

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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    ['''pixel_values''']




				def __init__(      self   ,     snake_case = True   ,     snake_case = None   ,     snake_case = PILImageResampling.BICUBIC   ,     snake_case = True   ,     snake_case = None   ,     snake_case = True   ,     snake_case = 1 / 255   ,     snake_case = True   ,     snake_case = None   ,     snake_case = None   ,     snake_case = True   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					size if size is not None else {'shortest_edge': 224}
											snake_case_           =					get_size_dict(snake_case   ,     default_to_square=snake_case )
											snake_case_           =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
											snake_case_           =					get_size_dict(snake_case   ,     default_to_square=snake_case   ,     param_name='crop_size' )

											snake_case_           =					do_resize
											snake_case_           =					size
											snake_case_           =					resample
											snake_case_           =					do_center_crop
											snake_case_           =					crop_size
											snake_case_           =					do_rescale
											snake_case_           =					rescale_factor
											snake_case_           =					do_normalize
											snake_case_           =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											snake_case_           =					image_std if image_std is not None else OPENAI_CLIP_STD
											snake_case_           =					do_convert_rgb




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case = PILImageResampling.BICUBIC   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					get_size_dict(snake_case   ,     default_to_square=snake_case )
											if "shortest_edge" not in size:
																		raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
											snake_case_           =					get_resize_output_image_size(snake_case   ,     size=size['shortest_edge']   ,     default_to_square=snake_case )
											return resize(snake_case   ,     size=snake_case   ,     resample=snake_case   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					get_size_dict(snake_case )
											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(snake_case   ,     size=(size['height'], size['width'])   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											return rescale(snake_case   ,     scale=snake_case   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     **snake_case   ,     ):
											return normalize(snake_case   ,     mean=snake_case   ,     std=snake_case   ,     data_format=snake_case   ,     **snake_case )




				def 					a							(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = ChannelDimension.FIRST   ,     **snake_case   ,     ):
											snake_case_           =					do_resize if do_resize is not None else self.do_resize
											snake_case_           =					size if size is not None else self.size
											snake_case_           =					get_size_dict(snake_case   ,     param_name='size'   ,     default_to_square=snake_case )
											snake_case_           =					resample if resample is not None else self.resample
											snake_case_           =					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_           =					crop_size if crop_size is not None else self.crop_size
											snake_case_           =					get_size_dict(snake_case   ,     param_name='crop_size'   ,     default_to_square=snake_case )
											snake_case_           =					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_           =					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_           =					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_           =					image_mean if image_mean is not None else self.image_mean
											snake_case_           =					image_std if image_std is not None else self.image_std
											snake_case_           =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											snake_case_           =					make_list_of_images(snake_case )

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

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

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

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

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

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

											snake_case_           =					[to_channel_dimension_format(snake_case   ,     snake_case ) for image in images]

											snake_case_           =					{'pixel_values': images}
											return BatchFeature(data=snake_case   ,     tensor_type=snake_case )







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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




