


'''simple docstring'''
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      lowerCamelCase_  (      __a	,			unittest.TestCase ):
      lowerCAmelCase__							=	OpenAIGPTTokenizer
      lowerCAmelCase__							=	OpenAIGPTTokenizerFast
      lowerCAmelCase__							=	True
      lowerCAmelCase__							=	False






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase__     :						str						=							[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''w</w>''',
              '''r</w>''',
              '''t</w>''',
              '''lo''',
              '''low''',
              '''er</w>''',
              '''low</w>''',
              '''lowest</w>''',
              '''newer</w>''',
              '''wider</w>''',
              '''<unk>''',
          ]
          UpperCAmelCase__     :						int						=							dict(zip(_A		,		range(len(_A					)					)					)					)
          UpperCAmelCase__     :						str						=							['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

          UpperCAmelCase__     :						Optional[Any]						=							os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['''vocab_file''']					)
          UpperCAmelCase__     :						Optional[int]						=							os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES['''merges_file''']					)
          with open(self.vocab_file		,		'''w'''					) as fp:
              fp.write(json.dumps(_A					)					)
          with open(self.merges_file		,		'''w'''					) as fp:
              fp.write('''\n'''.join(_A					)					)






      def lowercase_				(   self							:       int		,		_A							:       List[Any]					):


          '''simple docstring'''


          return "lower newer", "lower newer"






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							OpenAIGPTTokenizer(self.vocab_file		,		self.merges_file					)

          UpperCAmelCase__     :						Any						=							'''lower'''
          UpperCAmelCase__     :						List[Any]						=							['''low''', '''er</w>''']
          UpperCAmelCase__     :						List[str]						=							tokenizer.tokenize(_A					)
          self.assertListEqual(_A		,		_A					)

          UpperCAmelCase__     :						int						=							tokens + ['''<unk>''']
          UpperCAmelCase__     :						int						=							[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_A					)		,		_A					)






      def lowercase_				(   self							:       List[Any]		,		_A							:       List[str]=15					):


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                  UpperCAmelCase__     :						Dict						=							self.rust_tokenizer_class.from_pretrained(_A		,		**_A					)

                  # Simple input
                  UpperCAmelCase__     :						Any						=							'''This is a simple input'''
                  UpperCAmelCase__     :						Tuple						=							['''This is a simple input 1''', '''This is a simple input 2''']
                  UpperCAmelCase__     :						List[Any]						=							('''This is a simple input''', '''This is a pair''')
                  UpperCAmelCase__     :						Union[str, Any]						=							[
                      ('''This is a simple input 1''', '''This is a simple input 2'''),
                      ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                  ]

                  # Simple input tests
                  self.assertRaises(_A		,		tokenizer_r.encode		,		_A		,		max_length=_A		,		padding='''max_length'''					)

                  # Simple input
                  self.assertRaises(_A		,		tokenizer_r.encode_plus		,		_A		,		max_length=_A		,		padding='''max_length'''					)

                  # Simple input
                  self.assertRaises(
                      _A		,		tokenizer_r.batch_encode_plus		,		_A		,		max_length=_A		,		padding='''max_length'''		,		)

                  # Pair input
                  self.assertRaises(_A		,		tokenizer_r.encode		,		_A		,		max_length=_A		,		padding='''max_length'''					)

                  # Pair input
                  self.assertRaises(_A		,		tokenizer_r.encode_plus		,		_A		,		max_length=_A		,		padding='''max_length'''					)

                  # Pair input
                  self.assertRaises(
                      _A		,		tokenizer_r.batch_encode_plus		,		_A		,		max_length=_A		,		padding='''max_length'''		,		)






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          pass




@require_ftfy
@require_spacy
@require_tokenizers
class      lowerCamelCase_  (      __a ):
      pass







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       :   List[Any]					= parser.parse_args()

					A       :   int					= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					A       :   Tuple					= CLIPImageProcessor()
					A       :   int					= CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

					A       :   Tuple					= 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)



