




"""simple docstring"""






import warnings

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




class   a__			(       _lowerCamelCase  ):
      snake_case__             = ['''image_processor''', '''tokenizer''']
      snake_case__             = '''ChineseCLIPImageProcessor'''
      snake_case__             = ('''BertTokenizer''', '''BertTokenizerFast''')





      def __init__(							self      :	Dict     ,a__      :	Union[str, Any]=None     ,a__      :	Union[str, Any]=None     ,**a__      :	Dict)						->       int:




           """simple docstring"""







           _lowerCAmelCase: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.'''     ,A__     ,)
                _lowerCAmelCase:Union[str, Any]									=  kwargs.pop('''feature_extractor''')

           _lowerCAmelCase:Optional[int]									=  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__(A__     ,A__)
           _lowerCAmelCase:List[Any]									=  self.image_processor





      def __call__(							self      :	Union[str, Any]     ,a__      :	Optional[Any]=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[Any]=None     ,**a__      :	Union[str, Any])						->       Optional[int]:




           """simple docstring"""







           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:
                _lowerCAmelCase:List[str]									=  self.tokenizer(A__     ,return_tensors=A__     ,**A__)

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

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





      def       __UpperCamelCase     (							self      :	List[str]     ,*a__      :	Tuple     ,**a__      :	List[Any])						->       Dict:




           """simple docstring"""







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





      def       __UpperCamelCase     (							self      :	Dict     ,*a__      :	Optional[Any]     ,**a__      :	Optional[int])						->       Optional[Any]:




           """simple docstring"""







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





      @property
      def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




           """simple docstring"""







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







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




           """simple docstring"""







           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,A__     ,)
           return self.image_processor_class












"""simple docstring"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCamelCase__						=			logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	int     ,*a__      :	Optional[Any]     ,**a__      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											super().__init__(*a__     ,**a__)
											requires_backends(self     ,'''vision''')
											self.check_model_type(a__)





						def __call__(							self      :	str     ,a__      :	Union[str, List[str], "Image.Image", List["Image.Image"]]     ,**a__      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											return super().__call__(a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,**a__      :	List[Any])						->       Any:




											"""simple docstring"""







											return {}, {}, {}





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[int])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  load_image(a__)
											_lowerCAmelCase:int									=  image.size
											_lowerCAmelCase:int									=  self.image_processor(images=a__     ,return_tensors=self.framework)
											return model_inputs





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model(**a__)
											return model_outputs







						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Dict)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  model_outputs.predicted_depth
											_lowerCAmelCase:Union[str, Any]									=  torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1)     ,size=self.image_size[::-1]     ,mode='''bicubic'''     ,align_corners=a__)
											_lowerCAmelCase:List[str]									=  prediction.squeeze().cpu().numpy()
											_lowerCAmelCase:Any									=  (output * 255 / np.max(a__)).astype('''uint8''')
											_lowerCAmelCase:Dict									=  Image.fromarray(a__)
											_lowerCAmelCase:Tuple									=  {}
											_lowerCAmelCase:Optional[int]									=  predicted_depth
											_lowerCAmelCase:str									=  depth
											return output_dict






