return ["input_ids", "attention_mask", "pixel_values"]











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

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



