




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            = logging.get_logger(__name__)


if is_vision_available():
  import PIL







class 		lowercase							(     UpperCamelCase__	):
     _a											=		["pixel_values"]

     def __init__(						self	,  _a = True	,  _a = None	,  _a = PILImageResampling.BICUBIC	,  _a = True	,  _a = None	,  _a = True	,  _a = 1 / 255	,  _a = True	,  _a = None	,  _a = None	,  _a = True	,  **_a	,  )		->							None:
           super().__init__(**_a       )
           _A							:  Dict    =		size if size is not None else {"""shortest_edge""": 224}
           _A							:  List[str]    =		get_size_dict(_a	,  default_to_square=_a       )
           _A							:  Optional[int]    =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           _A							:  Tuple    =		get_size_dict(_a	,  default_to_square=_a	,  param_name="""crop_size"""       )

           _A							:  List[str]    =		do_resize
           _A							:  Optional[Any]    =		size
           _A							:  int    =		resample
           _A							:  List[Any]    =		do_center_crop
           _A							:  Union[str, Any]    =		crop_size
           _A							:  Optional[Any]    =		do_rescale
           _A							:  int    =		rescale_factor
           _A							:  Dict    =		do_normalize
           _A							:  str    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           _A							:  Dict    =		image_std if image_std is not None else OPENAI_CLIP_STD
           _A							:  str    =		do_convert_rgb

     def       a__			(						self	,  _a	,  _a	,  _a = PILImageResampling.BICUBIC	,  _a = None	,  **_a	,  )		->							np.ndarray:
           _A							:  List[str]    =		get_size_dict(_a	,  default_to_square=_a       )
           if "shortest_edge" not in size:
                 raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
           _A							:  List[Any]    =		get_resize_output_image_size(_a	,  size=size["""shortest_edge"""]	,  default_to_square=_a       )
           return resize(_a	,  size=_a	,  resample=_a	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
           _A							:  Union[str, Any]    =		get_size_dict(_a       )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
           return center_crop(_a	,  size=(size["""height"""], size["""width"""])	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							Optional[Any]:
           return rescale(_a	,  scale=_a	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
           return normalize(_a	,  mean=_a	,  std=_a	,  data_format=_a	,  **_a       )



     def       a__			(						self	,  _a	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = ChannelDimension.FIRST	,  **_a	,  )		->							PIL.Image.Image:
           _A							:  Tuple    =		do_resize if do_resize is not None else self.do_resize
           _A							:  List[str]    =		size if size is not None else self.size
           _A							:  int    =		get_size_dict(_a	,  param_name="""size"""	,  default_to_square=_a       )
           _A							:  Tuple    =		resample if resample is not None else self.resample
           _A							:  Optional[Any]    =		do_center_crop if do_center_crop is not None else self.do_center_crop
           _A							:  Optional[int]    =		crop_size if crop_size is not None else self.crop_size
           _A							:  Union[str, Any]    =		get_size_dict(_a	,  param_name="""crop_size"""	,  default_to_square=_a       )
           _A							:  List[Any]    =		do_rescale if do_rescale is not None else self.do_rescale
           _A							:  List[Any]    =		rescale_factor if rescale_factor is not None else self.rescale_factor
           _A							:  Optional[Any]    =		do_normalize if do_normalize is not None else self.do_normalize
           _A							:  Optional[int]    =		image_mean if image_mean is not None else self.image_mean
           _A							:  Tuple    =		image_std if image_std is not None else self.image_std
           _A							:  Tuple    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           _A							:  int    =		make_list_of_images(_a       )

           if not valid_images(_a       ):
                 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:
                 _A							:  Union[str, Any]    =		[convert_to_rgb(_a       ) for image in images]

           # All transformations expect numpy arrays.
           _A							:  Optional[Any]    =		[to_numpy_array(_a       ) for image in images]

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

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

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

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

           _A							:  Union[str, Any]    =		[to_channel_dimension_format(_a	,  _a       ) for image in images]

           _A							:  Optional[Any]    =		{"""pixel_values""": images}
           return BatchFeature(data=_a	,  tensor_type=_a       )







from typing import TYPE_CHECKING

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


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

