
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 					__lowercase		(					unittest.TestCase       ):


					@cached_property
					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''						)


					@cached_property
					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''						)


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  self.tokenizer_ab
									A_       	=  ''' I am a small frog.'''
									A_       	=  tok([src_text]       ,       padding=a__       ,       truncation=a__						)['''input_ids''']
									A_       	=  tok.batch_decode(a__       ,       skip_special_tokens=a__       ,       clean_up_tokenization_spaces=a__						)[0]
									assert src_text == decoded


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.rust_tokenizer_ab
									A_       	=  ''' I am a small frog.'''
									A_       	=  tok([src_text]       ,       padding=a__       ,       truncation=a__						)['''input_ids''']
									A_       	=  tok.batch_decode(a__       ,       skip_special_tokens=a__       ,       clean_up_tokenization_spaces=a__						)[0]
									assert src_text == decoded


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





									'''simple docstring'''
									assert self.tokenizer_ab.add_prefix_space
									assert self.tokenizer_ab([''' Sam''', '''Sam''']						).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]


					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									assert self.rust_tokenizer_ab.add_prefix_space
									assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']						).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker