






"""simple docstring"""



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A		:       Any  				=		logging.get_logger(__name__)





class 					_UpperCAmelCase       :




					def __init__(				self     :						List[Any]    ,						A     :						List[str]    ,						A     :						Tuple   )  ->  List[str]:
							lowercase_ : Union[str, Any]							       =   question_encoder
							lowercase_ : int							       =   generator
							lowercase_ : Any							       =   self.question_encoder




					def   A  (				self     :						Union[str, Any]    ,						A     :						List[Any]   )  ->  Tuple:
							if os.path.isfile(A   ):
									raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
							os.makedirs(A    ,						exist_ok=A   )
							lowercase_ : str							       =   os.path.join(A    ,						'''question_encoder_tokenizer'''   )
							lowercase_ : int							       =   os.path.join(A    ,						'''generator_tokenizer'''   )
							self.question_encoder.save_pretrained(A   )
							self.generator.save_pretrained(A   )




					@classmethod
					def   A  (				cls     :						Union[str, Any]    ,						A     :						Optional[int]    ,						**A     :						Union[str, Any]   )  ->  int:
							# dynamically import AutoTokenizer
							from ..auto.tokenization_auto import AutoTokenizer

							lowercase_ : Tuple							       =   kwargs.pop('''config'''    ,						A   )

							if config is None:
									lowercase_ : int							       =   RagConfig.from_pretrained(A   )

							lowercase_ : Any							       =   AutoTokenizer.from_pretrained(
							    A    ,						config=config.question_encoder    ,						subfolder='''question_encoder_tokenizer'''   )
							lowercase_ : Optional[int]							       =   AutoTokenizer.from_pretrained(
							    A    ,						config=config.generator    ,						subfolder='''generator_tokenizer'''   )
							return cls(question_encoder=A    ,						generator=A   )




					def __call__(				self     :						Optional[int]    ,						*A     :						int    ,						**A     :						Tuple   )  ->  str:
							return self.current_tokenizer(*A    ,						**A   )




					def   A  (				self     :						str    ,						*A     :						Union[str, Any]    ,						**A     :						str   )  ->  Tuple:
							return self.generator.batch_decode(*A    ,						**A   )




					def   A  (				self     :						Union[str, Any]    ,						*A     :						int    ,						**A     :						Dict   )  ->  Union[str, Any]:
							return self.generator.decode(*A    ,						**A   )




					def   A  (				self     :						str   )  ->  List[str]:
							lowercase_ : List[Any]							       =   self.question_encoder




					def   A  (				self     :						Optional[Any]   )  ->  Tuple:
							lowercase_ : Optional[Any]							       =   self.generator






					def   A  (				self     :						Optional[int]    ,						A     :						List[str]    ,						A     :						Optional[List[str]] = None    ,						A     :						Optional[int] = None    ,						A     :						Optional[int] = None    ,						A     :						str = "longest"    ,						A     :						str = None    ,						A     :						bool = True    ,						**A     :						Optional[int]    ,						)  ->  BatchEncoding:
							warnings.warn(
							    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
							    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
							    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
							    '''details'''    ,						A    ,						)
							if max_length is None:
									lowercase_ : str							       =   self.current_tokenizer.model_max_length
							lowercase_ : Tuple							       =   self(
							    A    ,						add_special_tokens=A    ,						return_tensors=A    ,						max_length=A    ,						padding=A    ,						truncation=A    ,						**A    ,						)
							if tgt_texts is None:
									return model_inputs
							# Process tgt_texts
							if max_target_length is None:
									lowercase_ : Dict							       =   self.current_tokenizer.model_max_length
							lowercase_ : Tuple							       =   self(
							    text_target=A    ,						add_special_tokens=A    ,						return_tensors=A    ,						padding=A    ,						max_length=A    ,						truncation=A    ,						**A    ,						)
							lowercase_ : Optional[Any]							       =   labels['''input_ids''']
							return model_inputs











"""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)



