




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class     snake_case__							(_A   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					["image_processor", "tokenizer"]
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					"BridgeTowerImageProcessor"
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					("RobertaTokenizer", "RobertaTokenizerFast")





				def __init__(      self				:     int					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     List[str]							)					->     int:
							super().__init__(__lowerCamelCase					,      __lowerCamelCase							)





				def __call__(      self				:     str					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Union[bool, str, PaddingStrategy] = False					,      __lowerCamelCase				:     Union[bool, str, TruncationStrategy] = None					,      __lowerCamelCase				:     Optional[int] = None					,      __lowerCamelCase				:     int = 0					,      __lowerCamelCase				:     Optional[int] = None					,      __lowerCamelCase				:     Optional[bool] = None					,      __lowerCamelCase				:     Optional[bool] = None					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     BatchEncoding:
							a    =			self.tokenizer(
							    text=__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      max_length=__lowerCamelCase					,      stride=__lowerCamelCase					,      pad_to_multiple_of=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase					,      return_attention_mask=__lowerCamelCase					,      return_overflowing_tokens=__lowerCamelCase					,      return_special_tokens_mask=__lowerCamelCase					,      return_offsets_mapping=__lowerCamelCase					,      return_length=__lowerCamelCase					,      verbose=__lowerCamelCase					,      return_tensors=__lowerCamelCase					,      **__lowerCamelCase					,      )
							# add pixel_values + pixel_mask
							a    =			self.image_processor(
							    __lowerCamelCase					,      return_tensors=__lowerCamelCase					,      do_normalize=__lowerCamelCase					,      do_center_crop=__lowerCamelCase					,      **__lowerCamelCase							)
							encoding.update(__lowerCamelCase							)

							return encoding





				def 							__UpperCAmelCase							(      self				:     Tuple					,      *__lowerCamelCase				:     str					,      **__lowerCamelCase				:     Optional[Any]							)					->     List[str]:
							return self.tokenizer.batch_decode(*__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      *__lowerCamelCase				:     Any					,      **__lowerCamelCase				:     Union[str, Any]							)					->     List[str]:
							return self.tokenizer.decode(*__lowerCamelCase					,      **__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Tuple:
							a    =			self.tokenizer.model_input_names
							a    =			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)














"""simple docstring"""



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						__A		:       str  				=		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		:       str  				=		parser.parse_args()

						__A		:       List[Any]  				=		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

						__A		:       List[str]  				=		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)



