






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			@property
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  3
									_SCREAMING_SNAKE_CASE     				=  (32, 32)

									_SCREAMING_SNAKE_CASE     				=  floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0					)					).to(UpperCAmelCase_					)
									return image







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  UNetaDConditionModel(
									    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,     cross_attention_dim=32				,     )
									return model







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  AutoencoderKL(
									    block_out_channels=[32, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,     latent_channels=4				,     )
									return model







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  CLIPTextConfig(
									    bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
									return CLIPTextModel(UpperCAmelCase_					)







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									def extract(*UpperCAmelCase_:  Optional[Any]				,     **UpperCAmelCase_:  Optional[Any]					):



															class 				__UpperCAmelCase		:







																		def __init__(     self:  str					):







																								'''simple docstring'''




																								_SCREAMING_SNAKE_CASE     				=  torch.ones([0]					)







																		def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  int					):







																								'''simple docstring'''




																								self.pixel_values.to(UpperCAmelCase_					)
																								return self
															return Out()

									return extract







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.dummy_cond_unet
									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler(
									    beta_start=0.0_00_85				,     beta_end=0.0_12				,     beta_schedule="""scaled_linear"""				,     clip_sample=UpperCAmelCase_				,     set_alpha_to_one=UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  self.dummy_vae
									_SCREAMING_SNAKE_CASE     				=  self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									# make sure here that pndm scheduler skips prk
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline(
									    unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     safety_checker=UpperCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """A painting of a squirrel eating a burger"""

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe([prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""					)
									_SCREAMING_SNAKE_CASE     				=  output.images

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=UpperCAmelCase_				,     )[0]

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.dummy_cond_unet
									_SCREAMING_SNAKE_CASE     				=  PNDMScheduler(skip_prk_steps=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.dummy_vae
									_SCREAMING_SNAKE_CASE     				=  self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									# make sure here that pndm scheduler skips prk
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline(
									    unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     safety_checker=UpperCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """A painting of a squirrel eating a burger"""
									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe([prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""					)

									_SCREAMING_SNAKE_CASE     				=  output.images

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=UpperCAmelCase_				,     )[0]

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,     safety_checker=UpperCAmelCase_					)
									assert isinstance(UpperCAmelCase_				,     UpperCAmelCase_					)
									assert isinstance(pipe.scheduler				,     UpperCAmelCase_					)
									assert pipe.safety_checker is None

									_SCREAMING_SNAKE_CASE     				=  pipe("""example prompt"""				,     num_inference_steps=2					).images[0]
									assert image is not None

									# check that there's no error when saving a pipeline with one of the models being None
									with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained(UpperCAmelCase_					)

									# sanity check that the pipeline still works
									assert pipe.safety_checker is None
									_SCREAMING_SNAKE_CASE     				=  pipe("""example prompt"""				,     num_inference_steps=2					).images[0]
									assert image is not None







			@unittest.skipIf(torch_device != """cuda"""				,     """This test requires a GPU"""					)
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.dummy_cond_unet
									_SCREAMING_SNAKE_CASE     				=  PNDMScheduler(skip_prk_steps=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.dummy_vae
									_SCREAMING_SNAKE_CASE     				=  self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									# put models in fp16
									_SCREAMING_SNAKE_CASE     				=  unet.half()
									_SCREAMING_SNAKE_CASE     				=  vae.half()
									_SCREAMING_SNAKE_CASE     				=  bert.half()

									# make sure here that pndm scheduler skips prk
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline(
									    unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     safety_checker=UpperCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """A painting of a squirrel eating a burger"""
									_SCREAMING_SNAKE_CASE     				=  sd_pipe([prompt]				,     num_inference_steps=2				,     output_type="""np"""					).images

									assert image.shape == (1, 64, 64, 3)



@nightly
@require_torch_gpu
class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  (
									    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
									    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
									    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
									    """ children from bahnhof zoo, detailed """
									)
									_SCREAMING_SNAKE_CASE     				=  4_003_660_346
									_SCREAMING_SNAKE_CASE     				=  7

									# without safety guidance (sld_guidance_scale = 0)
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2

									# without safety guidance (strong configuration)
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2_000				,     sld_warmup_steps=7				,     sld_threshold=0.0_25				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """padme amidala taking a bath artwork, safe for work, no nudity"""
									_SCREAMING_SNAKE_CASE     				=  2_734_971_755
									_SCREAMING_SNAKE_CASE     				=  7

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2_000				,     sld_warmup_steps=7				,     sld_threshold=0.0_25				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  (
									    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
									    """ leyendecker"""
									)
									_SCREAMING_SNAKE_CASE     				=  1_044_355_234
									_SCREAMING_SNAKE_CASE     				=  12

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]					)

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-7

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2_000				,     sld_warmup_steps=7				,     sld_threshold=0.0_25				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]					)
									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2









import copy
import re



class 				__UpperCAmelCase		:
			__snake_case    :				Any									=							"hp"
			__snake_case    :				str									=							{}
			__snake_case    :				List[Any]									=							None







