
"""simple docstring"""







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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     float:







							"""simple docstring"""


							__snake_case     			=       np.array([[1, item, train_mtch[i]] for i, item in enumerate(SCREAMING_SNAKE_CASE )] )
							__snake_case     			=       np.array(SCREAMING_SNAKE_CASE )
							__snake_case     			=       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()							,    SCREAMING_SNAKE_CASE ) )							,    x.transpose() )							,    SCREAMING_SNAKE_CASE )
							return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     float:







							"""simple docstring"""


							__snake_case     			=       (1, 2, 1)
							__snake_case     			=       (1, 1, 0, 7)
							__snake_case     			=       SARIMAX(
							    SCREAMING_SNAKE_CASE							,    exog=SCREAMING_SNAKE_CASE							,    order=SCREAMING_SNAKE_CASE							,    seasonal_order=SCREAMING_SNAKE_CASE )
							__snake_case     			=       model.fit(disp=SCREAMING_SNAKE_CASE							,    maxiter=6_00							,    method="nm" )
							__snake_case     			=       model_fit.predict(1							,    len(SCREAMING_SNAKE_CASE )							,    exog=[test_match] )
							return result[0]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     float:







							"""simple docstring"""


							__snake_case     			=       SVR(kernel="rbf"							,    C=1							,    gamma=0.1							,    epsilon=0.1 )
							regressor.fit(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							__snake_case     			=       regressor.predict(SCREAMING_SNAKE_CASE )
							return y_pred[0]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     float:







							"""simple docstring"""


							train_user.sort()
							__snake_case     			=       np.percentile(SCREAMING_SNAKE_CASE							,    25 )
							__snake_case     			=       np.percentile(SCREAMING_SNAKE_CASE							,    75 )
							__snake_case     			=       qa - qa
							__snake_case     			=       qa - (iqr * 0.1)
							return low_lim
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     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(SCREAMING_SNAKE_CASE ) - abs(SCREAMING_SNAKE_CASE ) ) <= 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)
		_SCREAMING_SNAKE_CASE	       =      [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
		_SCREAMING_SNAKE_CASE	       =      pd.DataFrame(
		    data_input, columns=["""total_user""", """total_even""", """days"""]
		)
		_SCREAMING_SNAKE_CASE	       =      Normalizer().fit_transform(data_input_df.values)
		# split data
		_SCREAMING_SNAKE_CASE	       =      normalize_df[:, 2].tolist()
		_SCREAMING_SNAKE_CASE	       =      normalize_df[:, 0].tolist()
		_SCREAMING_SNAKE_CASE	       =      normalize_df[:, 1].tolist()

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

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

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

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


"""simple docstring"""







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort







@nightly
@require_onnxruntime
@require_torch_gpu
class 							__magic_name__     (		unittest.TestCase   ):





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       ort.SessionOptions()
												__snake_case     			=       False
												return options





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
												__snake_case     			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
												__snake_case     			=       load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"    )

												# using the PNDM scheduler by default
												__snake_case     			=       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"							,						revision="onnx"							,						safety_checker=snake_case_							,						feature_extractor=snake_case_							,						provider=self.gpu_provider							,						sess_options=self.gpu_options							,						)
												pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       "A red cat sitting on a park bench"

												__snake_case     			=       np.random.RandomState(0    )
												__snake_case     			=       pipe(
												    prompt=snake_case_							,						image=snake_case_							,						mask_image=snake_case_							,						strength=0.75							,						guidance_scale=7.5							,						num_inference_steps=15							,						generator=snake_case_							,						output_type="np"							,						)

												__snake_case     			=       output.images[0]

												assert image.shape == (512, 512, 3)
												assert np.abs(expected_image - image    ).max() < 1e-2
