



'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase	 =       {
    'configuration_chinese_clip': [
        'CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ChineseCLIPConfig',
        'ChineseCLIPOnnxConfig',
        'ChineseCLIPTextConfig',
        'ChineseCLIPVisionConfig',
    ],
    'processing_chinese_clip': ['ChineseCLIPProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase	 =       ['ChineseCLIPFeatureExtractor']
  lowerCamelCase	 =       ['ChineseCLIPImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase	 =       [
      'CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ChineseCLIPModel',
      'ChineseCLIPPreTrainedModel',
      'ChineseCLIPTextModel',
      'ChineseCLIPVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_chinese_clip import (
      CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ChineseCLIPConfig,
      ChineseCLIPOnnxConfig,
      ChineseCLIPTextConfig,
      ChineseCLIPVisionConfig,
  )
  from .processing_chinese_clip import ChineseCLIPProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_chinese_clip import (
        CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ChineseCLIPModel,
        ChineseCLIPPreTrainedModel,
        ChineseCLIPTextModel,
        ChineseCLIPVisionModel,
    )

else:
  import sys

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








'''simple docstring'''



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 _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    ["""image_processor""", """tokenizer"""]
				lowerCAmelCase__					    =    """ViltImageProcessor"""
				lowerCAmelCase__					    =    ("""BertTokenizer""", """BertTokenizerFast""")

				def __init__(							self  :  str  ,       _lowerCAmelCase  :  Optional[Any]=None  ,       _lowerCAmelCase  :  int=None  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=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  ,       )
																		__lowercase       	=kwargs.pop('feature_extractor')

											__lowercase       	=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)
											__lowercase       	=self.image_processor

				def __call__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Tuple  ,       _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  :  Any  ,       ):







											'''simple docstring'''

											__lowercase       	=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
											__lowercase       	=self.image_processor(_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase)
											encoding.update(_lowerCAmelCase)

											return encoding

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

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

				@property
				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

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

				@property
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''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 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

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



