


'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
 import PIL

lowercase							=logging.get_logger(__name__)





def lowerCamelCase__      (						__lowerCamelCase	:							List[str]							):






   if isinstance(_a	,   (list, tuple)							) and isinstance(videos[0]	,   (list, tuple)							) and is_valid_image(videos[0][0]							):
      return videos

   elif isinstance(_a	,   (list, tuple)							) and is_valid_image(videos[0]							):
      return [videos]

   elif is_valid_image(_a							):
      return [[videos]]

   raise ValueError(f"Could not make batched video from {videos}"							)






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=["pixel_values"]






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




          '''simple docstring'''
          super().__init__(**lowerCamelCase_)
          _UpperCAmelCase       :       Tuple							   =size if size is not None else {'shortest_edge': 2_5_6}
          _UpperCAmelCase       :       str							   =get_size_dict(lowerCamelCase_  ,							default_to_square=lowerCamelCase_)
          _UpperCAmelCase       :       Union[str, Any]							   =crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase       :       Optional[Any]							   =get_size_dict(lowerCamelCase_  ,							param_name='crop_size')

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






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = PILImageResampling.BILINEAR  ,							snake_case = None  ,							**snake_case  ,							)						->  np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =get_size_dict(lowerCamelCase_  ,							default_to_square=lowerCamelCase_)
          if "shortest_edge" in size:
             _UpperCAmelCase       :       int							   =get_resize_output_image_size(lowerCamelCase_  ,							size['shortest_edge']  ,							default_to_square=lowerCamelCase_)
          elif "height" in size and "width" in size:
             _UpperCAmelCase       :       Tuple							   =(size['height'], size['width'])
          else:
             raise ValueError(f"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}")
          return resize(lowerCamelCase_  ,							size=lowerCamelCase_  ,							resample=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = None  ,							**snake_case  ,							)						->  np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =get_size_dict(lowerCamelCase_)
          if "height" not in size or "width" not in size:
             raise ValueError(f"Size must have \'height\' and \'width\' as keys. Got {size.keys()}")
          return center_crop(lowerCamelCase_  ,							size=(size['height'], size['width'])  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case = True  ,							snake_case = None  ,							**snake_case  ,							)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =image.astype(np.floataa)
          if offset:
             _UpperCAmelCase       :       Optional[Any]							   =image - (scale / 2)
          return rescale(lowerCamelCase_  ,							scale=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case = None  ,							**snake_case  ,							)						->  np.ndarray:




          '''simple docstring'''
          return normalize(lowerCamelCase_  ,							mean=lowerCamelCase_  ,							std=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							**lowerCamelCase_)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							snake_case = ChannelDimension.FIRST  ,							)						->  np.ndarray:




          '''simple docstring'''
          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.')

          if offset and not do_rescale:
             raise ValueError('For offset, do_rescale must also be set to True.')

          # All transformations expect numpy arrays.
          _UpperCAmelCase       :       Union[str, Any]							   =to_numpy_array(lowerCamelCase_)

          if do_resize:
             _UpperCAmelCase       :       str							   =self.resize(image=lowerCamelCase_  ,							size=lowerCamelCase_  ,							resample=lowerCamelCase_)

          if do_center_crop:
             _UpperCAmelCase       :       Optional[Any]							   =self.center_crop(lowerCamelCase_  ,							size=lowerCamelCase_)

          if do_rescale:
             _UpperCAmelCase       :       Optional[Any]							   =self.rescale(image=lowerCamelCase_  ,							scale=lowerCamelCase_  ,							offset=lowerCamelCase_)

          if do_normalize:
             _UpperCAmelCase       :       List[Any]							   =self.normalize(image=lowerCamelCase_  ,							mean=lowerCamelCase_  ,							std=lowerCamelCase_)

          _UpperCAmelCase       :       Tuple							   =to_channel_dimension_format(lowerCamelCase_  ,							lowerCamelCase_)
          return image






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




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase       :       Tuple							   =resample if resample is not None else self.resample
          _UpperCAmelCase       :       str							   =do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase       :       List[str]							   =do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase       :       Optional[int]							   =rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase       :       str							   =offset if offset is not None else self.offset
          _UpperCAmelCase       :       Union[str, Any]							   =do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase       :       Any							   =image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase       :       Dict							   =image_std if image_std is not None else self.image_std

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

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

          _UpperCAmelCase       :       Optional[Any]							   =make_batched(lowerCamelCase_)

          _UpperCAmelCase       :       Tuple							   =[
              [
                  self._preprocess_image(
                      image=lowerCamelCase_  ,							do_resize=lowerCamelCase_  ,							size=lowerCamelCase_  ,							resample=lowerCamelCase_  ,							do_center_crop=lowerCamelCase_  ,							crop_size=lowerCamelCase_  ,							do_rescale=lowerCamelCase_  ,							rescale_factor=lowerCamelCase_  ,							offset=lowerCamelCase_  ,							do_normalize=lowerCamelCase_  ,							image_mean=lowerCamelCase_  ,							image_std=lowerCamelCase_  ,							data_format=lowerCamelCase_  ,							)
                  for img in video
              ]
              for video in videos
          ]

          _UpperCAmelCase       :       List[str]							   ={'pixel_values': videos}
          return BatchFeature(data=lowerCamelCase_  ,							tensor_type=lowerCamelCase_)





'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase							={
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase							=[
     'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GroupViTModel',
     'GroupViTPreTrainedModel',
     'GroupViTTextModel',
     'GroupViTVisionModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase							=[
     'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFGroupViTModel',
     'TFGroupViTPreTrainedModel',
     'TFGroupViTTextModel',
     'TFGroupViTVisionModel',
 ]

if TYPE_CHECKING:
 from .configuration_groupvit import (
     GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GroupViTConfig,
     GroupViTOnnxConfig,
     GroupViTTextConfig,
     GroupViTVisionConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_groupvit import (
      GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      GroupViTModel,
      GroupViTPreTrainedModel,
      GroupViTTextModel,
      GroupViTVisionModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_groupvit import (
      TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGroupViTModel,
      TFGroupViTPreTrainedModel,
      TFGroupViTTextModel,
      TFGroupViTVisionModel,
  )

else:
 import sys

 lowercase							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

