"""simple docstring"""






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  str				    =      CTRLTokenizer
      snake_case__     :  Optional[Any]				    =      False
      snake_case__     :  List[str]				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            lowercase								=      ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
            lowercase								=      dict(zip(__lowerCAmelCase						,			range(len(__lowerCAmelCase		)		)		)		)
            lowercase								=      ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
            lowercase								=      {"""unk_token""": """<unk>"""}

            lowercase								=      os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]		)
            lowercase								=      os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""merges_file"""]		)
            with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""		) as fp:
                  fp.write(json.dumps(__lowerCAmelCase		) + """\n"""		)
            with open(self.merges_file						,			"""w"""						,			encoding="""utf-8"""		) as fp:
                  fp.write("""\n""".join(__lowerCAmelCase		)		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            kwargs.update(self.special_tokens_map		)
            return CTRLTokenizer.from_pretrained(self.tmpdirname						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      """adapt react readapt apt"""
            lowercase								=      """adapt react readapt apt"""
            return input_text, output_text







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      CTRLTokenizer(self.vocab_file						,			self.merges_file						,			**self.special_tokens_map		)
            lowercase								=      """adapt react readapt apt"""
            lowercase								=      """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
            lowercase								=      tokenizer.tokenize(__lowerCAmelCase		)
            self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

            lowercase								=      tokens + [tokenizer.unk_token]

            lowercase								=      [0, 1, 2, 4, 5, 1, 0, 3, 6]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,			__lowerCAmelCase		)




"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



