
from typing import TYPE_CHECKING

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


lowerCamelCase__     :		str        = {
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		List[Any]        = [
         """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLMForMultipleChoice""",
         """XLMForQuestionAnswering""",
         """XLMForQuestionAnsweringSimple""",
         """XLMForSequenceClassification""",
         """XLMForTokenClassification""",
         """XLMModel""",
         """XLMPreTrainedModel""",
         """XLMWithLMHeadModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		List[Any]        = [
         """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLMForMultipleChoice""",
         """TFXLMForQuestionAnsweringSimple""",
         """TFXLMForSequenceClassification""",
         """TFXLMForTokenClassification""",
         """TFXLMMainLayer""",
         """TFXLMModel""",
         """TFXLMPreTrainedModel""",
         """TFXLMWithLMHeadModel""",
     ]


if TYPE_CHECKING:
     from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
     from .tokenization_xlm import XLMTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm import (
              XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMForMultipleChoice,
              XLMForQuestionAnswering,
              XLMForQuestionAnsweringSimple,
              XLMForSequenceClassification,
              XLMForTokenClassification,
              XLMModel,
              XLMPreTrainedModel,
              XLMWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm import (
              TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMForMultipleChoice,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForSequenceClassification,
              TFXLMForTokenClassification,
              TFXLMMainLayer,
              TFXLMModel,
              TFXLMPreTrainedModel,
              TFXLMWithLMHeadModel,
          )

else:
     import sys

     lowerCamelCase__     :		str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








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



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


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


   '''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__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

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

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   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   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




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






