
import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		Any					 =       []

						SCREAMING_SNAKE_CASE   :		int					 =       os.path.join("tests", "models", "**", "test_modeling_*.py")
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       glob.glob(pattern)
						# TODO: deal with TF/Flax too
						SCREAMING_SNAKE_CASE   :		Any					 =       [
						    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
						]

						for test_file in test_files:
												SCREAMING_SNAKE_CASE   :		Any					 =       get_tester_classes(test_file)
												for tester_class in tester_classes:
																		# A few tester classes don't have `parent` parameter in `__init__`.
																		# TODO: deal this better
																		try:
																								SCREAMING_SNAKE_CASE   :		int					 =       tester_class(parent=None)
																		except Exception:
																								continue
																		if hasattr(tester, "get_config"):
																								SCREAMING_SNAKE_CASE   :		str					 =       tester.get_config()
																								for k, v in config.to_dict().items():
																														if isinstance(v, int):
																																				SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       None
																																				if k in ["vocab_size"]:
																																										SCREAMING_SNAKE_CASE   :		List[str]					 =       100
																																				elif k in ["max_position_embeddings"]:
																																										SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       128
																																				elif k in ["hidden_size", "d_model"]:
																																										SCREAMING_SNAKE_CASE   :		List[Any]					 =       40
																																				elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																										SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       5
																																				if target is not None and v > target:
																																										failures.append(
																																										    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																										    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																										    f''' than {target}.'''
																																										)

						if len(failures) > 0:
												raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))

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