


"""simple docstring"""



import unittest

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







class       __a	(   unittest.TestCase       ):







				@cached_property
				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






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







				@cached_property
				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							assert self.tokenizer_ab.add_prefix_space
							assert self.tokenizer_ab([''' Sam''', '''Sam''']      ).input_ids == [[5502, 2], [5502, 2]]







				def     lowerCamelCase_    (   self      ):

							'''simple docstring'''






							assert self.rust_tokenizer_ab.add_prefix_space
							assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']      ).input_ids == [[5502, 2], [5502, 2]]



"""simple docstring"""



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       __a	(   __snake_case       ):
				lowerCamelCase    :					int 					={
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				lowerCamelCase    :					Optional[Any] 					={
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				lowerCamelCase    :					List[str] 					={
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				lowerCamelCase    :					Any 					={
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[List[PIL.Image.Image], np.ndarray]
				lowerCamelCase    :					Optional[List[bool]]
				lowerCamelCase    :					Optional[Union[List[PIL.Image.Image], np.ndarray]]
				lowerCamelCase    :					Optional[str]


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