

import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class   __lowercase    (			__lowercase		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[Any]      							=					CTRLTokenizer
			_UpperCAmelCase       :							Optional[Any]      							=					False
			_UpperCAmelCase       :							List[Any]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										SCREAMING_SNAKE_CASE_:			Tuple  		=	['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	dict(zip(snake_case_       ,						range(len(snake_case_))))
										SCREAMING_SNAKE_CASE_:			Tuple  		=	['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{'''unk_token''': '''<unk>'''}

										SCREAMING_SNAKE_CASE_:			Dict  		=	os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES["vocab_file"])
										SCREAMING_SNAKE_CASE_:			str  		=	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(snake_case_) + "\n")
										with open(self.merges_file       ,						"w"       ,						encoding="utf-8") as fp:
																	fp.write("\n".join(snake_case_))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						**lowerCAmelCase__  :					Tuple):
										kwargs.update(self.special_tokens_map)
										return CTRLTokenizer.from_pretrained(self.tmpdirname       ,						**snake_case_)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Tuple):
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	'''adapt react readapt apt'''
										SCREAMING_SNAKE_CASE_:			Dict  		=	'''adapt react readapt apt'''
										return input_text, output_text




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
										SCREAMING_SNAKE_CASE_:			Any  		=	CTRLTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map)
										SCREAMING_SNAKE_CASE_:			Dict  		=	'''adapt react readapt apt'''
										SCREAMING_SNAKE_CASE_:			int  		=	'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenizer.tokenize(snake_case_)
										self.assertListEqual(snake_case_       ,						snake_case_)

										SCREAMING_SNAKE_CASE_:			Tuple  		=	tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_:			List[Any]  		=	[0, 1, 2, 4, 5, 1, 0, 3, 6]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_)       ,						snake_case_)









import torch

from diffusers import StableDiffusionPipeline


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

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

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






