





'''simple docstring'''







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


snake_case_     :       Any						=  logging.get_logger(__name__)


class 					__a      (_UpperCamelCase     ):
							__a					:   int									=      ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     Union[int, float] = 1 / 2_55       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 8       ,     **__magic_name__							:     Dict       ,     )					->       Tuple:







													"""simple docstring"""
													super().__init__(**__magic_name__   )

													UpperCAmelCase_			:      str     						=  do_rescale
													UpperCAmelCase_			:      Optional[Any]     						=  rescale_factor
													UpperCAmelCase_			:      Any     						=  do_pad
													UpperCAmelCase_			:      Optional[Any]     						=  pad_size

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[Union[str, ChannelDimension]] = None       ,     **__magic_name__							:     Optional[int]   )					->       Any:







													"""simple docstring"""
													return rescale(__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[Union[str, ChannelDimension]] = None   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  get_image_size(__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  (old_height // size + 1) * size - old_height
													UpperCAmelCase_			:      List[Any]     						=  (old_width // size + 1) * size - old_width

													return pad(__magic_name__       ,     ((0, pad_height), (0, pad_width))       ,     mode='''symmetric'''       ,     data_format=__magic_name__   )



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







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Optional[Any]     						=  rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_			:      str     						=  do_pad if do_pad is not None else self.do_pad
													UpperCAmelCase_			:      Optional[int]     						=  pad_size if pad_size is not None else self.pad_size

													UpperCAmelCase_			:      Tuple     						=  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_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

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

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

													if do_pad:
																			UpperCAmelCase_			:      Tuple     						=  [self.pad(__magic_name__       ,     size=__magic_name__   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

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












'''simple docstring'''







snake_case_     :       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",
}