			@classmethod
			def 							UpperCamelCase				(     cls:  Optional[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  prefix
									_SCREAMING_SNAKE_CASE     				=  defaults
									cls.build_naming_info()







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									if len(UpperCAmelCase_					) == 0:
															return ""
									_SCREAMING_SNAKE_CASE     				=  None
									if any(char.isdigit() for char in word					):
															raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number'					)
									if word in info["short_word"]:
															return info["short_word"][word]
									for prefix_len in range(1				,     len(UpperCAmelCase_					) + 1					):
															_SCREAMING_SNAKE_CASE     				=  word[:prefix_len]
															if prefix in info["reverse_short_word"]:
																					continue
															else:
																					_SCREAMING_SNAKE_CASE     				=  prefix
																					break

									if short_word is None:
															# Paranoid fallback
															def int_to_alphabetic(UpperCAmelCase_:  List[Any]					):
																					_SCREAMING_SNAKE_CASE     				=  """"""
																					while integer != 0:
																											_SCREAMING_SNAKE_CASE     				=  chr(ord("""A"""					) + integer % 10					) + s
																											integer //= 10
																					return s

															_SCREAMING_SNAKE_CASE     				=  0
															while True:
																					_SCREAMING_SNAKE_CASE     				=  word + """#""" + int_to_alphabetic(UpperCAmelCase_					)
																					if sword in info["reverse_short_word"]:
																											continue
																					else:
																											_SCREAMING_SNAKE_CASE     				=  sword
																											break

									_SCREAMING_SNAKE_CASE     				=  short_word
									_SCREAMING_SNAKE_CASE     				=  word
									return short_word







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  param_name.split("""_"""					)

									_SCREAMING_SNAKE_CASE     				=  [TrialShortNamer.shortname_for_word(UpperCAmelCase_				,     UpperCAmelCase_					) for word in words]

									# We try to create a separatorless short name, but if there is a collision we have to fallback
									# to a separated short name
									_SCREAMING_SNAKE_CASE     				=  ["""""", """_"""]

									for separator in separators:
															_SCREAMING_SNAKE_CASE     				=  separator.join(UpperCAmelCase_					)
															if shortname not in info["reverse_short_param"]:
																					_SCREAMING_SNAKE_CASE     				=  shortname
																					_SCREAMING_SNAKE_CASE     				=  param_name
																					return shortname

									return param_name







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TrialShortNamer.shortname_for_key(UpperCAmelCase_				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  short_name
									_SCREAMING_SNAKE_CASE     				=  param_name







			@classmethod
			def 							UpperCamelCase				(     cls:  str					):







									'''simple docstring'''




									if cls.NAMING_INFO is not None:
															return

									_SCREAMING_SNAKE_CASE     				=  {
									    """short_word""": {},
									    """reverse_short_word""": {},
									    """short_param""": {},
									    """reverse_short_param""": {},
									}

									_SCREAMING_SNAKE_CASE     				=  list(cls.DEFAULTS.keys()					)

									for k in field_keys:
															cls.add_new_param_name(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  info







			@classmethod
			def 							UpperCamelCase				(     cls:  Any				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									cls.build_naming_info()
									assert cls.PREFIX is not None
									_SCREAMING_SNAKE_CASE     				=  [copy.copy(cls.PREFIX					)]

									for k, v in params.items():
															if k not in cls.DEFAULTS:
																					raise Exception(F'You should provide a default value for the param name {k} with value {v}'					)
															if v == cls.DEFAULTS[k]:
																					# The default value is not added to the name
																					continue

															_SCREAMING_SNAKE_CASE     				=  cls.NAMING_INFO["""short_param"""][k]

															if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  1 if v else 0

															_SCREAMING_SNAKE_CASE     				=  """""" if isinstance(UpperCAmelCase_				,     (int, float)					) else """-"""
															_SCREAMING_SNAKE_CASE     				=  F'{key}{sep}{v}'
															name.append(UpperCAmelCase_					)

									return "_".join(UpperCAmelCase_					)







			@classmethod
			def 							UpperCamelCase				(     cls:  int				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  repr[len(cls.PREFIX					) + 1 :]
									if repr == "":
															_SCREAMING_SNAKE_CASE     				=  []
									else:
															_SCREAMING_SNAKE_CASE     				=  repr.split("""_"""					)

									_SCREAMING_SNAKE_CASE     				=  {}

									for value in values:
															if "-" in value:
																					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  value.split("""-"""					)
															else:
																					_SCREAMING_SNAKE_CASE     				=  re.sub("""[0-9.]"""				,     """"""				,     UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  float(re.sub("""[^0-9.]"""				,     """"""				,     UpperCAmelCase_					)					)

															_SCREAMING_SNAKE_CASE     				=  cls.NAMING_INFO["""reverse_short_param"""][p_k]

															_SCREAMING_SNAKE_CASE     				=  p_v

									for k in cls.DEFAULTS:
															if k not in parameters:
																					_SCREAMING_SNAKE_CASE     				=  cls.DEFAULTS[k]

									return parameters

