




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					:       List[str]    					=      logging.get_logger(__name__)


if is_vision_available():
		import PIL

class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  ['''pixel_values''']
							def __init__(						self						:Any		,      __magic_name__						:bool = True		,      __magic_name__						:Dict[str, int] = None		,      __magic_name__						:PILImageResampling = PILImageResampling.BICUBIC		,      __magic_name__						:bool = True		,      __magic_name__						:Dict[str, int] = None		,      __magic_name__						:bool = True		,      __magic_name__						:Union[int, float] = 1 / 255		,      __magic_name__						:bool = True		,      __magic_name__						:Optional[Union[float, List[float]]] = None		,      __magic_name__						:Optional[Union[float, List[float]]] = None		,      __magic_name__						:bool = True		,      **__magic_name__						:Any		,      ):







								'''simple docstring'''







								super().__init__(**__magic_name__				)
								a										=							size if size is not None else {"""shortest_edge""": 224}
								a										=							get_size_dict(__magic_name__		,      default_to_square=__magic_name__				)
								a										=							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								a										=							get_size_dict(__magic_name__		,      default_to_square=__magic_name__		,      param_name="""crop_size"""				)

								a										=							do_resize
								a										=							size
								a										=							resample
								a										=							do_center_crop
								a										=							crop_size
								a										=							do_rescale
								a										=							rescale_factor
								a										=							do_normalize
								a										=							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								a										=							image_std if image_std is not None else OPENAI_CLIP_STD
								a										=							do_convert_rgb
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:np.ndarray		,      __magic_name__						:Dict[str, int]		,      __magic_name__						:PILImageResampling = PILImageResampling.BICUBIC		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:Optional[Any]		,      ):







								'''simple docstring'''







								a										=							get_size_dict(__magic_name__		,      default_to_square=__magic_name__				)
								if "shortest_edge" not in size:
									raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'				)
								a										=							get_resize_output_image_size(__magic_name__		,      size=size["""shortest_edge"""]		,      default_to_square=__magic_name__				)
								return resize(__magic_name__		,      size=__magic_name__		,      resample=__magic_name__		,      data_format=__magic_name__		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:np.ndarray		,      __magic_name__						:Dict[str, int]		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:Union[str, Any]		,      ):







								'''simple docstring'''







								a										=							get_size_dict(__magic_name__				)
								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(__magic_name__		,      size=(size["""height"""], size["""width"""])		,      data_format=__magic_name__		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:np.ndarray		,      __magic_name__						:Union[int, float]		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:Optional[int]		,      ):







								'''simple docstring'''







								return rescale(__magic_name__		,      scale=__magic_name__		,      data_format=__magic_name__		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:np.ndarray		,      __magic_name__						:Union[float, List[float]]		,      __magic_name__						:Union[float, List[float]]		,      __magic_name__						:Optional[Union[str, ChannelDimension]] = None		,      **__magic_name__						:List[str]		,      ):







								'''simple docstring'''







								return normalize(__magic_name__		,      mean=__magic_name__		,      std=__magic_name__		,      data_format=__magic_name__		,      **__magic_name__				)





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







								'''simple docstring'''







								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(__magic_name__		,      param_name="""size"""		,      default_to_square=__magic_name__				)
								a										=							resample if resample is not None else self.resample
								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(__magic_name__		,      param_name="""crop_size"""		,      default_to_square=__magic_name__				)
								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										=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								a										=							make_list_of_images(__magic_name__				)

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

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

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

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

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

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

								a										=							[to_channel_dimension_format(__magic_name__		,      __magic_name__				) for image in images]

								a										=							{"""pixel_values""": images}
								return BatchFeature(data=__magic_name__		,      tensor_type=__magic_name__				)







__UpperCamelCase					:       Optional[int]    					=      {
    "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",
}

