






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     np.array([[1, item, train_mtch[i]] for i, item in enumerate(_A  )]  )
				snake_case__									=     np.array(_A  )
				snake_case__									=     np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()      ,   _A  )  )      ,   x.transpose()  )      ,   _A  )
				return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]  )
def      a_ (      _A      ,   _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     (1, 2, 1)
				snake_case__									=     (1, 1, 0, 7)
				snake_case__									=     SARIMAX(
				    _A      ,   exog=_A      ,   order=_A      ,   seasonal_order=_A  )
				snake_case__									=     model.fit(disp=_A      ,   maxiter=600      ,   method='nm'  )
				snake_case__									=     model_fit.predict(1      ,   len(_A  )      ,   exog=[test_match]  )
				return result[0]
def      a_ (      _A      ,   _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     SVR(kernel='rbf'      ,   C=1      ,   gamma=0.1      ,   epsilon=0.1  )
				regressor.fit(_A      ,   _A  )
				snake_case__									=     regressor.predict(_A  )
				return y_pred[0]
def      a_ (      _A  )			->			float:







				"""simple docstring"""
				train_user.sort()
				snake_case__									=     np.percentile(_A      ,   25  )
				snake_case__									=     np.percentile(_A      ,   75  )
				snake_case__									=     qa - qa
				snake_case__									=     qa - (iqr * 0.1)
				return low_lim
def      a_ (      _A      ,   _A  )			->			bool:







				"""simple docstring"""
				snake_case__									=     0
				snake_case__									=     0
				for i in list_vote:
								if i > actual_result:
												snake_case__									=     not_safe + 1
								else:
												if abs(abs(_A  ) - abs(_A  )  ) <= 0.1:
																safe += 1
												else:
																not_safe += 1
				return safe > not_safe


if __name__ == "__main__":
							# data_input_df = pd.read_csv("ex_data.csv", header=None)
							__UpperCamelCase					:		Dict	  =						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
							__UpperCamelCase					:		Optional[int]	  =						pd.DataFrame(
							    data_input, columns=["""total_user""", """total_even""", """days"""]
							)
							__UpperCamelCase					:		Tuple	  =						Normalizer().fit_transform(data_input_df.values)
							# split data
							__UpperCamelCase					:		Dict	  =						normalize_df[:, 2].tolist()
							__UpperCamelCase					:		Optional[int]	  =						normalize_df[:, 0].tolist()
							__UpperCamelCase					:		Optional[int]	  =						normalize_df[:, 1].tolist()

							# for svr (input variable = total date and total match)
							__UpperCamelCase					:		int	  =						normalize_df[:, [1, 2]].tolist()
							__UpperCamelCase					:		Tuple	  =						x[: len(x) - 1]
							__UpperCamelCase					:		Optional[int]	  =						x[len(x) - 1 :]

							# for linear regression & sarimax
							__UpperCamelCase					:		Optional[int]	  =						total_date[: len(total_date) - 1]
							__UpperCamelCase					:		List[Any]	  =						total_user[: len(total_user) - 1]
							__UpperCamelCase					:		Tuple	  =						total_match[: len(total_match) - 1]

							__UpperCamelCase					:		Any	  =						total_date[len(total_date) - 1 :]
							__UpperCamelCase					:		List[str]	  =						total_user[len(total_user) - 1 :]
							__UpperCamelCase					:		str	  =						total_match[len(total_match) - 1 :]

							# voting system with forecasting
							__UpperCamelCase					:		Dict	  =						[
							    linear_regression_prediction(
							        trn_date, trn_user, trn_match, tst_date, tst_match
							    ),
							    sarimax_predictor(trn_user, trn_match, tst_match),
							    support_vector_regressor(x_train, x_test, trn_user),
							]

							# check the safety of today's data
							__UpperCamelCase					:		str	  =						"""""" if data_safety_checker(res_vote, tst_user) else """not """
							print("""Today's data is {not_str}safe.""")












import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionSAGPipeline
							_UpperCAmelCase     =       TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											torch.manual_seed(0						)
											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       ,	)
											snake_case__									=     DDIMScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	)
											torch.manual_seed(0						)
											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       ,	)
											torch.manual_seed(0						)
											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=10_00       ,	)
											snake_case__									=     CLIPTextModel(UpperCamelCase						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=0						)		->		Union[str, Any]:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': '.',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 1.0,
											    'sag_scale': 1.0,
											    'output_type': 'numpy',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	width=7_68       ,	height=5_12       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'       ,	)

											snake_case__									=     output.images

											assert image.shape == (1, 5_12, 7_68, 3)




