




import warnings

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





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["image_processor", "tokenizer"]
 lowercase_							       =       "CLIPImageProcessor"
 lowercase_							       =       ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						List[Any]=None							,							**_lowerCAmelCase    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     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							,							)
             SCREAMING_SNAKE_CASE_      		=     kwargs.pop('feature_extractor'		)

       SCREAMING_SNAKE_CASE_      		=     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    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]=None							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Union[str, Any]=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_      		=     self.tokenizer(_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase		)

       if images is not None:
             SCREAMING_SNAKE_CASE_      		=     self.image_processor(_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase		)

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



 def  lowerCAmelCase_    (				self    :						str							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						Any		):
       return self.tokenizer.batch_decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							*_lowerCAmelCase    :						Union[str, Any]							,							**_lowerCAmelCase    :						str		):
       return self.tokenizer.decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)




 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE_      		=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





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,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase__					:  List[Any]      							=				TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				[
       'MT5EncoderModel',
       'MT5ForConditionalGeneration',
       'MT5ForQuestionAnswering',
       'MT5Model',
       'MT5PreTrainedModel',
       'MT5Stack',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   lowerCamelCase__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )