






"""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 _lowerCAmelCase	(						UpperCamelCase__					):
						SCREAMING_SNAKE_CASE_:				Any   				=							{
						    """sld_warmup_steps""": 15,
						    """sld_guidance_scale""": 20,
						    """sld_threshold""": 0.0,
						    """sld_momentum_scale""": 0.0,
						    """sld_mom_beta""": 0.0,
						}
						SCREAMING_SNAKE_CASE_:				Dict   				=							{
						    """sld_warmup_steps""": 10,
						    """sld_guidance_scale""": 1000,
						    """sld_threshold""": 0.01,
						    """sld_momentum_scale""": 0.3,
						    """sld_mom_beta""": 0.4,
						}
						SCREAMING_SNAKE_CASE_:				Dict   				=							{
						    """sld_warmup_steps""": 7,
						    """sld_guidance_scale""": 2000,
						    """sld_threshold""": 0.025,
						    """sld_momentum_scale""": 0.5,
						    """sld_mom_beta""": 0.7,
						}
						SCREAMING_SNAKE_CASE_:				Union[str, Any]   				=							{
						    """sld_warmup_steps""": 0,
						    """sld_guidance_scale""": 5000,
						    """sld_threshold""": 1.0,
						    """sld_momentum_scale""": 0.5,
						    """sld_mom_beta""": 0.7,
						}







@dataclass
class _lowerCAmelCase	(						UpperCamelCase__					):
						SCREAMING_SNAKE_CASE_:				Union[List[PIL.Image.Image], np.ndarray]
						SCREAMING_SNAKE_CASE_:				Optional[List[bool]]
						SCREAMING_SNAKE_CASE_:				Optional[Union[List[PIL.Image.Image], np.ndarray]]
						SCREAMING_SNAKE_CASE_:				Optional[str]


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














"""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 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, logging


lowerCAmelCase__							= logging.get_logger(__name__)


class _lowerCAmelCase	(						__UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[str]   				=							['pixel_values']







						def __init__(						self      ,							lowerCAmelCase_ = True      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = PILImageResampling.BILINEAR      ,							lowerCAmelCase_ = True      ,							lowerCAmelCase_ = 1 / 2_5_5      ,							lowerCAmelCase_ = True      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							**lowerCAmelCase_      ,							)		->       None:
										super().__init__(**lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		str							=						size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
										_SCREAMING_SNAKE_CASE      :		List[str]							=						get_size_dict(lowerCAmelCase_       )
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						do_resize
										_SCREAMING_SNAKE_CASE      :		List[str]							=						do_rescale
										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						do_normalize
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						size
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						resample
										_SCREAMING_SNAKE_CASE      :		List[str]							=						rescale_factor
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_SCREAMING_SNAKE_CASE      :		Dict							=						image_std if image_std is not None else IMAGENET_STANDARD_STD







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_ = PILImageResampling.BILINEAR      ,							lowerCAmelCase_ = None      ,							**lowerCAmelCase_      ,							)		->       np.ndarray:
										_SCREAMING_SNAKE_CASE      :		List[str]							=						get_size_dict(lowerCAmelCase_       )
										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(
										    lowerCAmelCase_      ,							size=(size['height'], size['width'])      ,							resample=lowerCAmelCase_      ,							data_format=lowerCAmelCase_      ,							**lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_ = None      ,							**lowerCAmelCase_       )		->       np.ndarray:
										return rescale(lowerCAmelCase_      ,							scale=lowerCAmelCase_      ,							data_format=lowerCAmelCase_      ,							**lowerCAmelCase_       )







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







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = None      ,							lowerCAmelCase_ = ChannelDimension.FIRST      ,							**lowerCAmelCase_      ,							)		->       Dict:
										_SCREAMING_SNAKE_CASE      :		List[str]							=						do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE      :		Any							=						do_rescale if do_rescale is not None else self.do_rescale
										_SCREAMING_SNAKE_CASE      :		List[str]							=						do_normalize if do_normalize is not None else self.do_normalize
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE      :		List[str]							=						rescale_factor if rescale_factor is not None else self.rescale_factor
										_SCREAMING_SNAKE_CASE      :		Any							=						image_mean if image_mean is not None else self.image_mean
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						image_std if image_std is not None else self.image_std

										_SCREAMING_SNAKE_CASE      :		Tuple							=						size if size is not None else self.size
										_SCREAMING_SNAKE_CASE      :		List[str]							=						get_size_dict(lowerCAmelCase_       )

										_SCREAMING_SNAKE_CASE      :		Dict							=						make_list_of_images(lowerCAmelCase_       )

										if not valid_images(lowerCAmelCase_       ):
														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.'       )

										# All transformations expect numpy arrays.
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						[to_numpy_array(lowerCAmelCase_       ) for image in images]

										if do_resize:
														_SCREAMING_SNAKE_CASE      :		Dict							=						[self.resize(image=lowerCAmelCase_      ,							size=lowerCAmelCase_      ,							resample=lowerCAmelCase_       ) for image in images]

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

										if do_normalize:
														_SCREAMING_SNAKE_CASE      :		str							=						[self.normalize(image=lowerCAmelCase_      ,							mean=lowerCAmelCase_      ,							std=lowerCAmelCase_       ) for image in images]

										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[to_channel_dimension_format(lowerCAmelCase_      ,							lowerCAmelCase_       ) for image in images]

										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						{'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase_      ,							tensor_type=lowerCAmelCase_       )

