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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCamelCase__     :	List[str]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				Dict    ):
   '''simple docstring'''
   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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   ['''pixel_values''']




      def __init__(				self					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = PILImageResampling.BILINEAR					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = True					,lowerCamelCase_ = 1 / 255					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			None:



         '''simple docstring'''



         super().__init__(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase__							:				Tuple   				=  get_size_dict(lowerCamelCase_					,default_to_square=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase__							:				str   				=  get_size_dict(lowerCamelCase_					,param_name='''crop_size'''							)

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  get_size_dict(lowerCamelCase_					,default_to_square=lowerCamelCase_							)
         if "shortest_edge" in size:
            UpperCAmelCase__							:				Any   				=  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					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			np.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  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					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			List[Any]:



         '''simple docstring'''



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




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = 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.'''							)

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

         if do_resize:
            UpperCAmelCase__							:				Union[str, Any]   				=  self.resize(image=lowerCamelCase_					,size=lowerCamelCase_					,resample=lowerCamelCase_							)

         if do_center_crop:
            UpperCAmelCase__							:				List[Any]   				=  self.center_crop(lowerCamelCase_					,size=lowerCamelCase_							)

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

         if do_normalize:
            UpperCAmelCase__							:				Any   				=  self.normalize(image=lowerCamelCase_					,mean=lowerCamelCase_					,std=lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  to_channel_dimension_format(lowerCamelCase_					,lowerCamelCase_							)
         return image




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



         '''simple docstring'''



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

         UpperCAmelCase__							:				Union[str, Any]   				=  size if size is not None else self.size
         UpperCAmelCase__							:				Dict   				=  get_size_dict(lowerCamelCase_					,default_to_square=lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__							:				Optional[Any]   				=  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__							:				Union[str, Any]   				=  make_batched(lowerCamelCase_							)

         UpperCAmelCase__							:				int   				=  [
             [
                 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_					,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_torch_available, is_vision_available


UpperCamelCase__     :	List[Any]				=			{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__     :	int				=			['YolosFeatureExtractor']
  UpperCamelCase__     :	int				=			['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__     :	int				=			[
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__     :	int				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




