

"""simple docstring"""




from typing import TYPE_CHECKING

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


__UpperCAmelCase			=					{
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			=					[
          'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SwiftFormerForImageClassification',
          'SwiftFormerModel',
          'SwiftFormerPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_swiftformer import (
          SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SwiftFormerConfig,
          SwiftFormerOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_swiftformer import (
                SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                SwiftFormerForImageClassification,
                SwiftFormerModel,
                SwiftFormerPreTrainedModel,
            )

else:
      import sys

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









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 convert_to_rgb, 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


if is_vision_available():
		import PIL


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Dict       			=  ["""pixel_values"""]






		def __init__(						self				:     Tuple   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Union[int, float] = 1 / 2_55   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Any   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						size if size is not None else {"height": 3_84, "width": 3_84}
				SCREAMING_SNAKE_CASE_				:str				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

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






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Any   ,  ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)
				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()}")
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(size["height"], size["width"])
				return resize(UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






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






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Union[float, List[float]]   ,  UpperCAmelCase				:     Union[float, List[float]]   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Optional[Any]   ,  ):
				return normalize(UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Dict[str, int]] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[float] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     ChannelDimension = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Dict   ,  ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image_mean if image_mean is not None else self.image_mean
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image_std if image_std is not None else self.image_std
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Tuple				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						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 or resample is None:
						raise ValueError("Size and resample 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 std must be specified if do_normalize is True.")

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[convert_to_rgb(UpperCAmelCase) for image in images]

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

				if do_resize:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						[self.resize(image=UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase) for image in images]

				if do_rescale:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.rescale(image=UpperCAmelCase   ,  scale=UpperCAmelCase) for image in images]

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.normalize(image=UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						BatchFeature(data={"pixel_values": images}   ,  tensor_type=UpperCAmelCase)

				return encoded_outputs






