


'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCAmelCase					:			Dict				     =					logging.get_logger(__name__)


if is_vision_available():
	import PIL


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Dict						=						['pixel_values']




				def __init__(self							,      __lowercase = True							,      __lowercase = None							,      __lowercase = PILImageResampling.BILINEAR							,      __lowercase = True							,      __lowercase = False							,      __lowercase = True							,      __lowercase = True							,      __lowercase = 1 / 2_55							,      __lowercase = True							,      __lowercase = None							,      __lowercase = None							,      **__lowercase							,      ):
										super().__init__(**__lowercase		)

										__lowerCAmelCase											=					size if size is not None else {'''height''': 25_60, '''width''': 19_20}
										if isinstance(__lowercase							,      (tuple, list)		):
																# The previous feature extractor size parameter was in (width, height) format
																__lowerCAmelCase											=					size[::-1]
										__lowerCAmelCase											=					get_size_dict(__lowercase		)

										__lowerCAmelCase											=					do_resize
										__lowerCAmelCase											=					size
										__lowerCAmelCase											=					resample
										__lowerCAmelCase											=					do_thumbnail
										__lowerCAmelCase											=					do_align_long_axis
										__lowerCAmelCase											=					do_pad
										__lowerCAmelCase											=					do_rescale
										__lowerCAmelCase											=					rescale_factor
										__lowerCAmelCase											=					do_normalize
										__lowerCAmelCase											=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__lowerCAmelCase											=					image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = None		):
										__lowerCAmelCase							,       __lowerCAmelCase											=					get_image_size(__lowercase		)
										__lowerCAmelCase							,       __lowerCAmelCase											=					size['''height'''], size['''width''']

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																__lowerCAmelCase											=					np.rotaa(__lowercase							,      3		)

										if data_format is not None:
																__lowerCAmelCase											=					to_channel_dimension_format(__lowercase							,      __lowercase		)

										return image




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = False							,      __lowercase = None							,      ):
										__lowerCAmelCase							,       __lowerCAmelCase											=					size['''height'''], size['''width''']
										__lowerCAmelCase							,       __lowerCAmelCase											=					get_image_size(__lowercase		)

										__lowerCAmelCase											=					output_width - input_width
										__lowerCAmelCase											=					output_height - input_height

										if random_padding:
																__lowerCAmelCase											=					np.random.randint(low=0							,      high=delta_height + 1		)
																__lowerCAmelCase											=					np.random.randint(low=0							,      high=delta_width + 1		)
										else:
																__lowerCAmelCase											=					delta_height // 2
																__lowerCAmelCase											=					delta_width // 2

										__lowerCAmelCase											=					delta_height - pad_top
										__lowerCAmelCase											=					delta_width - pad_left

										__lowerCAmelCase											=					((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(__lowercase							,      __lowercase							,      data_format=__lowercase		)




				def        _snake_case	(self							,      *__lowercase							,      **__lowercase		):
										logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''		)
										return self.pad_image(*__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = PILImageResampling.BICUBIC							,      __lowercase = None							,      **__lowercase							,      ):
										__lowerCAmelCase							,       __lowerCAmelCase											=					get_image_size(__lowercase		)
										__lowerCAmelCase							,       __lowerCAmelCase											=					size['''height'''], size['''width''']

										# We always resize to the smallest of either the input or output size.
										__lowerCAmelCase											=					min(__lowercase							,      __lowercase		)
										__lowerCAmelCase											=					min(__lowercase							,      __lowercase		)

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																__lowerCAmelCase											=					int(input_width * height / input_height		)
										elif input_width > input_height:
																__lowerCAmelCase											=					int(input_height * width / input_width		)

										return resize(
										    __lowercase							,      size=(height, width)							,      resample=__lowercase							,      reducing_gap=2.0							,      data_format=__lowercase							,      **__lowercase		)




				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase = PILImageResampling.BICUBIC							,      __lowercase = None							,      **__lowercase							,      ):
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										__lowerCAmelCase											=					min(size['''height''']							,      size['''width''']		)
										__lowerCAmelCase											=					get_resize_output_image_size(__lowercase							,      size=__lowercase							,      default_to_square=__lowercase		)
										__lowerCAmelCase											=					resize(__lowercase							,      size=__lowercase							,      resample=__lowercase							,      data_format=__lowercase							,      **__lowercase		)
										return resized_image




				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 = False							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = ChannelDimension.FIRST							,      **__lowercase							,      ):
										__lowerCAmelCase											=					do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase											=					size if size is not None else self.size
										if isinstance(__lowercase							,      (tuple, list)		):
																# Previous feature extractor had size in (width, height) format
																__lowerCAmelCase											=					size[::-1]
										__lowerCAmelCase											=					get_size_dict(__lowercase		)
										__lowerCAmelCase											=					resample if resample is not None else self.resample
										__lowerCAmelCase											=					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										__lowerCAmelCase											=					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										__lowerCAmelCase											=					do_pad if do_pad is not None else self.do_pad
										__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_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_pad and size is None:
																raise ValueError('''Size must be specified if do_pad 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											=					[to_numpy_array(__lowercase		) for image in images]

										if do_align_long_axis:
																__lowerCAmelCase											=					[self.align_long_axis(__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_thumbnail:
																__lowerCAmelCase											=					[self.thumbnail(image=__lowercase							,      size=__lowercase		) for image in images]

										if do_pad:
																__lowerCAmelCase											=					[self.pad_image(image=__lowercase							,      size=__lowercase							,      random_padding=__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

