


'''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


__snake_case				 =	logging.get_logger(__name__)
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['pixel_values']




		def __init__(						self       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = PILImageResampling.BICUBIC       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = 1 / 255       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					size if size is not None else {'''height''': 224, '''width''': 224}
							UpperCamelCase__  :Tuple				   =					get_size_dict(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							UpperCamelCase__  :Optional[int]				   =					get_size_dict(UpperCamelCase_       ,  default_to_square=UpperCamelCase_       ,  param_name='''crop_size'''				)

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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_ = PILImageResampling.BILINEAR       ,  UpperCamelCase_ = None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					get_size_dict(UpperCamelCase_				)

							if "shortest_edge" in size:
												UpperCamelCase__  :Dict				   =					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:
												UpperCamelCase__  :Dict				   =					(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_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					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_				):

							'''simple docstring'''


							return rescale(UpperCamelCase_       ,  scale=UpperCamelCase_       ,  data_format=UpperCamelCase_       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_ = None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							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_       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__  :str				   =					do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__  :Optional[Any]				   =					do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__  :str				   =					do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase__  :str				   =					crop_size if crop_size is not None else self.crop_size
							UpperCamelCase__  :Optional[Any]				   =					get_size_dict(UpperCamelCase_       ,  param_name='''crop_size'''       ,  default_to_square=UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					resample if resample is not None else self.resample
							UpperCamelCase__  :str				   =					rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__  :str				   =					image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__  :Union[str, Any]				   =					image_std if image_std is not None else self.image_std

							UpperCamelCase__  :Any				   =					size if size is not None else self.size
							UpperCamelCase__  :Union[str, Any]				   =					get_size_dict(UpperCamelCase_				)

							if not is_batched(UpperCamelCase_				):
												UpperCamelCase__  :Tuple				   =					[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.
							UpperCamelCase__  :Optional[Any]				   =					[to_numpy_array(UpperCamelCase_				) for image in images]

							if do_resize:
												UpperCamelCase__  :int				   =					[self.resize(image=UpperCamelCase_       ,  size=UpperCamelCase_       ,  resample=UpperCamelCase_				) for image in images]

							if do_center_crop:
												UpperCamelCase__  :List[str]				   =					[self.center_crop(image=UpperCamelCase_       ,  size=UpperCamelCase_				) for image in images]

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

							if do_normalize:
												UpperCamelCase__  :List[Any]				   =					[self.normalize(image=UpperCamelCase_       ,  mean=UpperCamelCase_       ,  std=UpperCamelCase_				) for image in images]

							UpperCamelCase__  :List[Any]				   =					[to_channel_dimension_format(UpperCamelCase_       ,  UpperCamelCase_				) for image in images]

							UpperCamelCase__  :List[Any]				   =					{'''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__":
		__snake_case				 =	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.''',
		)

		__snake_case				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

		__snake_case				 =	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)