


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 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= np.array([[1, item, train_mtch[i]] for i, item in enumerate(_a	)]	)
		lowerCAmelCase__       :      int   					= np.array(_a	)
		lowerCAmelCase__       :      Tuple   					= 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 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= (1, 2, 1)
		lowerCAmelCase__       :      Optional[Any]   					= (1, 1, 0, 7)
		lowerCAmelCase__       :      Dict   					= SARIMAX(
		    _a     ,       exog=_a     ,       order=_a     ,       seasonal_order=_a	)
		lowerCAmelCase__       :      int   					= model.fit(disp=_a     ,       maxiter=600     ,       method='''nm'''	)
		lowerCAmelCase__       :      Optional[Any]   					= model_fit.predict(1     ,       len(_a	)     ,       exog=[test_match]	)
		return result[0]






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= SVR(kernel='''rbf'''     ,       C=1     ,       gamma=0.1     ,       epsilon=0.1	)
		regressor.fit(_a     ,       _a	)
		lowerCAmelCase__       :      Dict   					= regressor.predict(_a	)
		return y_pred[0]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		train_user.sort()
		lowerCAmelCase__       :      List[str]   					= np.percentile(_a     ,       25	)
		lowerCAmelCase__       :      str   					= np.percentile(_a     ,       75	)
		lowerCAmelCase__       :      Dict   					= qa - qa
		lowerCAmelCase__       :      List[str]   					= qa - (iqr * 0.1)
		return low_lim






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[int]   					= 0
		lowerCAmelCase__       :      List[Any]   					= 0
		for i in list_vote:
				if i > actual_result:
						lowerCAmelCase__       :      Any   					= 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)
		lowerCamelCase						=		[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
		lowerCamelCase						=		pd.DataFrame(
		    data_input, columns=['''total_user''', '''total_even''', '''days''']
		)
		lowerCamelCase						=		Normalizer().fit_transform(data_input_df.values)
		# split data
		lowerCamelCase						=		normalize_df[:, 2].tolist()
		lowerCamelCase						=		normalize_df[:, 0].tolist()
		lowerCamelCase						=		normalize_df[:, 1].tolist()

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

		# for linear regression & sarimax
		lowerCamelCase						=		total_date[: len(total_date) - 1]
		lowerCamelCase						=		total_user[: len(total_user) - 1]
		lowerCamelCase						=		total_match[: len(total_match) - 1]

		lowerCamelCase						=		total_date[len(total_date) - 1 :]
		lowerCamelCase						=		total_user[len(total_user) - 1 :]
		lowerCamelCase						=		total_match[len(total_match) - 1 :]

		# voting system with forecasting
		lowerCamelCase						=		[
		    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
		lowerCamelCase						=		'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
		print('''Today\'s data is {not_str}safe.''')








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase						=		False





class  _a							(    unittest.TestCase):
	pass





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




	def 							UpperCAmelCase__( self						:		int		)->	int:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 							UpperCAmelCase__( self						:		Any		)->	Dict:
			lowerCAmelCase__       :      int   					= VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		, torch_dtype=torch.floataa		)
			pipe.to(_SCREAMING_SNAKE_CASE		)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[str]   					= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

			lowerCAmelCase__       :      List[str]   					= torch.manual_seed(0		)
			lowerCAmelCase__       :      List[str]   					= pipe.dual_guided(
			    prompt='''first prompt'''		, image=_SCREAMING_SNAKE_CASE		, text_to_image_strength=0.75		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=2		, output_type='''numpy'''		, ).images

			with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
					lowerCAmelCase__       :      List[Any]   					= VersatileDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		, torch_dtype=torch.floataa		)
			pipe.to(_SCREAMING_SNAKE_CASE		)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[str]   					= generator.manual_seed(0		)
			lowerCAmelCase__       :      str   					= pipe.dual_guided(
			    prompt='''first prompt'''		, image=_SCREAMING_SNAKE_CASE		, text_to_image_strength=0.75		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=2		, output_type='''numpy'''		, ).images

			assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"




	def 							UpperCAmelCase__( self						:		Tuple		)->	List[Any]:
			lowerCAmelCase__       :      Optional[int]   					= VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''		, torch_dtype=torch.floataa		)
			pipe.to(_SCREAMING_SNAKE_CASE		)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= '''cyberpunk 2077'''
			lowerCAmelCase__       :      List[str]   					= load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
			lowerCAmelCase__       :      List[Any]   					= torch.manual_seed(0		)
			lowerCAmelCase__       :      Union[str, Any]   					= pipe.dual_guided(
			    prompt=_SCREAMING_SNAKE_CASE		, image=_SCREAMING_SNAKE_CASE		, text_to_image_strength=0.75		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=50		, output_type='''numpy'''		, ).images

			lowerCAmelCase__       :      List[Any]   					= image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase__       :      List[str]   					= np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

			lowerCAmelCase__       :      int   					= '''A painting of a squirrel eating a burger '''
			lowerCAmelCase__       :      Optional[Any]   					= torch.manual_seed(0		)
			lowerCAmelCase__       :      Union[str, Any]   					= pipe.text_to_image(
			    prompt=_SCREAMING_SNAKE_CASE		, generator=_SCREAMING_SNAKE_CASE		, guidance_scale=7.5		, num_inference_steps=50		, output_type='''numpy'''		).images

			lowerCAmelCase__       :      List[Any]   					= image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase__       :      Optional[Any]   					= np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

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

			lowerCAmelCase__       :      Optional[Any]   					= pipe.image_variation(_SCREAMING_SNAKE_CASE		, generator=_SCREAMING_SNAKE_CASE		, output_type='''numpy'''		).images

			lowerCAmelCase__       :      List[Any]   					= image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase__       :      Any   					= np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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




