"""simple docstring"""
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_STANDARD_MEAN,
    IMAGENET_STANDARD_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__   :      Optional[Any]							=logging.get_logger(__name__)



class      _UpperCAmelCase						( __snake_case						):






    """simple docstring"""
    __snake_case    =       ["""pixel_values"""]







    def __init__(			self    , _lowercase = True    , _lowercase = None    , _lowercase = PIL.Image.BICUBIC    , _lowercase = True    , _lowercase = None    , _lowercase = 1 / 255    , _lowercase = True    , _lowercase = True    , _lowercase = None    , _lowercase = None    , **_lowercase    , )	->	None:
        super().__init__(**a__					)
        _lowerCamelCase	: Optional[Any]       =       size if size is not None else {'''height''': 256, '''width''': 256}
        _lowerCamelCase	: Union[str, Any]       =       get_size_dict(a__					)
        _lowerCamelCase	: Optional[int]       =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _lowerCamelCase	: str       =       get_size_dict(a__    , param_name='''crop_size'''					)

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







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase = PIL.Image.BICUBIC    , _lowercase = None    , **_lowercase    , )	->	np.ndarray:
        _lowerCamelCase	: str       =       get_size_dict(a__					)
        if "height" not in size or "width" not in size:
            raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
        return resize(
            a__    , size=(size['''height'''], size['''width'''])    , resample=a__    , data_format=a__    , **a__					)







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase = None    , **_lowercase    , )	->	np.ndarray:
        _lowerCamelCase	: Union[str, Any]       =       get_size_dict(a__					)
        if "height" not in size or "width" not in size:
            raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
        return center_crop(a__    , size=(size['''height'''], size['''width'''])    , data_format=a__    , **a__					)







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase = None    , **_lowercase    , )	->	Any:
        return rescale(a__    , scale=a__    , data_format=a__    , **a__					)







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







    def 				a__					(			self    , _lowercase    , _lowercase = None    , _lowercase = None    , _lowercase=None    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = None    , _lowercase = ChannelDimension.FIRST    , **_lowercase    , )	->	PIL.Image.Image:
        _lowerCamelCase	: Any       =       do_resize if do_resize is not None else self.do_resize
        _lowerCamelCase	: Tuple       =       resample if resample is not None else self.resample
        _lowerCamelCase	: Tuple       =       do_center_crop if do_center_crop is not None else self.do_center_crop
        _lowerCamelCase	: Any       =       do_rescale if do_rescale is not None else self.do_rescale
        _lowerCamelCase	: Optional[int]       =       rescale_factor if rescale_factor is not None else self.rescale_factor
        _lowerCamelCase	: Optional[int]       =       do_normalize if do_normalize is not None else self.do_normalize
        _lowerCamelCase	: Optional[Any]       =       image_mean if image_mean is not None else self.image_mean
        _lowerCamelCase	: Union[str, Any]       =       image_std if image_std is not None else self.image_std

        _lowerCamelCase	: Optional[int]       =       size if size is not None else self.size
        _lowerCamelCase	: int       =       get_size_dict(a__					)
        _lowerCamelCase	: Union[str, Any]       =       crop_size if crop_size is not None else self.crop_size
        _lowerCamelCase	: Dict       =       get_size_dict(a__    , param_name='''crop_size'''					)

        _lowerCamelCase	: Optional[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 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.
        _lowerCamelCase	: List[str]       =       [to_numpy_array(a__					) for image in images]

        if do_resize:
            _lowerCamelCase	: Optional[Any]       =       [self.resize(image=a__    , size=a__    , resample=a__					) for image in images]

        if do_center_crop:
            _lowerCamelCase	: Optional[Any]       =       [self.center_crop(image=a__    , size=a__					) for image in images]

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

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

        _lowerCamelCase	: Dict       =       [to_channel_dimension_format(a__    , a__					) for image in images]

        _lowerCamelCase	: List[Any]       =       {'''pixel_values''': images}
        return BatchFeature(data=a__    , tensor_type=a__					)






_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



