'''simple docstring'''

from typing import TYPE_CHECKING

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


__snake_case       :   int  		=    {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   Optional[Any]  		=    [
          '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''VideoMAEForPreTraining''',
          '''VideoMAEModel''',
          '''VideoMAEPreTrainedModel''',
          '''VideoMAEForVideoClassification''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   str  		=    ['''VideoMAEFeatureExtractor''']
      __snake_case       :   Any  		=    ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
      from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_videomae import (
                VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                VideoMAEForPreTraining,
                VideoMAEForVideoClassification,
                VideoMAEModel,
                VideoMAEPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_videomae import VideoMAEFeatureExtractor
            from .image_processing_videomae import VideoMAEImageProcessor

else:
      import sys

      __snake_case       :   List[str]  		=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

'''simple docstring'''

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__snake_case       :   Any  		=    logging.get_logger(__name__)


if is_vision_available():
      import PIL
class 							lowercase_    (     _A						):
    a_             =   ["""pixel_values"""]







    def __init__(	self       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = PILImageResampling.BICUBIC       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = 1 / 2_5_5       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = True       ,     **UpperCamelCase__       ,     )				->       None:





     """simple docstring"""

     super().__init__(**UpperCamelCase__				)
     UpperCAmelCase_				       =					size if size is not None else {"shortest_edge": 2_2_4}
     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__       ,     default_to_square=UpperCamelCase__				)
     UpperCAmelCase_				       =					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__				)

     UpperCAmelCase_				       =					do_resize
     UpperCAmelCase_				       =					size
     UpperCAmelCase_				       =					resample
     UpperCAmelCase_				       =					do_center_crop
     UpperCAmelCase_				       =					crop_size
     UpperCAmelCase_				       =					do_rescale
     UpperCAmelCase_				       =					rescale_factor
     UpperCAmelCase_				       =					do_normalize
     UpperCAmelCase_				       =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     UpperCAmelCase_				       =					image_std if image_std is not None else OPENAI_CLIP_STD
     UpperCAmelCase_				       =					do_convert_rgb







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = PILImageResampling.BICUBIC       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       np.ndarray:





     """simple docstring"""

     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__       ,     default_to_square=UpperCamelCase__				)
     UpperCAmelCase_				       =					get_resize_output_image_size(
         UpperCamelCase__       ,     size=(size["height"], size["width"])       ,     default_to_square=UpperCamelCase__				)
     return resize(UpperCamelCase__       ,     size=UpperCamelCase__       ,     resample=UpperCamelCase__       ,     data_format=UpperCamelCase__       ,     **UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       np.ndarray:





     """simple docstring"""

     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__				)
     return center_crop(UpperCamelCase__       ,     size=(size["height"], size["width"])       ,     data_format=UpperCamelCase__       ,     **UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       int:





     """simple docstring"""

     return rescale(UpperCamelCase__       ,     scale=UpperCamelCase__       ,     data_format=UpperCamelCase__       ,     **UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       np.ndarray:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = ChannelDimension.FIRST       ,     **UpperCamelCase__       ,     )				->       PIL.Image.Image:





     """simple docstring"""

     UpperCAmelCase_				       =					do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase_				       =					size if size is not None else self.size
     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__       ,     default_to_square=UpperCamelCase__				)
     UpperCAmelCase_				       =					resample if resample is not None else self.resample
     UpperCAmelCase_				       =					do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase_				       =					crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__				)
     UpperCAmelCase_				       =					do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase_				       =					rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase_				       =					do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase_				       =					image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase_				       =					image_std if image_std is not None else self.image_std
     UpperCAmelCase_				       =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     UpperCAmelCase_				       =					make_list_of_images(UpperCamelCase__				)

     if not valid_images(UpperCamelCase__				):
      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:
      raise ValueError("Size 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."				)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
      UpperCAmelCase_				       =					[convert_to_rgb(UpperCamelCase__				) for image in images]

     # All transformations expect numpy arrays.
     UpperCAmelCase_				       =					[to_numpy_array(UpperCamelCase__				) for image in images]

     if do_resize:
      UpperCAmelCase_				       =					[self.resize(image=UpperCamelCase__       ,     size=UpperCamelCase__       ,     resample=UpperCamelCase__				) for image in images]

     if do_center_crop:
      UpperCAmelCase_				       =					[self.center_crop(image=UpperCamelCase__       ,     size=UpperCamelCase__				) for image in images]

     if do_rescale:
      UpperCAmelCase_				       =					[self.rescale(image=UpperCamelCase__       ,     scale=UpperCamelCase__				) for image in images]

     if do_normalize:
      UpperCAmelCase_				       =					[self.normalize(image=UpperCamelCase__       ,     mean=UpperCamelCase__       ,     std=UpperCamelCase__				) for image in images]

     UpperCAmelCase_				       =					[to_channel_dimension_format(UpperCamelCase__       ,     UpperCamelCase__				) for image in images]

     UpperCAmelCase_				       =					{"pixel_values": images}
     return BatchFeature(data=UpperCamelCase__       ,     tensor_type=UpperCamelCase__				)
