






'''simple docstring'''







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 _lowercase     (    _lowercase		):
							a    				=	["""image_processor""", """tokenizer"""]
							a    				=	"""Pix2StructImageProcessor"""
							a    				=	("""T5Tokenizer""", """T5TokenizerFast""")




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Optional[Any]					):
														lowerCamelCase__ :     List[Any]	  =	False
														super().__init__(UpperCamelCase__      ,							UpperCamelCase__					)




							def __call__(							self:    List[Any]      ,							UpperCamelCase__:    Optional[Any]=None      ,							UpperCamelCase__:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__:    Union[bool, str, TruncationStrategy] = None      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    Optional[int] = 2_048      ,							UpperCamelCase__:    int = 0      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    Optional[bool] = None      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__:    Any      ,							):
														if images is None and text is None:
																					raise ValueError("""You have to specify either images or text."""					)

														# Get only text
														if images is None and not self.image_processor.is_vqa:
																					lowerCamelCase__ :     Dict	  =	self.tokenizer
																					lowerCamelCase__ :     List[Any]	  =	self.tokenizer(
																					    text=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)
																					return text_encoding

														if not self.image_processor.is_vqa:
																					# add pixel_values
																					lowerCamelCase__ :     List[Any]	  =	self.image_processor(
																					    UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							max_patches=UpperCamelCase__      ,							**UpperCamelCase__					)
														else:
																					# add pixel_values and bbox
																					lowerCamelCase__ :     int	  =	self.image_processor(
																					    UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							max_patches=UpperCamelCase__      ,							header_text=UpperCamelCase__      ,							**UpperCamelCase__					)

														if text is not None and not self.image_processor.is_vqa:
																					lowerCamelCase__ :     Dict	  =	self.tokenizer(
																					    text=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

																					if "attention_mask" in text_encoding:
																												lowerCamelCase__ :     str	  =	text_encoding.pop("""attention_mask"""					)
																					if "input_ids" in text_encoding:
																												lowerCamelCase__ :     Optional[Any]	  =	text_encoding.pop("""input_ids"""					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	None

														if text_encoding is not None:
																					encoding_image_processor.update(UpperCamelCase__					)

														return encoding_image_processor




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							*UpperCamelCase__:    Dict      ,							**UpperCamelCase__:    Dict					):
														return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Dict      ,							*UpperCamelCase__:    Union[str, Any]      ,							**UpperCamelCase__:    int					):
														return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.tokenizer.model_input_names
														lowerCamelCase__ :     Optional[int]	  =	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     :      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)

