


import warnings
from typing import List, Optional, Union

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

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      ["image_processor", "tokenizer"]
     A:		List[str]			=      "ViltImageProcessor"
     A:		Optional[int]			=      ("BertTokenizer", "BertTokenizerFast")


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





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, 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__							,	)
       UpperCamelCase__					:    str          =   kwargs.pop('''feature_extractor'''			)

      UpperCamelCase__					:    Tuple          =   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__			)
      UpperCamelCase__					:    Optional[int]          =   self.image_processor


     def __call__(      self							:     Union[str, Any]							,	lowerCamelCase__							:     int							,	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__							:     Optional[bool] = None							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	**lowerCamelCase__							:     List[str]							,	)  ->      BatchEncoding:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   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__							,	)
      # add pixel_values + pixel_mask
      UpperCamelCase__					:    Tuple          =   self.image_processor(lowerCamelCase__							,	return_tensors=lowerCamelCase__			)
      encoding.update(lowerCamelCase__			)

      return encoding


     def      UpperCAmelCase__	(      self							:     int							,	*lowerCamelCase__							:     Optional[Any]							,	**lowerCamelCase__							:     Any			)  ->      Dict:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Any							,	*lowerCamelCase__							:     int							,	**lowerCamelCase__							:     Optional[Any]			)  ->      Tuple:





      '''simple docstring'''






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


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





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.tokenizer.model_input_names
      UpperCamelCase__					:    Any          =   self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      int:





      '''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							:     int			)  ->      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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase       :       Dict						       =				{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       str						       =				["MobileViTFeatureExtractor"]
       __UpperCamelCase       :       Optional[Any]						       =				["MobileViTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Union[str, Any]						       =				[
           "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileViTForImageClassification",
           "MobileViTForSemanticSegmentation",
           "MobileViTModel",
           "MobileViTPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMobileViTForImageClassification",
           "TFMobileViTForSemanticSegmentation",
           "TFMobileViTModel",
           "TFMobileViTPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilevit import MobileViTFeatureExtractor
              from .image_processing_mobilevit import MobileViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilevit import (
                  MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileViTForImageClassification,
                  MobileViTForSemanticSegmentation,
                  MobileViTModel,
                  MobileViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mobilevit import (
                  TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMobileViTForImageClassification,
                  TFMobileViTForSemanticSegmentation,
                  TFMobileViTModel,
                  TFMobileViTPreTrainedModel,
              )


else:
       import sys

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


