

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 	_A						(					_A):
  SCREAMING_SNAKE_CASE      : Any    =					['''image_processor''', '''tokenizer''']
  SCREAMING_SNAKE_CASE      : Union[str, Any]    =					'''VideoMAEImageProcessor'''
  SCREAMING_SNAKE_CASE      : Optional[Any]    =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')

  def __init__(			self	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=None	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'	,   UpperCamelCase__	,   )
              SCREAMING_SNAKE_CASE_			:					List[str]          =       kwargs.pop('feature_extractor'   )

        SCREAMING_SNAKE_CASE_			:					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__(UpperCamelCase__	,   UpperCamelCase__   )
        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.image_processor

  def __call__(			self	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=None	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        if text is None and videos is None:
              raise ValueError('You have to specify either text or videos. Both cannot be none.'   )

        if text is not None:
              SCREAMING_SNAKE_CASE_			:					Any          =       self.tokenizer(UpperCamelCase__	,   return_tensors=UpperCamelCase__	,   **UpperCamelCase__   )

        if videos is not None:
              SCREAMING_SNAKE_CASE_			:					str          =       self.image_processor(UpperCamelCase__	,   return_tensors=UpperCamelCase__	,   **UpperCamelCase__   )

        if text is not None and videos is not None:
              SCREAMING_SNAKE_CASE_			:					Any          =       image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**UpperCamelCase__   )	,   tensor_type=UpperCamelCase__   )

  def 	UpperCAmelCase      (			self	,   *_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







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

  def 	UpperCAmelCase      (			self	,   *_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







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

  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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



  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase       :				Dict         =					logging.get_logger(__name__)
class 	_A						(					__magic_name__):
  SCREAMING_SNAKE_CASE      : Any    =					['''pixel_values''']

  def __init__(			self	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = PIL.Image.NEAREST	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = 1 / 255	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = True	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					str          =       size if size is not None else {'height': 346, 'width': 346}
        SCREAMING_SNAKE_CASE_			:					str          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					int          =       crop_size if crop_size is not None else {'height': 289, 'width': 289}
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       get_size_dict(_SCREAMING_SNAKE_CASE	,   param_name='crop_size'   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       do_resize
        SCREAMING_SNAKE_CASE_			:					int          =       size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       resample
        SCREAMING_SNAKE_CASE_			:					int          =       do_center_crop
        SCREAMING_SNAKE_CASE_			:					List[str]          =       crop_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_rescale
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       rescale_factor
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       rescale_offset
        SCREAMING_SNAKE_CASE_			:					Dict          =       do_normalize
        SCREAMING_SNAKE_CASE_			:					str          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        SCREAMING_SNAKE_CASE_			:					List[str]          =       image_std if image_std is not None else IMAGENET_STANDARD_STD
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       include_top

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = PIL.Image.NEAREST	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        if "height" not in size or "width" not in size:
              raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"   )
        return resize(
            _SCREAMING_SNAKE_CASE	,   size=(size['height'], size['width'])	,   resample=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        if "height" not in size or "width" not in size:
              raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"   )
        return center_crop(_SCREAMING_SNAKE_CASE	,   size=(size['height'], size['width'])	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        if offset:
              SCREAMING_SNAKE_CASE_			:					Dict          =       (image - 127.5) * scale
              if data_format is not None:
                    SCREAMING_SNAKE_CASE_			:					Tuple          =       to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					int          =       rescaled_image.astype(np.floataa   )
        else:
              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       rescale(_SCREAMING_SNAKE_CASE	,   scale=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        return rescaled_image

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        return normalize(_SCREAMING_SNAKE_CASE	,   mean=_SCREAMING_SNAKE_CASE	,   std=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )



  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_center_crop if do_center_crop is not None else self.do_center_crop
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE_			:					int          =       rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE_			:					Tuple          =       rescale_offset if rescale_offset is not None else self.rescale_offset
        SCREAMING_SNAKE_CASE_			:					Dict          =       do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE_			:					str          =       image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       include_top if include_top is not None else self.include_top

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       size if size is not None else self.size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Any          =       crop_size if crop_size is not None else self.crop_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       get_size_dict(_SCREAMING_SNAKE_CASE	,   param_name='crop_size'   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       make_list_of_images(_SCREAMING_SNAKE_CASE   )

        if not valid_images(_SCREAMING_SNAKE_CASE   ):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'   )

        if do_resize and size is None or resample is None:
              raise ValueError('Size and resample must be specified if do_resize is True.'   )

        if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.'   )

        if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE_			:					int          =       [to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE_			:					Dict          =       [self.resize(image=_SCREAMING_SNAKE_CASE	,   size=_SCREAMING_SNAKE_CASE	,   resample=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_center_crop:
              SCREAMING_SNAKE_CASE_			:					str          =       [self.center_crop(image=_SCREAMING_SNAKE_CASE	,   size=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_rescale:
              SCREAMING_SNAKE_CASE_			:					Tuple          =       [self.rescale(image=_SCREAMING_SNAKE_CASE	,   scale=_SCREAMING_SNAKE_CASE	,   offset=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_normalize:
              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       [self.normalize(image=_SCREAMING_SNAKE_CASE	,   mean=_SCREAMING_SNAKE_CASE	,   std=_SCREAMING_SNAKE_CASE   ) for image in images]

        if include_top:
              SCREAMING_SNAKE_CASE_			:					Dict          =       [self.normalize(image=_SCREAMING_SNAKE_CASE	,   mean=[0, 0, 0]	,   std=_SCREAMING_SNAKE_CASE   ) for image in images]

        SCREAMING_SNAKE_CASE_			:					Dict          =       [to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ) for image in images]

        SCREAMING_SNAKE_CASE_			:					Any          =       {'pixel_values': images}
        return BatchFeature(data=_SCREAMING_SNAKE_CASE	,   tensor_type=_SCREAMING_SNAKE_CASE   )






