


import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        a					(			_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	LxmertTokenizer
     UpperCAmelCase__ :  Tuple						      =	LxmertTokenizerFast
     UpperCAmelCase__ :  List[Any]						      =	True
     UpperCAmelCase__ :  str						      =	True





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        super().setUp()

        __lowerCamelCase:				Any     =	[
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """want""",
            """##want""",
            """##ed""",
            """wa""",
            """un""",
            """runn""",
            """##ing""",
            """,""",
            """low""",
            """lowest""",
        ]
        __lowerCamelCase:				Tuple     =	os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["""vocab_file"""]							)
        with open(self.vocab_file    ,	"""w"""    ,	encoding="""utf-8"""							) as vocab_writer:
           vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
        __lowerCamelCase:				str     =	"""UNwant\u00E9d,running"""
        __lowerCamelCase:				str     =	"""unwanted, running"""
        return input_text, output_text





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Optional[Any]     =	self.tokenizer_class(self.vocab_file							)

        __lowerCamelCase:				Optional[int]     =	tokenizer.tokenize("""UNwant\u00E9d,running"""							)
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_							)    ,	[7, 4, 5, 10, 8, 9]							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        if not self.test_rust_tokenizer:
           return

        __lowerCamelCase:				str     =	self.get_tokenizer()
        __lowerCamelCase:				Optional[Any]     =	self.get_rust_tokenizer()

        __lowerCamelCase:				Union[str, Any]     =	"""I was born in 92000, and this is falsé."""

        __lowerCamelCase:				List[str]     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	tokenizer.encode(SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	rust_tokenizer.encode(SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_							)
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				str     =	self.get_rust_tokenizer()
        __lowerCamelCase:				Tuple     =	tokenizer.encode(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	rust_tokenizer.encode(SCREAMING_SNAKE_CASE_							)
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)






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		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

   _A		:				Optional[Any]									=       CLIPImageProcessor()
   _A		:				List[Any]									=       CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   _A		:				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)


