
"""simple docstring"""





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






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'AutoImageProcessor'
		lowercase__         = 'AutoTokenizer'



		def __init__(   self							,						__a							,						__a)			->			Any:






							'''simple docstring'''
							super().__init__(__a							,						__a)
							_UpperCamelCase									=    self.image_processor



		def __call__(   self							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			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:
												_UpperCamelCase									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

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



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)




		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return ["input_ids", "attention_mask", "pixel_values"]








from typing import TYPE_CHECKING

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


_lowercase											=    {
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase											=    [
     """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """FalconForCausalLM""",
     """FalconModel""",
     """FalconPreTrainedModel""",
     """FalconForSequenceClassification""",
     """FalconForTokenClassification""",
     """FalconForQuestionAnswering""",
 ]


if TYPE_CHECKING:
 from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_falcon import (
      FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
      FalconPreTrainedModel,
  )


else:
 import sys

 _lowercase											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
