


'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


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


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		List[str]						=						['pixel_values']




				def __init__(self							,      __lowercase = True							,      __lowercase = None							,      __lowercase = True							,      __lowercase = None							,      __lowercase = PILImageResampling.BICUBIC							,      __lowercase = True							,      __lowercase = 1 / 2_55							,      __lowercase = True							,      __lowercase = None							,      __lowercase = None							,      **__lowercase							,      ):
										super().__init__(**__lowercase		)
										__lowerCAmelCase											=					crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
										__lowerCAmelCase											=					get_size_dict(__lowercase							,      param_name='''crop_size'''		)
										__lowerCAmelCase											=					size if size is not None else {'''height''': 2_24, '''width''': 2_24}
										__lowerCAmelCase											=					get_size_dict(__lowercase		)

										__lowerCAmelCase											=					do_center_crop
										__lowerCAmelCase											=					crop_size
										__lowerCAmelCase											=					do_resize
										__lowerCAmelCase											=					size
										__lowerCAmelCase											=					resample
										__lowerCAmelCase											=					do_rescale
										__lowerCAmelCase											=					rescale_factor
										__lowerCAmelCase											=					do_normalize
										__lowerCAmelCase											=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__lowerCAmelCase											=					image_std if image_std is not None else IMAGENET_DEFAULT_STD




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = None							,      __lowercase = None							,      **__lowercase							,      ):
										__lowerCAmelCase											=					self.size if size is None else size
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										__lowerCAmelCase											=					get_size_dict(__lowercase							,      param_name='''crop_size'''		)

										__lowerCAmelCase							,       __lowerCAmelCase											=					get_image_size(__lowercase		)
										__lowerCAmelCase											=					min(__lowercase							,      __lowercase		)
										__lowerCAmelCase											=					(size['''height'''] / crop_size['''height''']) * min_dim
										__lowerCAmelCase											=					(size['''width'''] / crop_size['''width''']) * min_dim
										return center_crop(__lowercase							,      size=(cropped_height, cropped_width)							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = PIL.Image.BICUBIC							,      __lowercase = None							,      **__lowercase							,      ):
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										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()}"""		)
										return resize(
										    __lowercase							,      size=(size['''height'''], size['''width'''])							,      resample=__lowercase							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = None							,      **__lowercase							,      ):
										return rescale(__lowercase							,      scale=__lowercase							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase = None							,      **__lowercase							,      ):
										return normalize(__lowercase							,      mean=__lowercase							,      std=__lowercase							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(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							,      ):
										__lowerCAmelCase											=					do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase											=					crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase											=					get_size_dict(__lowercase							,      param_name='''crop_size'''		)
										__lowerCAmelCase											=					do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase											=					size if size is not None else self.size
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										__lowerCAmelCase											=					resample if resample is not None else self.resample
										__lowerCAmelCase											=					do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase											=					rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase											=					do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase											=					image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase											=					image_std if image_std is not None else self.image_std

										__lowerCAmelCase											=					make_list_of_images(__lowercase		)

										if not valid_images(__lowercase		):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

										if do_center_crop and crop_size is None:
																raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''		)

										if do_resize and size is None:
																raise ValueError('''Size 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 image standard deviation must be specified if do_normalize is True.'''		)

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

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

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

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

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

										__lowerCAmelCase											=					[to_channel_dimension_format(__lowercase							,      __lowercase		) for image in images]

										__lowerCAmelCase											=					{'''pixel_values''': images}
										return BatchFeature(data=__lowercase							,      tensor_type=__lowercase		)





'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

