






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


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

class _lowercase     (    _lowercase		):
							a    				=	["""pixel_values"""]




							def __init__(							self:    Any      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    int = 0.9      ,							UpperCamelCase__:    PILImageResampling = PILImageResampling.BICUBIC      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    Union[int, float] = 1 / 255      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__:    List[str]      ,							):
														super().__init__(**UpperCamelCase__					)
														lowerCamelCase__ :     Any	  =	size if size is not None else {"""shortest_edge""": 224}
														lowerCamelCase__ :     int	  =	get_size_dict(UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
														lowerCamelCase__ :     Any	  =	get_size_dict(UpperCamelCase__      ,							param_name="""crop_size"""					)

														lowerCamelCase__ :     str	  =	do_resize
														lowerCamelCase__ :     List[Any]	  =	size
														lowerCamelCase__ :     List[Any]	  =	crop_pct
														lowerCamelCase__ :     Tuple	  =	resample
														lowerCamelCase__ :     Any	  =	do_center_crop
														lowerCamelCase__ :     Tuple	  =	crop_size
														lowerCamelCase__ :     str	  =	do_rescale
														lowerCamelCase__ :     Any	  =	rescale_factor
														lowerCamelCase__ :     Union[str, Any]	  =	do_normalize
														lowerCamelCase__ :     Optional[Any]	  =	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														lowerCamelCase__ :     Dict	  =	image_std if image_std is not None else IMAGENET_DEFAULT_STD




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Dict[str, int]      ,							UpperCamelCase__:    Optional[float] = None      ,							UpperCamelCase__:    PILImageResampling = PILImageResampling.BICUBIC      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Optional[int]      ,							):
														lowerCamelCase__ :     Optional[Any]	  =	get_size_dict(UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																					raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
														if crop_pct is not None:
																					if "shortest_edge" in size:
																												lowerCamelCase__ :     List[Any]	  =	int(size["""shortest_edge"""] / crop_pct					)
																					elif "height" in size and "width" in size:
																												if size["height"] == size["width"]:
																																			lowerCamelCase__ :     Tuple	  =	int(size["""height"""] / crop_pct					)
																												else:
																																			lowerCamelCase__ :     List[str]	  =	(int(size["""height"""] / crop_pct					), int(size["""width"""] / crop_pct					))
																					else:
																												raise ValueError("""Invalid size for resize: {}""".format(UpperCamelCase__					)					)

																					lowerCamelCase__ :     Optional[int]	  =	get_resize_output_image_size(UpperCamelCase__      ,							size=UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														else:
																					if "shortest_edge" in size:
																												lowerCamelCase__ :     List[str]	  =	get_resize_output_image_size(UpperCamelCase__      ,							size=size["""shortest_edge"""]      ,							default_to_square=UpperCamelCase__					)
																					elif "height" in size and "width" in size:
																												lowerCamelCase__ :     Optional[Any]	  =	(size["""height"""], size["""width"""])
																					else:
																												raise ValueError("""Invalid size for resize: {}""".format(UpperCamelCase__					)					)

														return resize(UpperCamelCase__      ,							size=UpperCamelCase__      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Dict[str, int]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Tuple      ,							):
														lowerCamelCase__ :     int	  =	get_size_dict(UpperCamelCase__					)
														if "height" not in size or "width" not in size:
																					raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''					)
														return center_crop(UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Union[int, float]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Dict      ,							):
														return rescale(UpperCamelCase__      ,							scale=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    np.ndarray      ,							UpperCamelCase__:    Union[float, List[float]]      ,							UpperCamelCase__:    Union[float, List[float]]      ,							UpperCamelCase__:    Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__:    Optional[Any]      ,							):
														return normalize(UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    ImageInput      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    int = None      ,							UpperCamelCase__:    PILImageResampling = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Dict[str, int] = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    float = None      ,							UpperCamelCase__:    bool = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__:    ChannelDimension = ChannelDimension.FIRST      ,							**UpperCamelCase__:    Tuple      ,							):
														lowerCamelCase__ :     int	  =	do_resize if do_resize is not None else self.do_resize
														lowerCamelCase__ :     List[str]	  =	crop_pct if crop_pct is not None else self.crop_pct
														lowerCamelCase__ :     Dict	  =	resample if resample is not None else self.resample
														lowerCamelCase__ :     int	  =	do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCamelCase__ :     List[Any]	  =	do_rescale if do_rescale is not None else self.do_rescale
														lowerCamelCase__ :     Any	  =	rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCamelCase__ :     List[str]	  =	do_normalize if do_normalize is not None else self.do_normalize
														lowerCamelCase__ :     str	  =	image_mean if image_mean is not None else self.image_mean
														lowerCamelCase__ :     Dict	  =	image_std if image_std is not None else self.image_std

														lowerCamelCase__ :     Union[str, Any]	  =	size if size is not None else self.size
														lowerCamelCase__ :     Any	  =	get_size_dict(UpperCamelCase__      ,							default_to_square=UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	crop_size if crop_size is not None else self.crop_size
														lowerCamelCase__ :     Union[str, Any]	  =	get_size_dict(UpperCamelCase__      ,							param_name="""crop_size"""					)

														lowerCamelCase__ :     Dict	  =	make_list_of_images(UpperCamelCase__					)

														if not valid_images(UpperCamelCase__					):
																					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 or resample is None:
																					raise ValueError("""Size and resample must be specified if do_resize is True."""					)

														if do_center_crop and crop_pct is None:
																					raise ValueError("""Crop_pct 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."""					)

														# All transformations expect numpy arrays.
														lowerCamelCase__ :     List[Any]	  =	[to_numpy_array(UpperCamelCase__					) for image in images]

														if do_resize:
																					lowerCamelCase__ :     List[str]	  =	[self.resize(image=UpperCamelCase__      ,							size=UpperCamelCase__      ,							crop_pct=UpperCamelCase__      ,							resample=UpperCamelCase__					) for image in images]

														if do_center_crop:
																					lowerCamelCase__ :     Any	  =	[self.center_crop(image=UpperCamelCase__      ,							size=UpperCamelCase__					) for image in images]

														if do_rescale:
																					lowerCamelCase__ :     int	  =	[self.rescale(image=UpperCamelCase__      ,							scale=UpperCamelCase__					) for image in images]

														if do_normalize:
																					lowerCamelCase__ :     Optional[int]	  =	[self.normalize(image=UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__					) for image in images]

														lowerCamelCase__ :     Tuple	  =	[to_channel_dimension_format(UpperCamelCase__      ,							UpperCamelCase__					) for image in images]

														lowerCamelCase__ :     Tuple	  =	{"""pixel_values""": images}
														return BatchFeature(data=UpperCamelCase__      ,							tensor_type=UpperCamelCase__					)









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=argparse.ArgumentParser()

			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

			parser.add_argument(
			    '''--txt2img_unclip''',
			    default='''kakaobrain/karlo-v1-alpha''',
			    type=str,
			    required=False,
			    help='''The pretrained txt2img unclip.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=UnCLIPImageVariationPipeline(
			    decoder=txtaimg.decoder,
			    text_encoder=txtaimg.text_encoder,
			    tokenizer=txtaimg.tokenizer,
			    text_proj=txtaimg.text_proj,
			    feature_extractor=feature_extractor,
			    image_encoder=image_encoder,
			    super_res_first=txtaimg.super_res_first,
			    super_res_last=txtaimg.super_res_last,
			    decoder_scheduler=txtaimg.decoder_scheduler,
			    super_res_scheduler=txtaimg.super_res_scheduler,
			)

			imgaimg.save_pretrained(args.dump_path)

