






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      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =LxmertTokenizer
	a_         =LxmertTokenizerFast
	a_         =True
	a_         =True

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							super().setUp()

							lowerCAmelCase__       =						[
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							lowerCAmelCase__       =						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 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						"UNwant\u00E9d,running"
							lowerCAmelCase__       =						"unwanted, running"
							return input_text, output_text

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.tokenizer_class(self.vocab_file       )

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


	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							if not self.test_rust_tokenizer:
													return

							lowerCAmelCase__       =						self.get_tokenizer()
							lowerCAmelCase__       =						self.get_rust_tokenizer()

							lowerCAmelCase__       =						"I was born in 92000, and this is falsé."

							lowerCAmelCase__       =						tokenizer.tokenize(__UpperCAmelCase       )
							lowerCAmelCase__       =						rust_tokenizer.tokenize(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						tokenizer.encode(__UpperCAmelCase		,    add_special_tokens=__UpperCAmelCase       )
							lowerCAmelCase__       =						rust_tokenizer.encode(__UpperCAmelCase		,    add_special_tokens=__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						self.get_rust_tokenizer()
							lowerCAmelCase__       =						tokenizer.encode(__UpperCAmelCase       )
							lowerCAmelCase__       =						rust_tokenizer.encode(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	a_							=    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_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

	a_							=    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)






