
'''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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__											=logging.get_logger(__name__)







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			int       =    ['pixel_values']




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

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = PILImageResampling.BILINEAR					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				a_      				=						get_size_dict(UpperCAmelCase	)

				if "shortest_edge" in size:
					a_      				=						get_resize_output_image_size(UpperCAmelCase					,   size=size["""shortest_edge"""]					,   default_to_square=UpperCAmelCase	)
					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
					a_      				=						(size["""height"""], size["""width"""])
				else:
					raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''	)
				return resize(UpperCAmelCase					,   size=UpperCAmelCase					,   resample=UpperCAmelCase					,   data_format=UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				a_      				=						get_size_dict(UpperCAmelCase	)
				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(UpperCAmelCase					,   size=(size["""height"""], size["""width"""])					,   data_format=UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase	):
				return rescale(UpperCAmelCase					,   scale=UpperCAmelCase					,   data_format=UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				return normalize(UpperCAmelCase					,   mean=UpperCAmelCase					,   std=UpperCAmelCase					,   data_format=UpperCAmelCase					,   **UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = ChannelDimension.FIRST					,   **UpperCAmelCase					,   ):
				a_      				=						do_resize if do_resize is not None else self.do_resize
				a_      				=						do_rescale if do_rescale is not None else self.do_rescale
				a_      				=						do_normalize if do_normalize is not None else self.do_normalize
				a_      				=						do_center_crop if do_center_crop is not None else self.do_center_crop
				a_      				=						crop_size if crop_size is not None else self.crop_size
				a_      				=						get_size_dict(UpperCAmelCase					,   param_name="""crop_size"""					,   default_to_square=UpperCAmelCase	)
				a_      				=						resample if resample is not None else self.resample
				a_      				=						rescale_factor if rescale_factor is not None else self.rescale_factor
				a_      				=						image_mean if image_mean is not None else self.image_mean
				a_      				=						image_std if image_std is not None else self.image_std

				a_      				=						size if size is not None else self.size
				a_      				=						get_size_dict(UpperCAmelCase	)

				if not is_batched(UpperCAmelCase	):
					a_      				=						[images]

				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:
					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."""	)

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

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

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

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

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

				a_      				=						[to_channel_dimension_format(UpperCAmelCase					,   UpperCAmelCase	) for image in images]

				a_      				=						{"""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__":
		lowercase__											=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.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=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)
