from typing import TYPE_CHECKING

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


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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """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:
       a_							=      [
           """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

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
import warnings

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


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= ['image_processor', 'tokenizer']
  lowerCAmelCase__		:	Any    		= 'CLIPImageProcessor'
  lowerCAmelCase__		:	Any    		= ('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(				self:			str				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Any=None				,				**__lowerCAmelCase:			str   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       None
    if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,				__lowerCAmelCase				,				)
      __UpperCAmelCase						    =       kwargs.pop("feature_extractor"   )

    __UpperCAmelCase						    =       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__(__lowerCAmelCase				,				__lowerCAmelCase   )






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




    '''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(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if images is not None:
      __UpperCAmelCase						    =       self.image_processor(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    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(**__lowerCAmelCase   )				,				tensor_type=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str				,				*__lowerCAmelCase:			str				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Any				,				*__lowerCAmelCase:			List[Any]				,				**__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




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






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer.model_input_names
    __UpperCAmelCase						    =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






  @property
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor_class






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor



