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


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)


if is_vision_available():
						import PIL


class      _lowercase						(				lowerCAmelCase_					):
				lowerCamelCase__									=    ['pixel_values']

				def __init__(       self    :      int    ,      _lowercase    :      bool = True    ,      _lowercase    :      Dict[str, int] = None    ,      _lowercase    :      PILImageResampling = PILImageResampling.BICUBIC    ,      _lowercase    :      bool = True    ,      _lowercase    :      Dict[str, int] = None    ,      _lowercase    :      bool = True    ,      _lowercase    :      Union[int, float] = 1 / 2_5_5    ,      _lowercase    :      bool = True    ,      _lowercase    :      Optional[Union[float, List[float]]] = None    ,      _lowercase    :      Optional[Union[float, List[float]]] = None    ,      _lowercase    :      bool = True    ,      **_lowercase    :      Union[str, Any]    ,      ):


										'''simple docstring'''
										super().__init__(**_snake_case       )
										UpperCAmelCase__   		=						size if size is not None else {'shortest_edge': 2_2_4}
										UpperCAmelCase__   		=						get_size_dict(_snake_case    ,      default_to_square=_snake_case       )
										UpperCAmelCase__   		=						crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
										UpperCAmelCase__   		=						get_size_dict(_snake_case       )

										UpperCAmelCase__   		=						do_resize
										UpperCAmelCase__   		=						size
										UpperCAmelCase__   		=						resample
										UpperCAmelCase__   		=						do_center_crop
										UpperCAmelCase__   		=						crop_size
										UpperCAmelCase__   		=						do_rescale
										UpperCAmelCase__   		=						rescale_factor
										UpperCAmelCase__   		=						do_normalize
										UpperCAmelCase__   		=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										UpperCAmelCase__   		=						image_std if image_std is not None else OPENAI_CLIP_STD
										UpperCAmelCase__   		=						do_convert_rgb

				def     A	(       self    :      Dict    ,      _lowercase    :      np.ndarray    ,      _lowercase    :      Dict[str, int]    ,      _lowercase    :      PILImageResampling = PILImageResampling.BICUBIC    ,      _lowercase    :      Optional[Union[str, ChannelDimension]] = None    ,      **_lowercase    :      List[str]    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						get_size_dict(_snake_case    ,      default_to_square=_snake_case       )
										UpperCAmelCase__   		=						get_resize_output_image_size(
										    _snake_case    ,      size=(size["""height"""], size["""width"""])    ,      default_to_square=_snake_case       )
										return resize(_snake_case    ,      size=_snake_case    ,      resample=_snake_case    ,      data_format=_snake_case    ,      **_snake_case       )

				def     A	(       self    :      int    ,      _lowercase    :      np.ndarray    ,      _lowercase    :      Dict[str, int]    ,      _lowercase    :      Optional[Union[str, ChannelDimension]] = None    ,      **_lowercase    :      Tuple    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						get_size_dict(_snake_case       )
										return center_crop(_snake_case    ,      size=(size["""height"""], size["""width"""])    ,      data_format=_snake_case    ,      **_snake_case       )

				def     A	(       self    :      Any    ,      _lowercase    :      np.ndarray    ,      _lowercase    :      Union[int, float]    ,      _lowercase    :      Optional[Union[str, ChannelDimension]] = None    ,      **_lowercase    :      Dict    ,      ):


										'''simple docstring'''
										return rescale(_snake_case    ,      scale=_snake_case    ,      data_format=_snake_case    ,      **_snake_case       )

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      np.ndarray    ,      _lowercase    :      Union[float, List[float]]    ,      _lowercase    :      Union[float, List[float]]    ,      _lowercase    :      Optional[Union[str, ChannelDimension]] = None    ,      **_lowercase    :      Any    ,      ):


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

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      ImageInput    ,      _lowercase    :      bool = None    ,      _lowercase    :      Dict[str, int] = None    ,      _lowercase    :      PILImageResampling = None    ,      _lowercase    :      bool = None    ,      _lowercase    :      int = None    ,      _lowercase    :      bool = None    ,      _lowercase    :      float = None    ,      _lowercase    :      bool = None    ,      _lowercase    :      Optional[Union[float, List[float]]] = None    ,      _lowercase    :      Optional[Union[float, List[float]]] = None    ,      _lowercase    :      bool = None    ,      _lowercase    :      Optional[Union[str, TensorType]] = None    ,      _lowercase    :      Optional[ChannelDimension] = ChannelDimension.FIRST    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__   		=						size if size is not None else self.size
										UpperCAmelCase__   		=						get_size_dict(_snake_case    ,      default_to_square=_snake_case       )
										UpperCAmelCase__   		=						resample if resample is not None else self.resample
										UpperCAmelCase__   		=						do_center_crop if do_center_crop is not None else self.do_center_crop
										UpperCAmelCase__   		=						crop_size if crop_size is not None else self.crop_size
										UpperCAmelCase__   		=						get_size_dict(_snake_case       )
										UpperCAmelCase__   		=						do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__   		=						rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__   		=						do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__   		=						image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__   		=						image_std if image_std is not None else self.image_std
										UpperCAmelCase__   		=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										UpperCAmelCase__   		=						make_list_of_images(_snake_case       )

										if not valid_images(_snake_case       ):
																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:
																UpperCAmelCase__   		=						[convert_to_rgb(_snake_case       ) for image in images]

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

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

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

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

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

										UpperCAmelCase__   		=						[to_channel_dimension_format(_snake_case    ,      _snake_case       ) for image in images]

										UpperCAmelCase__   		=						{'pixel_values': images}
										return BatchFeature(data=_snake_case    ,      tensor_type=_snake_case       )


"""simple docstring"""







import safs


class      _lowercase						(				safs.SaFileSystem					):
				pass

