






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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__A          = logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE (       lowercase_    ):





							"""simple docstring"""

							A_       =    ["pixel_values"]

							def __init__(     self:      List[Any]						,   __A:      bool = True						,   __A:      Dict[str, int] = None						,   __A:      bool = True						,   __A:      Dict[str, int] = None						,   __A:      PILImageResampling = PILImageResampling.BICUBIC						,   __A:      bool = True						,   __A:      Union[int, float] = 1 / 2_55						,   __A:      bool = True						,   __A:      Optional[Union[float, List[float]]] = None						,   __A:      Optional[Union[float, List[float]]] = None						,   **__A:      List[str]						,   )			-> int:
												super().__init__(**_lowercase      )
												_A									=       crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
												_A									=       get_size_dict(_lowercase						,   param_name='''crop_size'''      )
												_A									=       size if size is not None else {'''height''': 2_24, '''width''': 2_24}
												_A									=       get_size_dict(_lowercase      )

												_A									=       do_center_crop
												_A									=       crop_size
												_A									=       do_resize
												_A									=       size
												_A									=       resample
												_A									=       do_rescale
												_A									=       rescale_factor
												_A									=       do_normalize
												_A									=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												_A									=       image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def 					__A       (     self:      int						,   __A:      np.ndarray						,   __A:      Dict[str, int]						,   __A:      Optional[int] = None						,   __A:      Optional[Union[str, ChannelDimension]] = None						,   **__A:      Any						,   )			-> Any:
												_A									=       self.size if size is None else size
												_A									=       get_size_dict(_lowercase      )
												_A									=       get_size_dict(_lowercase						,   param_name='''crop_size'''      )

												_A							,_A									=       get_image_size(_lowercase      )
												_A									=       min(_lowercase						,   _lowercase      )
												_A									=       (size['''height'''] / crop_size['''height''']) * min_dim
												_A									=       (size['''width'''] / crop_size['''width''']) * min_dim
												return center_crop(_lowercase						,   size=(cropped_height, cropped_width)						,   data_format=_lowercase						,   **_lowercase      )

							def 					__A       (     self:      int						,   __A:      np.ndarray						,   __A:      Dict[str, int]						,   __A:      PILImageResampling = PIL.Image.BICUBIC						,   __A:      Optional[Union[str, ChannelDimension]] = None						,   **__A:      str						,   )			-> Tuple:
												_A									=       get_size_dict(_lowercase      )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"""      )
												return resize(
												    _lowercase						,   size=(size['''height'''], size['''width'''])						,   resample=_lowercase						,   data_format=_lowercase						,   **_lowercase      )

							def 					__A       (     self:      Optional[Any]						,   __A:      np.ndarray						,   __A:      Union[int, float]						,   __A:      Optional[Union[str, ChannelDimension]] = None						,   **__A:      List[Any]						,   )			-> List[Any]:
												return rescale(_lowercase						,   scale=_lowercase						,   data_format=_lowercase						,   **_lowercase      )

							def 					__A       (     self:      Dict						,   __A:      np.ndarray						,   __A:      Union[float, List[float]]						,   __A:      Union[float, List[float]]						,   __A:      Optional[Union[str, ChannelDimension]] = None						,   **__A:      Union[str, Any]						,   )			-> Optional[Any]:
												return normalize(_lowercase						,   mean=_lowercase						,   std=_lowercase						,   data_format=_lowercase						,   **_lowercase      )



							def 					__A       (     self:      int						,   __A:      ImageInput						,   __A:      Optional[bool] = None						,   __A:      Optional[Dict[str, int]] = None						,   __A:      Optional[bool] = None						,   __A:      Optional[Dict[str, int]] = None						,   __A:      PILImageResampling = None						,   __A:      Optional[bool] = None						,   __A:      Optional[float] = None						,   __A:      Optional[bool] = None						,   __A:      Optional[Union[float, List[float]]] = None						,   __A:      Optional[Union[float, List[float]]] = None						,   __A:      Optional[Union[str, TensorType]] = None						,   __A:      ChannelDimension = ChannelDimension.FIRST						,   **__A:      Optional[Any]						,   )			-> Dict:
												_A									=       do_center_crop if do_center_crop is not None else self.do_center_crop
												_A									=       crop_size if crop_size is not None else self.crop_size
												_A									=       get_size_dict(_lowercase						,   param_name='''crop_size'''      )
												_A									=       do_resize if do_resize is not None else self.do_resize
												_A									=       size if size is not None else self.size
												_A									=       get_size_dict(_lowercase      )
												_A									=       resample if resample is not None else self.resample
												_A									=       do_rescale if do_rescale is not None else self.do_rescale
												_A									=       rescale_factor if rescale_factor is not None else self.rescale_factor
												_A									=       do_normalize if do_normalize is not None else self.do_normalize
												_A									=       image_mean if image_mean is not None else self.image_mean
												_A									=       image_std if image_std is not None else self.image_std

												_A									=       make_list_of_images(_lowercase      )

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

												if do_center_crop and crop_size is None:
																	raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''      )

												if do_resize and size is None:
																	raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''      )

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

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

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

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

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

												_A									=       [to_channel_dimension_format(_lowercase						,   _lowercase      ) for image in images]

												_A									=       {'''pixel_values''': images}
												return BatchFeature(data=_lowercase						,   tensor_type=_lowercase      )










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case				= {"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RegNetForImageClassification""",
      """RegNetModel""",
      """RegNetPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRegNetForImageClassification""",
      """TFRegNetModel""",
      """TFRegNetPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """FlaxRegNetForImageClassification""",
      """FlaxRegNetModel""",
      """FlaxRegNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






