import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      ["image_processor", "tokenizer"]
    __a  =      "FlavaImageProcessor"
    __a  =      ("BertTokenizer", "BertTokenizerFast")
    def __init__(  self , lowerCAmelCase=None , lowerCAmelCase=None , **lowerCAmelCase   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[Any]=      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__:   Optional[Any]=      kwargs.pop('''feature_extractor'''   )

     SCREAMING_SNAKE_CASE__:   str=      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   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.image_processor
    def __call__(  self , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = True , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = None , lowerCAmelCase = 0 , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = False , lowerCAmelCase = True , lowerCAmelCase = None , **lowerCAmelCase , )       ->					List[Any]:

     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__:   Any=      self.tokenizer(
          text=lowerCAmelCase , add_special_tokens=lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase , max_length=lowerCAmelCase , stride=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_token_type_ids=lowerCAmelCase , return_attention_mask=lowerCAmelCase , return_overflowing_tokens=lowerCAmelCase , return_special_tokens_mask=lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , return_length=lowerCAmelCase , verbose=lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase , )
     if images is not None:
      SCREAMING_SNAKE_CASE__:   Tuple=      self.image_processor(
          lowerCAmelCase , return_image_mask=lowerCAmelCase , return_codebook_pixels=lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase , )

     if text is not None and images is not None:
      encoding.update(lowerCAmelCase   )
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**lowerCAmelCase   ) , tensor_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Dict:
     return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , *lowerCAmelCase , **lowerCAmelCase   )       ->					Any:
     return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE__:   str=      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )
    @property
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     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   )       ->					Optional[Any]:
     warnings.warn(
         '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowerCAmelCase , )
     return self.image_processor


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

