



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 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: int          =		["""image_processor""", """tokenizer"""]
   snake_case_					: Dict          =		"""BlipImageProcessor"""
   snake_case_					: Optional[Any]          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Optional[int])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Tuple					=					False
    super().__init__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  List[str]					=					self.image_processor

   def __call__(					self							:			Optional[int]				,		lowerCAmelCase							:			ImageInput = None				,		lowerCAmelCase							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Union[bool, str, PaddingStrategy] = False				,		lowerCAmelCase							:			Union[bool, str, TruncationStrategy] = None				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			int = 0				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			Optional[bool] = None				,		lowerCAmelCase							:			bool = False				,		lowerCAmelCase							:			bool = False				,		lowerCAmelCase							:			bool = False				,		lowerCAmelCase							:			bool = False				,		lowerCAmelCase							:			bool = False				,		lowerCAmelCase							:			bool = True				,		lowerCAmelCase							:			Optional[Union[str, TensorType]] = None				,		**lowerCAmelCase							:			Optional[Any]				,		)				->				BatchEncoding:

    """simple docstring"""







    if images is None and text is None:
     raise ValueError("""You have to specify either images or text.""")

    # Get only text
    if images is None:
     _snake_case    :  Optional[Any]					=					self.tokenizer
     _snake_case    :  Union[str, Any]					=					self.tokenizer(
         text=lowerCAmelCase				,		add_special_tokens=lowerCAmelCase				,		padding=lowerCAmelCase				,		truncation=lowerCAmelCase				,		max_length=lowerCAmelCase				,		stride=lowerCAmelCase				,		pad_to_multiple_of=lowerCAmelCase				,		return_attention_mask=lowerCAmelCase				,		return_overflowing_tokens=lowerCAmelCase				,		return_special_tokens_mask=lowerCAmelCase				,		return_offsets_mapping=lowerCAmelCase				,		return_token_type_ids=lowerCAmelCase				,		return_length=lowerCAmelCase				,		verbose=lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase				,		)
     return text_encoding

    # add pixel_values
    _snake_case    :  int					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase)

    if text is not None:
     _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_attention_mask=lowerCAmelCase				,		return_overflowing_tokens=lowerCAmelCase				,		return_special_tokens_mask=lowerCAmelCase				,		return_offsets_mapping=lowerCAmelCase				,		return_token_type_ids=lowerCAmelCase				,		return_length=lowerCAmelCase				,		verbose=lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase				,		)
    else:
     _snake_case    :  Dict					=					None

    if text_encoding is not None:
     encoding_image_processor.update(lowerCAmelCase)

    return encoding_image_processor

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		*lowerCAmelCase							:			Optional[Any]				,		**lowerCAmelCase							:			Optional[Any])				->				Optional[Any]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			str)				->				int:

    """simple docstring"""







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


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Dict:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					self.tokenizer.model_input_names
    _snake_case    :  Optional[int]					=					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_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

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